GENETIC DISTANCE BETWEEN SUNNI AND SHIA MUSLIMS OF PUNJAB

Dr. HARJOT PAL KAUR

Associate Professor, SUS College of Research and Technology, Tangori, Mohali (Punjab) India

INDEXING

CHAPTER-I: Introduction 1 Genetic variation in man 1 The concept of polymorphism 1 Protein and enzyme polymorphisms 1 Human population genetics studies 2 Genetic markers studied 2 The present study 8 The present biochemical genetic study among the Muslims of Punjab 8 aims CHAPTER-II: Area And People 9 PUNJAB 9 Malerkotla 9 Muslims of india 10 Muslims of punjab 11 CHAPTER-III: Material And Methods Field Collections 12 Laboratory Analysis 12 Acid Phosphatase Locus 1 (ACP1) 13 Phosphoglucomutase Locus 1 (PGM1) 14 Adenylate Kinase Locus 1 (AK1) 16 Adenosine Deaminase (ADA) 17 Phosphohexose Isomerase (PHI) 18 Esterase D (ESD) 20 Statistical Analysis 21 Allele frequency calculations 21 The Chi-square test for goodness of fit 22 The contingency Chi-square test 22 Inbreeding Coefficient (F) 24 Measures of Genic Differentiation 24 Gene diversity analysis 24 Wahlund’s variance 25 NEI’S Standard Genetic Distance 25 CHAPTER-IV: Results And Discussion 27 Genetic Markers In The Muslims of Punjab 28 Acid Phosphatase Locus 1 (ACP1) Variation in the Muslims of Punjab 28 Phosphoglucomutase Locus 1 (PGM1) variation in the Muslims of 28 Punjab Adenylate Kinase Locus 1 (AK1) Variation in the Muslims of Punjab 29 Adenosine Deaminase (ADA) Variation in the Muslims of Punjab 29 Phosphohexose Isomerase (PHI) Variation in the Muslims of Punjab 30 Esterase D (ESD) Variation in the Muslims of Punjab 31 INDEXING

Inbreeding coefficient in the muslims of punjab 31 Genic differentiation in the muslims of punjab 31 Gene diversity analysis 31 Wahlund’s Variance 32 Genic Differentiation In The Muslims Of India 32 Gene diversity analysis 32 Wahlund’s variance analysis 32 Nei’s standard genetic distances in people of punjab 32 Nei’s standard genetic distances among muslim Populations of india 33 CHAPTER-V: CONCLUSIONS 43 SUMMARY 44 REFERENCES 45

GENETIC DISTANCE BETWEEN SUNNI AND SHIA MUSLIMS OF PUNJAB

HARJOT PAL KAUR

ABSTRACT

This study was planned to provide baseline data and to assess similarities and differences between Sunni and Shia Muslims of Punjab, with other caste populations of state and various other Muslim populations of India. Six red cell enzyme markers viz., Acid Phosphatase locus 1 (ACP1), Phosphoglucomutase locus 1 (PGM1), Adenylate Kinase locus 1 (AK1), Adenosine Deaminase (ADA), Phosphohexose Isomerase (PHI) and Estrase D (ESD) were investigated following original standard starch gel electrophoresis techniques for the genetic characterization of Muslims of Punjab. Both the Sunni and Shia Muslims of Punjab were in Hardy-Weinberg (genetic) equilibrium for all the studied enzymes except for the ACP1 system in the Sunni sect where the heterozygotes were in very low proportion. Barring the ESD system, the allele frequencies of other five systems were found to be almost similar in both the sects of Muslims of Punjab and fit in the ranges reported earlier for other populations of Punjab. When compared with Muslims of other states of India, the frequency of ACPI*A and AKI*2 allele in present Sunni sample was found highest and ADA*2 allele in present Shia sample lowest so far reported in Indian Muslim populations. Intergroup χ2 comparison revealed that Muslims of Punjab were genetically close to some Muslim populations of other states of India. Mean value of inbreeding co-efficient was more in the Sunni Muslims as compared to the Shia Muslims of Punjab. Genetic differentiation when calculated with Nei’s co-efficient of differentiation (GST) and Wahlund’s varience (f) was found much lower than the other Muslim populations of India. Dendrogram analysis based on genetic distance revealed strong genetic affinities between the Sunni and Shia Muslims studied here. Both the endogamous sects were found closer to Jat Sikhs and distant from other caste populations of Punjab based on genetic distance. Muslims of Punjab also showed comparatively low genetic distance with other Muslim populations of India. This biochemical genetic investigation revealed that genetic constitution of the Sunni and Shia Muslims of Punjab was very similar and they also showed genetic affinities with other caste populations of Punjab and several Muslim populations of India.

CHAPTER I

INTRODUCTION

Genetic Variation In Man

Human genetic variation is the genetic differences both within and among populations. There may be multiple variants of any given gene in the human population (genes), leading to polymorphism. Many genes are not polymorphic, means that only a single allele is present in the population: the gene is then said to be fixed. The ABO blood groups, discovered at the beginning of nineteenth century (Landsteiner, 1901), provided the first example of normal genetic variation in man. It was later found out that these serologically detectable groups were in fact due to small chemical differences between molecules found at the surface of red blood cells of individuals of different blood groups. Antigenic variations on red cell surface were further enquired into by different workers and by 1940’s at least three more well known blood group systems viz., the MN, P and Rh were discovered (Race & Sanger, 1975).

On the other hand, genetic variation at the molecular level began to be searched about half a century later when Pauling et al. (1949) by using the electrophoresis technique of protein analysis showed heterogeneity at the haemoglobin locus and Smithies (1955) using starch gel electrophoresis did so at the haptoglobin locus. Variation in human red cell enzymes was however detected even later and first reports came only in the beginning of 1960’s (Fildes & Parr, 1963; Hopkinson et al., 1963), nearly six decades after the discovery of the ABO blood groups.

The Concept of Polymorphism

Mutation results in a new gene that is chemically only slightly different from the old one; the two types are called alleles of that gene. When the presence of two or more alleles of a gene in a population is detected, the gene is said to be polymorphic. Polymorphism is the occurrence together in the same locality of two or more discontinuous forms of a species in such proportions that rarest of them cannot be maintained merely by recurrent mutation (Ford, 1945). Similarly, genetic polymorphism refers to “the occurrence together in the same population of two or more alleles at one locus, each with appreciable frequency (Cavalli-Sforza & Bodmer, 1971). That is, a locus is called polymorphic if the frequency of the most common allele is less than or equal to 0.99” (Nei, 1987).

Genetic polymorphisms provide us with pointers to the variation of specific chromosome segments. In this sense the polymorphisms are genetic markers and thus our door to the understanding and measurement of genetic variation. They can be studied from any cell or biological fluid, but blood is by far the favourite material because it is most easily obtained and gives the greatest opportunities for detecting them.

Protein and Enzyme Polymorphism

Proteins, including enzymes, are composed of amino acids joined by covalent peptide bonds to form polypeptides. These sequences, or “primary structures”, are genetically determined. Each of the 20 amino acids has a unique side chain, characterized by its shape, size and charge. The side chains on lysine, arginine and + histidine are positively charged (NH3 ) and thus basic; those on aspartic acid and glutamic acid are negatively charged (COO–) and thus acidic.

Charged side chains are responsible for the movement of the proteins through a (gel) matrix during electrophoresis. In an electrical field, the anions (negatively charged molecules) migrate towards the anode (positive electrode) and the cations (positively charged molecules) migrate towards the cathode (negative electrode). The speed of migration is related to net charge on the enzyme molecule and the electrical field strength applied through the electrodes. The former is governed by the pH of the buffer in which the electrophoretic separation is carried out and the latter is controlled by the voltage setting on the power supply. Thus proteins (including enzymes), the main products of genes, move in an electric field with mobility that depends mainly on their surface electric charge, which in turn depends on their chemical structure. The amino acid sequences of proteins are changed by mutations in the encoding DNA locus. Such mutations may alter shape and net charge. Electrophoresis aims to reveal as many of these changes as possible.

In man, proteins present in the liquid (serum) portion of blood as well as enzymes present within red blood cells (erythrocytes) have been studied. The first example of electrophoretically detectable human protein was that of

1 the well known blood protein haemoglobin (Pauling et al., 1949). Serum protein haptoglobin was found to be polymorphic soon thereafter (Smithies, 1955).

Enzymes are proteins active as specific catalysts in biochemical reactions and usually present in small concentrations in the blood. When it was learned how to uncover them through very sensitive and specific staining reactions (Hunter and Markert, 1957), enzymes provided the first statistical evidence that polymorphisms are much more common than earlier believed. In fact, works of Harris (1966, 1969) demonstrated widespread existence of enzyme polymorphisms as a “normal” condition in humans.

The different molecular forms of an enzyme which are functionally similar (catalyse the same biochemical reaction), but differed in their electrophoretic properties, were referred to as “isozyme” (isoenzyme) by Markert and Moller (1959). Starch gel electrophoresis has been widely used as technique to search for enzyme polymorphisms, because this is known to be capable of detecting quite subtle differences in molecular structure (isozymes), and yet is very suitable for examining large numbers of individuals.

Electrophoretic analysis has since been further developed and has helped detect a great deal of variation in both proteins and enzymes. It is now known that the majority of the tens of thousands of different proteins, including enzymes, found in an organism exist in more than one form, so that some individuals may have one form of the protein/ enzyme, whereas others may have another form.

In recent years a great deal of work has been carried out which basically aimed at assessing the extent of genetically determined variation, including enzyme variation, in human populations (Mourant et al., 1976; Tills et al., 1983; Roychoudhury & Nei, 1988). The general approach has been to screen large numbers of apparently normal and healthy individuals for phenotypes of certain polymorphic enzymes. Enzymes present in red cells have been mainly chosen for such investigations because many of them have been studied using standard electrophoretic techniques and their phenotypes and inheritance patterns are well established. Therefore, red cell enzyme markers are particularly convenient for population studies such as the present. Individuals that have received the same alleles from both parents are called homozygotes, whereas individuals that may have received different alleles of a polymorphic gene from their parents are called heterozygotes. If we know that there exist different genetic types of a specific protein or other strictly inherited characters, we can count individuals carrying one type or the other and establish the proportion of that type in the population being examined. These proportions vary from one population to another because they change over time in each population in a relatively unpredictable manner (Cavalli-Sforza et al., 1994).

One way of studying living populations is a geographic representation of the data. For this purpose we first consider each gene (a segment of DNA endowed with a specific function) by itself, and for each gene we separately analyse the different forms that we can recognize, the alleles of that gene. The proportion of a given allele in different populations is the raw material of this approach. It is well established that the proportion of an allele varies considerably from place to place, but usually there is little difference between neighbouring populations so that the greatest variation is observed at large distances. It is thus possible to prepare geographic maps representing these proportions for a particular allele (also called allele frequencies or simply, gene frequencies) when a sufficient number of populations have been tested (Cavalli-Sforza et al., 1994).

Human Population Genetics Studies

Population geneticists have developed statistical methods for interpreting genetic population structure. The most relevant of these to genetic data, including isozyme data, is the Hardy-Weinberg equilibrium law, “the root from which modern population genetics grew” (Buettner-Janusch, 1969). The law states that in the absence of selection, drift and migration, the genotype frequencies in a randomly mating population will maintain a stable equilibrium.

If the distribution of genotypes remains constant from generation to generation in a population, it is called an equilibrium (panmictic) or random mating population. On the other hand, non-conformity to the prediction of Hardy-Weinberg equilibrium indicates that the phenotypic variation has a non-genetic base or that one or more of the Hardy-Weinberg assumptions are not met in the population. Thus, for instance, the individuals in that population may not be randomly mating, or some natural selective force may be acting on the population, or genes from neighbouring populations may be migrating into that population.

Protein and enzyme polymorphism data in human populations have been used to characterize them in terms of allele frequencies. Such data are also useful for studying the genetic relationships of different human populations in particular and evolution in general. However, to conduct a quantitative study of the evolutionary change of genes or populations, statistical methods are needed. There are several such models for studying various evolutionary and population genetics problems (Cavalli-Sforza & Bodmer, 1971; Nei, 1987).

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In the early days of genetic studies, the genetic relationships among different human populations were studied by examining the geographical distribution of allele frequencies at a few polymorphic loci such as the ABO and Rh blood group systems. It was later realized that comparison of allele frequencies for one or two loci is not reliable, since each locus has a different distribution. Thus, only when a large number of loci are examined do the genetic relationships become clear (Cavalli-Sforza & Edwards, 1964). This is partly because the inter- populational genetic variation is very small compared with the intra-populational variation at the gene level (Nei & Roychoudhury, 1972; Lewontin, 1972). However, if a large number of loci are examined, even small differences can be detected with sufficient accuracy (Roychoudhury & Nei, 1988).

Nowadays, the genetic differences between a pair of populations are usually measured as a quantity called the genetic distance, which is a function of allele frequencies. Once genetic distances are estimated for a group of populations, their genetic relationships can be studied by using dendrograms or principal component analysis (Cavalli-Sforza & Bodmer, 1971; Sneath & Sokal, 1973).

One of the purposes of population genetics is to study the mechanism of maintenance of genetic variability. Noting that the frequency of the thalassaemia gene is high in the Mediterranean area, Haldane (1949) suggested that this deleterious gene might be maintained by heterozygote advantage. This brilliant suggestion led Allison (1955) to the discovery that the high frequency of the sickle cell anaemia gene in central Africa is the result of heterozygote advantage, which is caused by higher resistance of sickle-cell heterozygotes to malaria. Similar attempts to seek an association between allele frequencies and environmental factors have been made for several other polymorphic loci (Goedde, 1986; Flint et al., 1986; Flatz, 1987). Apparently, such studies are an effective way to make an inference about selective mechanisms, albeit such inference has to be proved later by more direct means. Clearly, data on the distribution of allele frequencies of various polymorphic loci of the blood groups, serum protein and red cell enzyme proteins among different populations inhabiting different geographical and environmental areas of the world, such as compiled in Mourant et al. (1976), will be a prerequisite to attempt any association study.

Allele frequency data are also useful for tracing past gene migration. For instance, the gene pool of the American Negroid population is known to contain Caucasian genes because of past interracial mating. The percentage of Caucasian genes has been estimated to be about 20% from allele frequency data for blood group loci (Reed, 1969). Similarly, allele frequency data also suggest that gene migration has occurred for a long time period between south Europeans and North Africans (Roychoudhury & Nei, 1988). Genetic Markers Studied

All the marker systems considered in this work were red cell enzyme polymorphisms which are qualitative traits i.e., there are discrete phenotypic categories (usually three) in each of them in which individuals can be placed without any ambiguity. The markers were Acid Phosphatase locus 1 (ACP1), Phosphoglucomutase locus 1 (PGM1), Adenylate Kinase locus 1 (AK1), Adenosine Deaminase (ADA), Phosphohexose Isomerase (PHI) and Esterase D (ESD). Latest standard notations (Shows et al., 1987) have been used to designate phenotypes and alleles, albeit older ones are also frequently retained in this chapter for clarity and to avoid confusion.

The class and enzyme commission (E.C.) number alongwith the chromosomal location of each enzyme system included in this investigation are detailed in Table 1. This table shows that, of the six systems considered, one (AK1) belonged to the transferases class, two (PHI, PGM1) to the isomerases class and three (ACPI, ADA, ESD) to the hydrolases class. Loci for these systems are located on different chromosomes i.e., ACP1 on 2, PGM1 on 1, AK1 on 9, ADA on 20, PHI on 19 and ESD on 13 (Table 2). Thus, apparently there is no linkage between the studied enzyme loci. Besides common alleles, a number of rare alleles, including null (silent) allele, have been encountered in various population surveys at these loci and these are listed in Table 3. The phenotypes of the studied systems are depicted in Figs. la - f.

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Table 1 Red cell enzyme systems employed in the present biochemical investigation

Enzyme Commission Common Common Discoverer of Enzyme Abbreviation Class (E.C.) number phenotypes alleles polymorphism Acid Phosphatase ACPI Hydrolases 3.1.3.2 ACPI A, A,B, B, ACP1*A Hopkinson et al. (1963) locus 1 B,C, A,C, C ACP1*B ACP1*C Phosphoglucomutase PGM1 Isomerases 5.4.2.2 PGM1 1, 1,2, 2 PGM1*1 Spencer et al. (1964) locus 1 (Formerly 2.7.5.1) PGM1*2 Adenylate Kinase AK1 Transferases 2.7.4.3 AK1 1, 1,2, 2 AK1*1 Fildes & Harris (1966) locus 1 AK1*2 Adenosine ADA Hydrolases 3.5.4.4 ADA 1, 1,2, 2 ADA*1 Spencer et al. (1968) Deaminase ADA*2 Phosphohexose PHI Isomerases 5.3.1.9 PHI 1 PHI*1 Detter et al. (1968) Isomerase Esterase D ESD Hydrolases 3.1.1.1 ESD 1, 1,2, 2 ESD*1 Hopkinson et al. (1973) ESD*2

Table 2 Chromosomal localization of the red cell enzyme system loci investigated

Enzyme locus Chromosome location Reference ACPI Short arm of chromosome 2 Ferguson-Smith et al. (1973) (2p25) PGM1 Short arm of chromosome 1 Douglas et al. (1973) (1p22.1) AK1 Long arm of chromosome 9 Povey et al. (1976) (9q34.1–34.2) ADA Long arm of chromosome 20 Tischfield et al. (1974) (20q13.11) PHI Chromosome 19 McMorris et al. (1973) (19cen-q13.2) ESD Long arm of chromosome 13 Heyningen et al. (1975) (13q14.1–14.2)

Table 3 Rare alleles of the studied six red cell enzyme systems reported in world populations.

Enzyme Rare allele Population/ Area Reference system ACPI ACP1*D Male patients, Texas Karp & Sutton (1967) Danish Lamm (1970) Polish Raszeja & Szezerkowa (1978) ACP1*E Danish Sorensen (1975) ACP1*F German Radam et al. (1982) American Hispanic Nelson et al. (1984) ACP1*G German Radam et al. (1982) American Black Nelson et al. (1984) ACP1*GUA-1 Guaymi (Panama) Tanix et al. (1977) Guaymi Indian (Panama, Costa Rica) Mohrenweiser & Novotony (1982) ACP1*H German Martin et al. (1982) ACP1*I German Martin et al. (1982) ACP1*K Polish Turowska (1984) ACP1*KUK Czechoslovakian Arnaud et al. (1989)

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ACP1*N American Black Miller et al. (1987) ACP1*P American White Miller et al. (1987) ACP1*R Thai Giblett & Scott (1965) Texas (U.S.A.) Karp & Sutton (1967) Bantu Kirk et al. (1971) Negro, Khoisan (S.Africa) Jenkins & Corfield (1972) Polish Byrdy (1973) Austrian Sorgo & Brinkmann (1974) ACP1*S American White Miller et al. (1987) ACP1*TIC-1 Ticuna Indian (South America) Yoshihara & Mohrenweiser (1980) ACP1*O German Herbich (1969) German Herbich et al. (1970) Austrian Herbich & Meinhart (1972) German Brinkmann et al. (1974) Danish Dissing & Svensmark (1976) Polish Turowska et al. (1977) Polish Turowska & Bogusz (1978) Polish Koziol & Bruszewska (1978) Czechoslovakian Nezbeda (1979) Polish Dobosz (1983) Polish Raczeck & Grazesik (1986) PGM1 PGM1*3 English Hopkinson & Harris (1966) PGM1*3 OKINAWA Ryukuan Blake & Omoto (1975) PGM1*4 English Hopkinson & Harris (1966) PGM1*5 English Hopkinson & Harris (1966) PGM1*5 JAPAN Japanese Blake & Omoto (1975) PGM1*6 Turkish Cypriot Hopkinson & Harris (1966) PGMI*6 AFRICAN African Hopkinson & Harris (1966) PGM1*6 JAPAN Japanese Blake & Omoto (1975) PGM1*6 MAL Malayarian (tribal population) Blake & Omoto (1975) PGM1*KADAR Kadar Saha et al. (1974) PGM1*7 English Hopkinson & Harris (1966) PGM1*8 Arab Hopkinson & Harris (1969) Jewish Tomashewsky & Szeinberg (1970) Assamese Goedde et al. (1972) PGM1*8 NARA Japanese Horai (1975) PGM1*O German Fielder & Pettenkofer (1968, 1969) German Brinkmann et al. (1972) Czechoslavakian Herzog & Libich (1982) American Caucasian Ward et al. (1985) PGM1*Ir German Brinkmann et al. (1972) Bertrams et al. (1986) Japanese Suzuki et al. (1988) AKI AK1*3 American White and Black Bowman et al. (1967) German Wendt et al. (1971) German Radam & Strauch (1968, 1971) Athabaskan (Alaska) Scott & Wright (1978) Wyampi (French Guiana) Seger et al. (1978) AK1*4 American Bowman et al. (1967) English Rapley et al. (1967) Greek Stamatoyannopouls et al. (1975) Santachiara-Benerecetti et al. AK1*5 Non-tribal (Andhra Pradesh) (1972) Yupik, Athabaskan (Alaska) Scott & Wright (1978) AK1*O English Singer & Brock (1971) ADA ADA*3 English Hopkinson et al. (1969) German Ritter et al. (1971) Jat (Punjab) Singh et al. (1974) ADA*4 Danish Dissing & Knudsen (1969) German Ritter et al. (1971) Austrian Wust (1971) Swiss Pflugshaupt et al. (1976) German Radam (1977) German Radam et al. (1977) German Manz et al. (1979) ADA*5 American Negro Detter et al. (1970) Macua Negro Renninger & Bimboese (1970) European Harris et al. (1974) Czechoslovakian Radam et al. (1975) Dayak (Malaysia) Ganesan et al. (1976) Portuguese Weissmann et al. (1980)

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ADA*6 German Radam et al. (1974, 1975) Arab Moslem (Israel) Nevo (1977) German Manz et al. (1979) Japenese Komatsu et al. (1987) ADA*7 German Berg et al. (1975) ADA*0 Giblett et al. (1972) Dissing & Knudsen (1972) Knudsen & Dissing (1973) German Brinkmann et al. (1973) American Chen et al. (1974) PHI PHI*2 Thai Detter et al. (1968) PHI*3 Asiatic Indian Detter et al. (1968) PHI*4 Thai Detter et al. (1968) PHI*5 European Detter et al. (1968) PHI*6 Negro Detter et al. (1968) PHI*7 Asiatic Indian Detter et al. (1968) PHI*8 Thai Detter et al. (1968) PHI*9 Asiatic Indian Detter et al. (1968) PHI*3 Israeli-Arab Bonne-Tamir et al. (1987) ISRAEL PHI*0 Baughan et al. (1968) ESD ESD*3 German Bender & Frank (1974) Italian Bargagna et al. (1975) German (Bonn) Rittner & Muller (1975) Papua New Guinea Blake (1976) Japanese Suzuki et al. (1978) Finnish Wetterling (1985) Italian Biondi et al. (1989) ESD*3 Negrito (Philippine) Omoto et al. (1989) NEGRITO ESD*3.1 Japanese Suzuki et al. (1978) ESD*4 German Berg et al. (1976) ESD*5 German Martin (1979) American White and Black Dykes et al. (1982) Italian (Rome) Destro-Bisol et al. (1986) Norwegian Olaisen et al. (1981) Italian (Rome) Ranalletta et al. (1987) Spaniard Moreno et al. (1989) ESD*6 German Radam et al. (1980) ESD*7 German Siege & Schwehn (1983) Japanese Nishigaki & Itoh (1984) ESD*11 German Henke et al. (1986) ESD*MAMELODI South African Negroid Hitzeroth et al. (1976) ESD*COPENHAGEN Danish Dissing & Eriksen (1984) ESD*DUSSELDORF German Henke & Basler (1984) ESD*DUSSELDORF-2 German Huckenbeck et al. (1988) ESD*YAMAGUCHI Japanese Yuasa et al. (1985) ESD*BERLIN German Weidinger et al. (1985) ESD*KOFU Japanese Komatsu et al. (1985) ESD*LISBON Portuguese Munier et al. (1988) ESD*0 Kwambi (South Africa) Marks et al. (1977) American Caucasian Sparkes et al. (1979) German Patschneider & Dirnhofer (1979) Polish Koziol & Stepien (1980)

Fig. 1a Diagrammatic representation of different electrophoretic patterns of ACP1

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Fig. 1b Diagrammatic representation of different electrophoretic patterns of PGM1

Fig. 1c Diagrammatic representation of different electrophoretic patterns of AK1

Fig. 1d Diagrammatic representation of different electrophoretic patterns of ADA

Fig. 1e Diagrammatic representation of different electrophoretic patterns of PHI

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Fig. 1f Diagrammatic representation of different electrophoretic patterns of ESD

The Present Study

The bulk of population of Punjab falls into two religious groups viz., the Hindus and Sikhs; two others, the Muslims and Christians, are in minority. For this obvious reason, most of the data pertaining to the distribution of serological and biochemical genetic markers from Punjab (Bhasin et al., 1992a) are almost exclusively limited to different caste populations of the majority groups of the Hindus and Sikhs. Barring a single quantitative study on G-6-PD deficiency among the Muslims of Malerkotla (Bansal et al., 1976), both the minority groups of Punjab have been totally neglected as for as biochemical genetic studies are concerned.

It is therefore that this genetic investigation, using data from as many as six enzyme marker systems, was planned among one of the minority group of the Punjab state viz., the Muslims, which form only about 1% of the state’s total population (Census of India, 1991). In fact, this is the first attempt to characterize the Muslims of Punjab using extensive qualitative electrophoretic data obtained from a good representative sample of the population.

The present biochemical genetic study among the Muslims of Punjab aims

 To provide genetic profile and baseline data for the minority Muslim population of Punjab, using phenotype and allele frequencies of six red cell enzyme marker systems viz., the polymorphisms of Acid Phosphatase locus 1 (ACP1), Phosphoglucomutase locus 1 (PGM1), Adenylate Kinase locus 1 (AK1), Adenosine Deaminase (ADA), Phosphohexose Isomerase (PHI), and Esterase D (ESD).  To estimate the degree of inbreeding coefficient (F) among the contemporary Muslim population of Punjab based on the genetic data of this study.  To estimate the degree of genic differentiation and to assess similarities and differences between two Muslim sects of Punjab as well as among all the Muslim populations studied from India, using different population genetics measures.

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CHAPTER – II

AREA AND PEOPLE

Punjab

Punjab is one of the smaller states of India, but its people and economy are among the most dynamic in the country. Initially the land of five rivers – the Satluj, Beas, Ravi, Chenab and Jhelum – Punjab extended over a vast area but after subsequent reorganisations it shrank in size. It was reduced to 50,362 sq. km in 1966 when it was finally reorganized on a linguistic basis.

Roughly triangular in shape (Fig. 2), the present state of Punjab extends from 29º30’ to 32º32’ North latitude and 73º55’ to 76º50’ East longitude. It is situated in North-Western part of India as a border state and far removed from the Arabian Sea and the Bay of Bengal. Punjab is an inland state – a fact which is loaded with strategic, political and socio-economic implications (Gosal, 1996).

Religion is one of the basic cultural characteristics of human populations and several religions have thrived in secular India. The information on various religious communities in the country has always been of vital interest to the administrators, planners, anthropologists, sociologists, demographers and the layman at large.

Punjab has a population of 20.3 million as per the 1991 Census, forming mere 2.4% of the total population of India. The state supports a population comprising all the major religions of the country namely the Hinduism, Islam, Sikhism and Christianity. However, while the Hindus and Sikhs, could be found throughout the length and breadth of Punjab, the minority communities of the Muslims and Christians are concentrated in certain pockets of the state. Malerkotla in the Sangrur district in South Punjab is one such pocket where a large chunk of the Muslim population of the state has been residing since generations.

In Punjab, the Sikhs form the predominant religious community and they, according to the 1991 Census, account for about two-thirds (62.95%) of the state’s total population; the Hindus are the second largest religious community (34.46%). Thus, together the Sikhs and Hindus in Punjab constitute the bulk of the total population of the state, leaving a little for the Muslims (1.18%) and the Christians (1.11%); the Buddhists and Jains are of course in negligible proportions in Punjab.

Before the independence of the country from the British in 1947, there was a very high percentage of the Muslims living in the Indian side of Punjab. But after independence and simultaneous partition, most of the Muslims migrated to the newly created Pakistan. Presently, the Muslims in Punjab are mainly concentrated in the Sangrur district where they account for as high as 7.15 percent of the total population of the district (Census of India, 1991). In fact, the Malerkotla town of the Sangrur district having a total population of 88,600 is dominated by the Muslim community, constituting two-thirds of its population.

Malerkotla

The Malerkotla state was founded by the great saint Sadar-ul-din Khan, a powerful and influential chief of Sherwani tribes of Afghans who came to India from Dara Ban in Afghanistan before the mid of the 15th Century, settled in Punjab and rebuilt the old town of Maler in 1442. The word Malerkotla is a combination of two words – ‘Maler’ and ‘Kotla’. ‘Maler’ originated from the name of a rural settlement i.e., village Malhargarh near Dera Atma Ram. The word ‘Kotla’ originated from ‘Kot’ which means a walled area. These two settlements, i.e. ‘Maler’ founded by Sadar-ul-din Khan in 1442 and ‘Kotla’ founded by Bayaxid Khan in 1656, were united by the construction of Moti Bazar in 1942. It is one of the oldest towns having historical background and the only formerly Muslim state among erstwhile princely states of Punjab.

Situated at 30º32’ North latitude and 75º59’ East longitude, Malerkotla (Fig. 2) as a princely state was ruled by Isa Mohammad Sahib, Khwaja and Fateh Mohammad khan as kings. The first king was Bayaxid Khan. The last ruler of this princely state was His Highness Nawab Ahmad Ali Khan under whose administration the state made alround development.

It was this Nawab who, in response to fervent appeal made by Sardar Patel to the rulers of princely states, signed the ‘Instrument of Accession’ in September, 1947 and after his death a month later his eldest son Sahibzada Iftikhar Ali Khan succeeded him. Later, Malerkotla came to be merged into erstwhile PEPSU (Patiala and East Punjab States Union).

Malerkotla has been one of the former Muslim princely states in India, but this particular state has a special importance in the eyes of chronicler, particularly Sikh chronicler. It may be pertinent to mention here a very 9 important historical fact that when Sahibzada Zorawar Singh and Sahibzada Fateh Singh, two younger sons of the tenth Sikh Guru Gobind Singh, were caught by Wazir Khan, the Muslim ruler at Sirhind, they were called upon to embrace Islam or face death. Brave sons of the brave Guru, as they were, they preferred death. So it was decided by the rulers to brick them alive. It was the then ruler of Malerkotla, Sher Mohammad Khan, who pleaded for mercey at Sirhind in 1701 and his brave pleadings for the tenth Guru’s sons became a household word. As a result, the city was blessed by the Guru, that is, it received the eternal blessings and prayers of the Guru. This blessisng of the Malerkotla state constitute an important historical fact and is also responsible for the ever cordial Sikh-Muslim relations. Malerkotla town has also the credit of being free from communal tensions or riots in the past as well as in the recent history of Punjab (Zafar, 1992).

Fig. 2 Map showing location of Malerkotla in Punjab, North–West India

Muslims of India

Islam came to India both peacefully through trade, mainly in the South-West coastal region and forcefully through conquest, mainly in North India. During the period 12th-17th century, it spread to different parts of the country. The spread of Islam in India was mainly through conversion of individuals and often sizeable segments of several castes. These conversions have been of two types. The rigid, hierarchical caste system prevalent among the Hindus tempted many in the lower castes to rise in social status through conversion to Islam which is theoretically a casteless religion. Conquest by and religious enthusiasm of Muslim rulers resulted in forcible conversions which most probably included many from the higher castes also. In short, the bulk of the Muslim population in India today are converts and hence are likely to exihibit the characteristics of the regional populations (Khan et al., 1985).

Islam is divided into two main sects, the Sunni and the Shia, mainly based on a dispute over the caliphate. The majority of the Muslim population in India as elsewhere (except in Iran) is Sunni. In fact, most of the Muslim rulers in India were Sunni. There is a belief among the Shia that some of the Sunni were also originally Shia who pretended to be Sunni because of fear of the Sunni rulers. 10

Muslims of Punjab

As in rest of India, the Muslims in Punjab comprise two main sects, namely, the Sunni and Shia - the former meaning “One of the path, a traditionalist”, the latter “A follower” i.e. follower of ‘Ali’, cousin german of the prophet and husband of his daughter Fatima. The Shia sect of Muslims in India owes its origin to a body of missionaries who were kindly received by the kings of Anhilwara in Gujarat in the eleventh century, while the Sunni sect was established by the influence of local Muslim kings. The Shias maintain that Ali was the first legitimate Imam and they reject first three caliphs recognised by the Sunnis. The Shias observe Muharram festival and add Bang or Azan to their prayers.

The difference between the Sunni and Shia Muslims is partly religious and partly social. The two sects are strictly different and there have been some riots among them during the festival days. In fact, the Sunni and Shia are two strictly endogamous Muslim populations and therefore they do not marry among each other. Four groups of the Muslims viz. the Syed, Seikh, Mughal and Pathan are found in either sect. Although the groups of one sect can marry each other they cannot do so with groups of the other sect.

In Punjab, Sunni is the predominant Muslim sect; the proportion of Shia sect is comparatively very low. Barring a couple of Shia localities, almost entire Malerkotla town is inhabited by the Sunnis (Bal, 1973). The town is backward as far as educational level is concerned and the percentage of literacy among females (38.37) is much lower than among males (61.63). Due to influence of Punjabi, the language spoken in the town is mainly Punjabi and Urdu, although Hindi is also used by a section of the population (Zafar, 1992).

11

CHAPTER – III

MATERIAL AND METHODS

Field Collections

For this biochemical population genetics work, blood samples from a total of 992 apparently healthy not very closely related Muslim subjects of the two endogamous sects of this religious community viz,. the Sunni (n=803) and Shia (n=189), inhabiting Malerkotla town of the Sangrur district of Punjab (North-West India) were collected. From each subject about 1 ml finger-prick blood was obtained in a potassium EDTA plastic vial, using a sterile disposable blood lancet.

The first batch of samples, mostly of the Sunni Muslims, was collected in December 1997 from Government College, Malerkotla, Islamia Senior Secondary School, Malerkotla, Islamia Girls High School Malerkotla and Khojia Mohalla of the Malerkotla town. The second batch, entirely of the Shia Muslims, was collected in September 1998 from the City School Azimpura and Azimpura Mohalla of Malerkotla. Blood samples collected in a day were transported to Patiala in an ice box on the same day where laboratory analysis began the following day.

LABORATORY ANALYSIS

In the laboratory at Patiala, the samples stored overnight in a refrigerator were processed for the preparation of haemolysates as follows. The whole blood samples were first transferred from collection vials into serology glass tubes (75 mm × 10 mm) and centrifuged at 3,000 r.p.m. for 5 minutes. The plasma was removed and remainder of the sample was washed thrice with 0.9% NaCl (isotonic) saline. Haemolysates were prepared by freezing and thawing technique by mixing an equal volume of distilled water to washed packed red cells and keeping the mixture overnight at –20ºC in a deep freezer. All haemolysates were properly labelled and stored in plastic boxes in freezer pending their electrophoretic analyses, which were accomplished system wise within few weeks of each collection.

For the detection of phenotypes of various red cell enzymes included in this study, the biochemical technique of electrophoresis was used. The technique involves a supporting medium such as starch gel, agarose gel, acrylamide gel or cellulose acetate strip and performs the separation of a specific protein or enzyme in the mixture (i.e. serum or haemolysate) in a specific buffer system under the influence of an electric field, whereby different serum fractions or isozymes are separated, depending on their net ionic charge and molecular weight.

After electrophoresis, the phenotypes were visualized using specific biochemical/ histochemical staining procedures. Most of the stains provide a specific substrate for the enzyme, allow it to catalyze the particular reaction involved, and then develop a dye that can be visualized in normal light or by fluorescence under UV light.

Starch was used as supporting medium in the present study. The isozyme separations obtained in a supporting medium of starch gel depend not only on net ionic charge but also on differences in molecular size. This is because the fine matrix of the starch gel imposes a molecular sieving effect.

For the preparation of starch gels, 12% (w/v) Connaught hydrolysed starch in a suitable gel buffer was heated in a toughened glass conical flask with continuous swirling. The mixture first became viscous and quite rapidly much less viscous (thin) and somewhat transparent. As boiling began, heating was stopped and cooked mixture was degased using a vacuum pump. The mixture resumed boiling. Swirling of the flask may be required during the first few seconds to avoid aspiration of the gel out of the flask. The mixture was rapidly poured into a 25 × 15 × 0.6 cm gel frame to fill evenly. The gel was allowed to set at room temperature for 45-60 minutes and then transferred to a refrigerator for cooling for another 60 minutes, after which it was ready for use.

Red cell haemolysates were loaded in gels using either Whatman No. 3 (thin) or Whatman No. 17 (thick) filter paper inserts (8 mm × 6 mm pieces of filter paper), depending upon the system. Up to 12 samples were accomodated in a gel. These inserts were left in the gel for the duration of the electrophoretic run and removed before staining.

All typings of the six red cell enzyme systems included in this study were done by horizontal starch gel electrophoresis system consisting of electrophoresis tanks with platinum electrodes, gel casting trays with an inside depth of 0.6 cm, a constant voltage-regulated power supply, tray with wire-cutting device and covered plastic boxes for staining. A very simple and inexpensive horizontal system can be constructed cheaply using assorted plastic food-storage boxes as electrophoresis tanks as well as staining boxes; small platinum wire

12 pieces can be used for the electrodes, and these held with crocodile clips are connected to gel via tank buffer with four-fold Whatman No. 1 filter paper wicks.

Standard electrophoretic techniques and staining procedures were followed throughout in this biochemical investigation. Thus, isozymes of Acid Phosphatase locus 1 (ACP1) were typed following Hopkinson et al. (1963), Phosphoglucomutase locus 1 (PGM1) following Spencer et al. (1964), Adenylate Kinase locus 1 (AK1) following Fildes and Harris (1966), Adenosine Deaminase (ADA) following Spencer et al. (1968), Phosphohexose Isomerase (PHI) following Detter et al. (1968) and Esterase D (ESD) following Hopkinson et al. (1973), with minor modifications.

The details of the various electrophoretic methods used and sequences of the biochemical (histochemical) reactions involved in staining of each of the six enzyme systems are given below separately.

Acid Phosphatase Locus 1 (ACP1)

Tank Buffer (0.41 M Citrate, pH 6.0)

86.16 grams Citric acid dissolved in about 750 ml distilled water, pH adjusted to 6.0 with strong (10 N) NaOH solution and final volume made up to 1 litre with distilled water.

Gel Buffer (0.0046 M Tris – 0.0025 M Succinate, pH 6.0).

0.307 grams Succinic acid and 0.557 grams Tris, dissolved in 1 litre distilled water.

Overlay Buffer (0.05 M Citrate, pH 6.0)

1.05 grams Citric acid dissolved in about 75 ml distilled water, pH adjusted to 6.0 with NaOH solution and final volume made up to 100 ml with distilled water.

Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 ml gel buffer.

93 mg EDTA (disodium salt) was added before cooking and 1 ml 2-mercaptoethanol was added before degassing.

Sample Application

Red cell haemolysate samples were loaded on thick (Whatman No. 17) filter paper pieces inserted in a line into gel, about 6 cm from cathode.

Electrophoresis

Gel was run at 110 volts constant in a refrigerator overnight for 17 hours (haemoglobin moved about 5 cm towards cathode and ACP1 isozymes moved towards anode).

After electrophoresis, the cathodal portion was discarded and filter paper inserts were removed. About 10 cm anodal portion of the gel was sliced horizontally into two flat slabs about 0.3 cm thick using a thread and the lower (bottom) slab was stained as follows.

Staining Method

10 mg 4-Methylumbelliferyl phosphate dissolved in 25 ml overlay buffer.

The bottom slab of gel was placed in a plastic staining box and the cut surface was carefully covered with a piece of thin filter paper (Whatman No. 3) which has been soaked in the substrate solution. The box was covered and the stained gel was incubated at 37ºC for 15–30 minutes.

Fluorescent zones of ACP1 isozyme activity were clearly visible under longwave (365 nm) ultra violet (U.V.) light, after removing the filter paper. The isozyme patterns were visible instantly and were recorded immediately (Fig. 3a). Zones which were very weak after prolonged incubation were seen more clearly if the gel surface was made alkaline with 30% ammonia solution to increase the level of fluorescence.

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Reaction Mechanism

4-Methylumbelliferyl phosphate (substrate) in the staining mixture is hydrolysed to free 4-methylumbelliferone and inorganic phosphate by ACP1 enzyme activity. 4-Methylumbelliferone is relatively insoluble and remains fairly well localized to the sites of enzymic activity. But unlike the substrate, it is fluorescent. So after a suitable period of incubation and visualization under U.V. light, the ACP1 isozyme activity is seen as fluorescent bands (Fig. 3a). Phosphoglucomutase Locus 1 (PGM1)

The buffer system used for PGM1 electrophoresis was TEMM buffer which essentially is a tris-maleate buffer at pH 7.4, containing ethylenediaminetetraacetic acid (EDTA) and magnesium chloride. The two latter reagents are known activators of PGM1 and their incorporation into the gel and tank buffers considerably enhances the PGM1 isozyme staining intensities.

2.03 grams (0.01 M) Magnesium chloride dissolved in about 750 ml distilled water, pH adjusted to 7.4 with NaOH solution and final volume made up to 1 litre with distilled water.

Gel Buffer

1:10 dilution of tank buffer.

Overlay Buffer (0.03 M Tris, pH 8.0)

363 mg Tris dissolved in about 75 ml distilled water, pH adjusted to 8.0 with dilute HCl and final volume made up to 100 ml with distilled water.

Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 ml gel buffer.

Sample Application

Whatman No. 17 filter paper inserts were used for loading haemolysates into gel, placed in a line about 5 cm from cathode.

Electrophoresis

Gel was run at 140 volts in a refrigerator overnight for 17 hours (haemoglobin moved little from origin and the PGM1 isozymes moved towards anode.)

After electrophoresis, the cathodal portion was discarded and filter paper inserts were removed. About 10 cm anodal portion of the gel was sliced horizontally into two halves using a thread and the lower slab was stained as follows.

Staining Method

For detection of PGM1 isozymes after their electrophoretic separation, an enzyme system, which leads to the formation of a coloured precipitate, was used to detect the glucose-6-phosphate formed from glucose-1- phosphate by the PGM1. This system consists of the enzyme glucose-6-phosphate dehydrogenase (G6PD), its coenzyme NADP, a dye MTT, and PMS (phenazine methosulfate) which acts as a catalyst in the colour development.

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45 mg Glucose-1-phosphate (containing atleast 1% glucose 1,6 diphosphate), 2.5 mg MTT, 2.5 mg NADP, 50 mg Magnesium chloride, 2.5 mg PMS (added just before mixing with agar solution) and 1 I.U. G6PD (added just before mixing with agar solution) dissolved in 12.5 ml overlay buffer.

The stain was applied in an agar overlay. This was achieved by adding 12.5 ml of a 1.6% agar solution (made by boiling in overlay buffer), cooled to 55ºC, to 12.5 ml of the above solution of stains, mixing gently and pouring the mixture evenly on the cut surface of the gel, and allowing the agar to set.

Any exposure to direct sunlight was avoided during staining. Stained gel was placed in a covered box and incubated at 37ºC in darkness for 1–2 hours. PGM1 isozyme activity was clearly seen as purple bands and phenotypes were recorded (Fig. 3b).

Reaction Mechanism

PGM1 catalyses the conversion of glucose-1-phosphate (substrate) to glucose-6-phosphate, in the presence of catalytic amounts of glucose 1, 6 diphosphate. In the presence of G6PD (glucose-6-phosphate dehydrogenase) and coenzyme NADP, the glucose 6-phosphate formed by the action of phosphoglucomutase is oxidized to 6- phosphogluconate and during this reaction the coenzyme NADP is reduced to NADPH. The latter in the presence of PMS (phenazine methosulfate) then reduces the yellow soluble dye MTT to a dark purple insoluble formazan which is thus deposited at the sites of phosphoglucomutase activity. Hence PGM1 isozymes are seen as purple bands against a yellow background (Fig. 3b).

Fig. 3a Starch gel electrophoresis patterns of ACP1

Fig. 3b Starch gel electrophoresis patterns of PGM1

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Adenylate Kinase Locus 1 (AK1)

Tank Buffer (0.41 M Citrate, pH 7.0)

86.16 grams Citric acid dissolved in about 750 ml distilled water, pH adjusted to 7.0 with strong NaOH solution and final volume made up to 1 litre with distilled water.

Gel Buffer (0.005 M Histidine, pH 7.0)

1.048 grams L-Histidine monohydrochloride dissolved in about 750 ml distilled water, pH adjusted to 7.0 with NaOH solution and final volume made up to 1 litre with distilled water.

Overlay Buffer (0.1 M Tris, pH 8.0)

1.21 grams Tris dissolved in about 75 ml distilled water, pH adjusted to 8.0 with dilute HC1 and final volume made up to 100 ml with distilled water.

Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 ml gel buffer.

Sample Application

Haemolysates were loaded on thin (Whatman No. 3) filter paper pieces and bloted before inserting into gel in a line, about 8 cm from cathode.

Electrophoresis

Gel was run at 175 volts in a refrigerator for 4 hours (haemoglobin moved about 4 cm towards cathode from origin and AK1 isozymes also move towards cathode, little less than haemoglobin).

After electrophoresis, paper inserts were removed and both anodal and cathodal portions (about 5 cm each) of the gel were sliced horizontally into two halves using a thread cutter and both lower (bottom) halves (slabs) were stained as follows.

Staining method

45 mg Glucose, 11 mg ADP (disodium salt), 7 mg NADP, 3 mg MTT, 100 mg MgCl2,

3 mg PMS (added just before mixing with agar solution), 1 I.U. G6PD (added just before mixing with agar solution) and 2 I.U. Hexokinase (added just before mixing with agar solution) dissolved in 12.5 ml overlay buffer.

In an another beaker 200 mg (1.6%) agar was dissolved by boiling in 12.5 ml overlay buffer and cooled down to about 55ºC.

The contents of the two staining beakers were mixed gently and immediately evenly poured over the cut surface of the sliced gel and allowed to set for few minutes, avoiding exposure to direct sunlight.

Stained gel was placed in a covered plastic box and incubated at 37ºC in darkness for 30–45 minutes. AK1 isozyme activity was seen clearly as purple bands and phenotypes were recorded (Fig. 3c). . Reaction Mechanism

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ADP added as substrate in the stain mixture is converted to ATP by the action of AK1 and since glucose and hexokinase are also present in the stain, glucose-6-phosphate is formed with the regeneration of ADP. In the presence of G6PD and the coenzyme NADP, glucose-6-phosphate thus formed is oxidised to 6- phosphogluconate and during this reaction NADP is reduced to NADPH. Electron donar NADPH in turn reduces yellow soluble MTT to purple insoluble formazan, deposited at the sites of AK1 activity. This reaction proceeds rapidly in the presence of PMS, which acts as a catalyst. Therefore, AK1 isozymes are seen as purple bands against a yellow background (Fig. 3c).

Fig.3c Starch gel electrophoresis patterns of AK1

Adenosine Deaminase (ADA)

Tank Buffer (0.1 M Citrate, pH 5.0)

21.0 grams Citric acid dissolved in about 750 ml distilled water, pH adjusted to 5.0 with NaOH solution and final volume made up to 1 litre with distilled water.

Gel Buffer

1:20 dilution of tank buffer

Overlay Buffer (0.06 M Tris, pH 8.0)

727 mg Tris dissolved in about 75 ml distilled water, pH adjusted to 8.0 with dilute HC1 and final volume made up to 100 ml with distilled water.

Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 ml gel buffer.

Sample Application

Whatman No. 17 filter paper inserts were used for loading haemolysates into gel which were placed in a line, about 5 cm from cathode.

Electrophoresis

Gel was run at 230 volts in a refrigerator for 4 hours (haemoglobin moved about 4 cm towards cathode and ADA isozymes moved towards anode).

After electrophoresis paper inserts were removed and about 10 cm anodal portion of the gel was sliced horizontally into two halves using a thread cutter and the lower (bottom) half was stained as follows.

Staining Method

10 mg Adenosine, 40 mg Sodium arsenate, 2.5 mg MTT, 2.5 mg PMS (added just before mixing with agar solution), 0.4 I.U. Nucleoside phosphorylase (added just before mixing with agar solution) and 0.04 I.U. Xanthine oxidase (added just before mixing with agar solution) dissolved in 12.5 ml overlay buffer.

In an another beaker 200 mg (1.6%) agar was dissolved by boiling in 12.5 ml overlay buffer and cooled down to about 55ºC.

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The contents of the two beakers were mixed gently and immediately evenly poured over the gel and allowed to set for a few minutes avoiding exposure to direct sunlight.

Stained gel was placed in a covered plastic box and incubated at 37ºC in darkness for 45–60 minutes. ADA isozymes were clearly seen as purple bands and phenotypes were recorded (Fig. 3d).

Reaction Mechanism

Substrate adenosine is converted to inosine by ADA which in turn is converted to hypoxanthine in the presence of nucleoside phosphorylase and sodium arsenate. Hypoxanthine is then oxidised by the action of xanthine oxidase and during this reaction yellow soluble MTT in the presence of PMS is reduced to purple insoluble formazan and deposited at the sites of ADA activity. The ADA isozymes are therefore seen as purple bands against a yellow background (Fig. 3d).

Fig.3d Starch gel electrophoresis patterns of ADA

Phosphohexose Isomerase (PHI)

Tank Buffer (0.25 M Tris- 0.057 M Citrate, pH 8.0)

30.29 grams Tris and 11.98 grams Citric acid, dissolved in 1 litre distilled water.

Gel Buffer (0.017 M Tris-0.0032 M Citrate, pH 8.0) 18

2.06 grams Tris and 0.67 grams Citric acid, dissolved in 1 litre distilled water.

Overlay Buffer (0.06 M Tris, pH 8.0)

727 mg Tris dissolved in about 75 ml distilled water, pH adjusted to 8.0 with dilute HC1 and final volume made up to 100 ml with distilled water. Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 gel buffer.

Sample Application

Thin (Whatman No. 3) filter paper inserts were used for loading haemolysates into gel which were placed in a line at about 1/3 of gel i.e. about 10 cm from cathode.

Electrophoresis

Gel was run at 75 volts in a refrigerator overnight for 17 hours (haemoglobin moved towards anode and PHI moved towards cathode).

After electrophoresis, anodal portion of the gel was discarded and the paper inserts were removed. The cathodal portion was sliced horizontally into two halves (slabs) using a thread cutter and the lower half was stained as follows.

Staining Method

7 mg Fructose-6-phosphate, 4 mg NADP, 4 mg MTT, 4 mg PMS (added just before mixing with agar solution) and 1 I.U. G6PD (added just before mixing with agar solution), dissolved in 12.5 ml overlay buffer.

In an another beaker 200 mg (1.6%) agar was dissolved by boiling in 12.5 ml overlay buffer and cooled down to about 55ºC.

The contents of the two staining beakers were mixed gently and immediately evenly poured over the gel and allowed to set for few minutes, avoiding exposure to direct sunlight.

Stained gel was placed in a covered plastic box and incubated at 37ºC in darkness for 30 minutes. PHI isozymes were seen as purple bands and phenotypes were recorded (Fig. 3e).

Reaction Mechanism

Purple colouration at the sites of PHI activity is due to deposition of purple formazan, dependent upon the conversion of fructose-6-phosphate to glucose-6-phosphate by the enzyme, with subsequent conversion of glucose-6-phosphatase to 6-phosphogluconate by G6PD in the presence of coenzyme NADP. During the latter 19 stages of reaction, yellow MTT in the presence of PMS (catalyst) is reduced to purple formazan, and NADPH produced earlier is converted back to free NADP. Hence PHI isozymes are seen as purple bands against yellow background (Fig. 3e).

Fig. 3e Starch gel electrophoresis patterns of PHI

Esterase D (ESD)

Tank Buffer (0.15 M Citrate-0.245 M Phosphate, pH 5.9)

31.5 grams Citric acid and 38.22 grams Sodium dihydrogen orthophosphate, dissolved in about 750 ml distilled water, pH adjusted to 5.9 with NaOH solution and final volume made up to 1 litre with distilled water.

Gel Buffer

1:100 dilution of tank buffer

Overlay Buffer (0.05 M Acetate, pH 6.9)

410 mg Sodium acetate (anhydrous), dissolved in about 75 ml distilled water, pH adjusted to 6.9 with dilute acetic acid and final volume made up to 100 ml with distilled water.

Gel Preparation

30 grams Hydrolysed starch (Connaught) cooked in 250 ml gel buffer.

Sample Application

Thin (Whatman No. 3) filter paper inserts were used for loading haemolysates into gel which were placed in a line, about 6 cm from cathode.

Haemolysates treated with an equal amount of 0.05 M DTT or DTE (Cleland’s reagent) for atleast 15 minutes prior to loading gave sharper resolution of bands.

Electrophoresis

Gel was run at 80 volts in a refrigerator overnight for 17 hours (haemoglobin moved towards cathode and ESD isozymes moved towards anode).

After electrophoresis, the cathodal portion of the gel was discarded and paper inserts were removed. About 10 cm anodal portion of the gel was sliced horizontally into two halves using a thread cutter and the lower (bottom) half (slab) was stained as follows.

Staining Method

2.5 mg 4-Methylumbelliferyl acetate dissolved in a few drops of acetone and added to 25 ml overlay buffer. Cut surface of the gel was covered with a Whatman No. 3 filter paper wick soaked in staining mixture and incubated uncovered for 5–10 minutes at room temperature. ESD isozymes were clearly seen as fluorescent bands under long wave ultra violet (UV) light, after removing the filter paper overlay. Phenotypes were recorded immediately since the fluorescent zones tend to diffuse quickly (Fig. 3f).

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Reaction Mechanism

At the sites of ESD activity on the gel the non-fluorescent substrate 4-methylumbelliferyl acetate is hydrolysed into fluorescent 4-methylumbelliferone and acetate. Hence ESD isozymes are seen as strongly fluorescent bands against a non-fluorescent background (Fig. 3f).

Fig.3f Starch gel electrophoresis patterns of ESD

STATISTICAL ANALYSIS

Allele Frequency Calculations

From the phenotype data of the six investigated enzyme systems, allele frequencies were calculated for estimation of different measures of genic differentiation and genetic distance among the Muslims of Punjab. Since the alleles involved in the polymorphic enzyme systems included in this study are co-dominant and genotypes corresponding to all phenotypes are known, allele frequencies were calculated by the gene counting method (Mourant et al., 1976) described below.

Let each individual be represented by the pair of alleles comprising his genotype. Then each AA homozygote has two A alleles and each BB homozygote has two B alleles; while each heterozygote (AB) has one A and one B allele. The total number of alleles is twice the number of individuals (n). The frequencies of the A and B alleles were obtained as follows. 2AA  AB A  and 2n

2BB  AB B  2n

For example, in a total of 803 Sunni Muslims of this study typed for the Adenylate Kinase locus 1 (AK1) system, 626 were homozygote AK1 1, 12 were homozygote AK1 2 and 165 were heterozygote AK1 1,2. The frequencies of the co-dominant alleles AK1*1 and AK1*2 calculated by gene counting method, were

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2(626)165 AK1*1  2(803)  0.8823

2(12)165 AK1*2  2(803)  0.1177

The Chi-square Test for Goodness of Fit

This was applied to test significance of deviations between the observed and expected phenotype numbers.

(Expected number  Observed number) 2 Chi  square χ 2  Expected number

The degree of freedom (d.f.) was obtained using the following formula.

d.f.  C  1R  1

Where C is the number of columns and R is the number of rows.

In the example cited above, the observed numbers of the three phenotypes viz., AK1 1, AK1 2 and AK1 1,2 were respectively 626, 12 and 165. The expected numbers of these phenotypes were calculated as follows.

AK11  (AK 1* 1) 2 .n  (0.8823) 2 .803  625.12 AK1 2  (AK1* 2) 2 .n  (0.1177)2 .803 11.12 AK1 1,2  (2 AK 1* 1. AK 1* 2).n  20.8823 0.1177 .803  166.75.

Thus, the goodness of fit Chi-square (2) value was calculated as follows.

(625.12 626)2 11.12 122 166.75 1652 χ 2    625.12 11.12 166.75

 0.0892

The above value when checked in the distribution table of the Chisquare against 2 degrees of freedom [(3–1)(2– 1)] yielded probability of 0.95 > p > 0.98. Thus, this Chi-square test revealed that the distribution of the AK1 system in the Sunni Muslims of Punjab was in genetic (Hardy-Weinberg) equilibrium and the deviations observed from expectations in phenotype numbers were not statistically significant.

The Contingency Chi-square Test

To compare the distribution of phenotypes of a system in two (or more) populations, the Contingency Chi- square test or inter-group Chi-square test was applied.

Let A denote the number of AK1 1 phenotypes, B number of AK1 1,2 phenotypes, and C number of AK1 2 phenotypes in the Sunni Muslims, and let a, b and c, respectively, denote the number of these phenotypes in the Shia Muslims.

The expected numbers of the three AK1 phenotypes in the Sunni Muslims were calculated as follows.

(A  a) A  B  C AK11  A  B  C  a  b  c (B  b) A  B  C AK11.2  A  B  C  a  b  c Similarly, the expected numbers of the AK1 phenotypes in the Shia Muslims were calculated. 22

Thereafter, the Chi-square (2) was calculated using the following formula.

(Expected number  Observed number) 2 χ 2  Expected number

The significance of the observed 2 value obtained was found out from the Chi-square distribution table. Statistically significant values (p  0.05) demonstrate heterogeneous distribution of the system in question in two compared populations, and non-significant values demonstrate homogeneity.

For instance, in the Sunni Muslims and Shia Muslims of this study the observed distribution of the AK1 system was as follows.

Population N AK1 phenotypes 1 1,2 2 Sunni Muslims 803 626 165 12 Shia Muslims 189 153 34 2 Total 992 779 199 14

The expected distribution of the system in these Muslim populations was calculated as follows. Sunni Muslims: 779 AK11  Χ 803 992

 630.58 199 AK1,2  803 992

161.09 14 AK12   803 992

11.33

Shia Muslims: 779 AK11  189 992

148.42 199 AK11,2  189 992

 37.91 14 AK12   803 992

 2.67

Thereafter, the overall contingency (inter-group) 2 value was obtained as follows. (630.58  626) 2 161.09 1652 11.33 122 X 2    630.58 161.09 11.33 148.42  1532 37.91  342 2.67  22    148.42 37.91 2.67

 0.8822 The degree of freedom (d.f.) in this case was d.f. 3 12 1

 2

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Since this 2 value at 2 d.f. was found to be statistically non-significant (0.70 > p > 0.50), the two compared populations i.e. the Sunni Muslims and Shia Muslims were found to be homogeneous for the AK1 system.

Inbreeding Coefficient (F)

In a panmictic population there is no correlation between homologous genes of uniting gametes. However, in an inbreeding population such a correlation is to be expected. Accordingly, Wright (1921) proposed a correlation coefficient which he called Fixation Index commonly known as inbreeding coefficient (F). For any locus with co-dominant alleles, F  1  H / 2 pq

Where H is the observed frequency of heterozygotes and p and q are the frequencies of co-dominant alleles in a population. High positive F values are indicative of inbreeding.

While F estimates are usually positive, under certain circumstances F values can be negative, which can be attributed to little or no systematic subdivision and systematic avoidance of consanguineous mating, respectively. Exogamy can lead to a negative F estimate, that is, if a population is not highly endogamous, this estimate will be negative as a result of the effects of migration alone (Harpending et al., 1973).

The F values are not very robust and react markedly to all factors influencing the observed phenotype numbers; it is therefore, important to look at the overall pattern that they show and exercise caution in interpretation.

For illustration, in the Sunni Muslims of the present study inbreeding coefficient (F) for the AK1 system was calculated as follows.

F  1  165/166.75 

  0.0105

The significance of this F value was checked by 2 obtained between the observed and expected phenotype numbers of the system. Since this value (2 = 0.0892) at 2 degrees of freedom was non-significant, the F estimate for the AK1 system in the Sunni Muslims of Punjab (+0.0105) was considered non-appreciable.

Measures of Genic Differentiation

Wahlund’s variance (Wahlund, 1928) and a set of measures given by Nei (1973) are two commonly used measures of genic differentiation in population genetics.

Gene Diversity Analysis

Nei (1973) showed that the gene diversity of the total population (Hr) can be divided into intra-subpopulational gene diversity (Hs) and inter-subpopulational gene diversity (Dst). For a population that is subdivided into S subpopulations, Nei (1973) gave the following equation for gene diversity in the total population (HT).

H T .  H S  DST

Where HS is the average gene diversity within the subpopulations and DST is the average gene diversity between the subpopulations.

HT can be calculated by taking the average gene frequencies of all subpopulations as representative of the total population. The average gene diversity of all subpopulations yields HS. The difference of these two values (HT and HS) gives the value of DST.

The absolute magnitude of gene differentiation among subpopulations is provided by DST while the gene differentiation relative to the total population (GST) is given by the following equation.

GST  DST / H T

For instance, in the present study consider the Sunni Muslims and Shia Muslims as two subpopulations of a total (Muslim) population. Using the allele frequency data for the AK1 enzyme system, the measures of gene diversity can be calculated as follows.

2 2 HT 1 0.8823 0.8995/2  0.1177 0.1005/2

 0.194394

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2 2 2 2 Hs  2  0.8823  0.1177  0.8995  0.1005 /2

 0.194246

DST  0.194394  0.194246

 0.000148

G ST  0.000148/0 .194394

 0.000761 Wahlund’s Variance

In a population divided into subpopulations, Wahlund (1928) showed that the frequency of heterozygotes decreases by an amount equal to twice the variance of allele frequency among subpopulations (compared to that expected in a single population) and the frequency of homozygotes increases by the same amount. The greater homozygosity will result when the subpopulations display greater differences in allele frequency and the decrease in overall heterozygosity is a direct measure of heterogeneity between subpopulations. For a diallelic locus, Wahlund’s variance (f) is defined as follows. f  σ p 2 / p 1  p  Where p2 is the variance of allele frequency p across subpopulations, and p is the mean of p across subpopulations.

For example, again in the AK1 system, for AK1*1 allele, the variance (( p2) in two Muslim subpopulations is 0.000074 and the mean ( p) is 0.890900. Thus f  0.000074 / 0.890900 1  0.890900 

 0.000761

Nei’s Standard Genetic Distance

Genetic distance is simply a tool to investigate the relationship among a set of populations (Balakrishnan, 1988). The genetic relationship between the present and other Muslim groups has been assessed using the measure of genetic distance (D) proposed by Nei (1972). D is one of the most widely used methods of studying genetic relationship between populations. In fact, “the basic principle of this method is that any allelic difference in electrophoretic mobiity or immunological reaction is caused by atleast one codon difference at the gene level and thus the average number of codon difference per locus can be estimated from allele frequency data statistically” (Nei, 1987).

Consider two randomly mating diploid populations X and Y in which multiple alleles segregate at a locus. Let xi th and yi be the frequencies of the i allele in X and Y, respectively.

2 2 The probability of identity of two randomly chosen alleles is jx = xi in population X and jy = yi in population Y. The probability of identity of two randomly chosen alleles (one from population X and one from population

Y) is jxy = xiyi. The normalised identity of genes between X and Y with respect to the locus is given as follows (Nei, 1972).

I  jxy / jx . jy

This quantity is unity when two populations have same alleles in identical frequencies, while it is zero when they have no common alleles.

The normalised identity of genes (I) between X and Y with respect to all loci is defined as follows.

I  J / J . J xy x y

Where Jx, Jy and Jxy are the arithmetic means of jx, jy and jxy respectively over all loci, including monomorphic loci. The genetic distance (D) between X and Y populations is defined as follows.

D  loge I

For instance, the probability of identity of two randomly chosen alleles at the AK1 locus in the present Muslim populations is:

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Sunni Muslims 0.88232  0.11772

 0.7923065 Sunni Muslims 0.89952  0.10052

 0.8192005

The probability of identity of two randomly chosen AK1 alleles, one from the Sunni Muslims and one from the Shia Muslims is:

Sunni Muslims. Shia Muslims = (0.8823)(0.8995)+(0.1177)(0.1005) = 0.8054577

Similarly, such three probabilities for other five enzyme loci were calculated and arithmetic means over all the loci were computed and are given below.

J x  0.731561

J y  0.747733 J  0.738842 xy

The normalised identity of genes (I) between two Muslim populations of the present study with respect to all six loci was calculated as follows.

0.738842 I  0.7315610.747733

 0.9989713

Thus, the genetic distance (D) between the Sunni Muslims and Shia Muslims of Punjab was

D   loge 0.9989713  0.000447

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CHAPTER – IV

RESULTS AND DISCUSSION

Results of this biochemical investigation among the Muslims of Malerkotla (Punjab), in terms of phenotypes and allele frequencies of the six marker enzyme systems studied viz., ACP1, PGM1, AK1, ADA, PHI and ESD, are set out in Tables 4-9. These are given separately for the two endogamous sects of the Muslims studied viz., the Sunni and Shia. Departure from Hardy-Weinberg (H.W.) equilibrium was checked using the goodness of fit Chi-square test. Barring one instance of the Sunni Muslims showing significant deviation in the ACP1 system (2 = 6.3721, d.f. 2, 0.05 > p > 0.02), in each of the remaining five enzyme systems investigated the distribution of the observed phenotypes was found in compliance with the Hardy-Weinberg expectations. This shows that either of the two Muslim sects studied here from Punjab was in almost complete genetic equilibrium.

The Muslims have only occasionally been studied for the red cell enzyme markers in India (Fig. 4) and the first such comprehensive study on any Muslim population of the country was reported by Roberts and his colleagues as early as mid seventies (Roberts et al., 1974). A review of literature reveals that only a handful of Muslim populations have been investigated in India since then. These include the Muslims of Pulwama (Chahal et al., 1989b) and Srinagar (Bhasin et al., 1992b) districts in Jammu and Kashmir, Muslims of Lucknow in Uttar Pradesh (Lanchbury et al., 1996), Muslims of Delhi (Papiha et al., 1976), Muslims of Surat in Gujarat (Papiha et al., 1981), Muslims of Indore in Madhya Pradesh (Roberts et al., 1974), Muslims of Hyderabad in Andhra Pradesh (Roberts et al., 1980), Muslims of North Kanara district in Karnataka (Vijayakumar et al., 1987), Muslims of Calicut in Kerala (Saha et al., 1976), Muslims of Calcutta in West Bengal (Mukherjee et al., 1974) and Muslims of Guwahati in Assam (Mukherjee et al., 1989) (Tables 16-21). However, in view of their large proportion (about 12%) of the country’s population (Census, 1991), genetic investigations among the Muslims of India are clearly still inadequate to characterize them fully and accurately.

Since from the state of Punjab no earlier comparative data are available on the Muslims, the results of the present electrophoretic analysis have been compared first with various caste populations of the state reported in literature (Tables 10-15) and thereafter with various Muslim groups studied from different other states of India (Fig. 4, Tables 16-21). It is hoped that such an analysis will help reveal genetic similarities and differences among people of Punjab on one hand, and the Muslim populations of India on the other.

Fig. 4 Map of India showing areas (·) from where Muslim populations have been studied

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GENETIC MARKERS IN THE MUSLIMS OF PUNJAB

Acid Phosphatase Locus1 (ACP1) Variation in the Muslims of Punjab

Results of the erythrocyte enzyme ACP1 polymorphism are presented in Table 4. In both the Muslim samples tested from Punjab five (A, A,B, B, A,C, B,C) out of the six common phenotypes of the system were found. However, no rare ACP1 phenotype (variant) was encountered in this genetic survey. The Chi-square test revealed that there was concordance between the observed and expected phenotype numbers in the Shia sample (Table 4).

In terms of the allele frequencies, the incidence of the ACP1*A was found to be higher in the Sunni Muslims (0.3431) than in the Shia Muslims (0.3280). Infrequent allele ACP1*C was present in both the Muslim sects tested, albeit in very low frequency (0.0019 and 0.0132) (Table 4).

The distribution of phenotypes and allele frequencies of the ACP1 system in different populations of Punjab, including the present, is listed in Table 10. This table shows that in addition to the common frequent phenotypes A, A,B and B found in each population, the infrequent common phenotypes A,C and B,C were also detected in many of them. It is observed that in Punjab, the ACP1*A frequency varied from 0.250 in the Jat (Singh et al., 1974) to as high as 0.430 in the Jat Sikh of Ludhiana (Woolley et al., 1983). Into this range the frequencies observed in the two Muslim sects of this study (0.3431 and 0.3280) fit quite well. In fact, the present ACP1*A frequencies are very similar to those of several caste populations reported from Patiala, Faridkot and Bhatinda districts (Table 10). Thus, as for the ACP1 enzyme system variation, there is great similarity between the Muslims and various caste populations in the North-West Indian state of Punjab. Barring statistically significant differences with the Jat and Khatri reported by Singh et al., (1974) (which both had shown rather low incidence of ACP1 A phenotype) and with the Kamboh and Jat Sikh of Patiala and Ludhiana districts, respectively (which both had shown a rather high incidence of this phenotype), all other populations reported from Punjab were found to be homogeneous with the present Muslim populations (Table 22). Thus typing of the ACP1 system in this study has clearly demonstrated that the genetic constitution of the Muslims of Punjab is similar to that of several other caste populations of the state.

As for the ACP1 system 13 Muslim samples, including the present two, have been studied from as many as 11 different states of India (Table 16). The ACP1*A range recorded in them was from 0.206 in the Muslims of Kerala (Saha et al., 1976) to 0.343 in the Sunni Muslims of Punjab (present study). The frequencies of this allele in the Sunni (0.343) and Shia (0.328) Muslim samples of this study are very similar to that of the Sunni Muslims of Jammu and Kashmir (0.320) and Muslims of Madhya Pradesh (0.328) as well as Andhra Pradesh (0.320). This indicates genetic affinities among the Muslims of different states of India. On the other hand, the present values are much higher than those recorded in the Muslims of some southern and eastern states of the country. The Chi-square analysis (Table 23) revealed that as for the ACP1 system, statistically significant differences are present between each of the present two Muslim sects of Punjab and the Muslims of Jammu and Kashmir, Uttar Pradesh, Madhya Pradesh, Kerala and West Bengal.

Thus, typing of the ACP1 system among the Muslims of Punjab has revealed that both the sects are genetically close (2 = 1.0012, d.f.= 2, 0.70 > p > 0.50) and similar to many and different from some other Muslim populations of India.

Phosphoglucomutase Locus1 (PGM1) Variation in the Muslims of Punjab

Table 5 shows the results of the PGM1 system in the Muslims of Punjab. In both the Muslim sects studied, the three common PGM1 phenotypes (1, 1,2, 2) were found. In addition, one example of a rare phenotype (1,3) was encountered in a Shia subject. Although rare variants of the PGM1 system (1,6 and 1,7) have been reported in Punjab (Papiha et al., 1972; Singh et al., 1974; Chahal & Papiha, 1981), this is the first report of the 1,3 variant from the state. The Chi-square test revealed that the observed and expected phenotype numbers were in agreement in both the Muslim sects investigated for the PGM1 system (Table 5).

The frequency of the PGM1*2 allele was found to be 0.2737 in the Sunni Muslims and 0.2910 in the Shia Muslims. Only one rare allele, PGM1*3, was found and that too only in the Shia Muslims with a very low frequency (0.0026) (Table 5).

Table 11 lists the distribution of the PGM1 system in various populations of Punjab, including the present Muslim material. This table shows that the PGM1*2 frequency in Punjab varied from 0.236 in the Mahajan Agarwal to 0.355 in the Ramdasia Sikh (Papiha et al., 1982). Thus, the frequencies observed in two Muslim sects of this study fit quite well into the already existing range for people of Punjab in general (Table 11). This demonstrates that like in the ACP1 system, in the PGM1 system also the genetic constitution of the Muslims of Punjab is similar to that of various caste populations of the state.

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Inter-group Chi-square comparisons of the Sunni and Shia Muslims with all caste populations reported from Punjab revealed no statistically significant differences for the PGM1 system (Table 22). This suggests genetic homogeneity in people of this North-West Indian state.

Data on the PGM1 system are available in literature on 8 Muslim populations of India and the present study adds information on further two. These include those reported from Jammu and Kashmir, Uttar Pradesh, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh and Kerala states (Table 17). This table shows that the frequency of the PGM1*2 allele in the Muslims of India varied from 0.252 in Delhi (Papiha et al., 1976) to 0.363 in Jammu and Kashmir (Chahal et al., 1989b). The present values fall well within this range and in particular show similarities with the Muslims of Jammu and Kashmir (Bhasin et al., 1992b), Uttar Pradesh (Lanchbury et al., 1996) and Madhya Pradesh (Roberts et al., 1974). This indicates genetic affinities among the Muslim populations inhabiting different parts of the country.

Like in other Indian populations, rare alleles of the PGM1 system have often been encountered in the Muslim populations also. Earlier PGM1*5 (Papiha et al., 1976; Roberts et al., 1980) and PGM1*7 (Papiha et al., 1976, 1981; Chahal et al., 1989b; Bhasin et al., 1992b) have been reported, but this is the first report on the presence of the rare allele PGM1*3 in the Muslims of India.

Unlike in the ACP1 system, in the PGM1 system, inter-group comparisons revealed no statistically significant differences, barring that between the Sunni Muslims of the present study and their counterparts from Jammu and Kashmir (2 = 6.5943, d.f. 2, 0.02 > p > 0.05) (Table 23). This suggests genetic homogeneity among the Muslims of North India.

Thus, like the ACP1 system, the typing of the PGM1 system in the Sunni and Shia Muslim sects of Punjab demonstrates that they are genetically homogeneous (2 = 1.8325, d.f. 2, 0.50 > p > 0.30) and similar to several other Muslim populations of north and central India.

Adenylate Kinase Locus 1 (AK1) Variation in the Muslims of Punjab

Distribution of the red cell enzyme AK1 polymorphism in the Sunni and Shia Muslims of Malerkotla is presented in Table 6. This table shows that only the common phenotypes of the AK1 system (1, 1,2, 2) were present in these two endogamous Muslim populations of Punjab and no rare variant was encountered. Like in the PGM1 system, in the AK1 system also no statistically significant differences were found between the observed and expected phenotype numbers in either of the Muslim sects studied (Table 6). The frequency of the AK1*2 allele in the Muslims of Punjab was calculated 0.1177 in the Sunni and 0.1005 in the Shia.

Distribution of the AK1 system in people of Punjab is given in Table 12 which shows that the AK1*2 allele frequency in the state ranged from a minimum of 0.077 in the Mahajan Agarwal (Papiha et al., 1982) to a maximum of 0.129 in the Ramgarhia Sikh (Chahal et al., 1986a). The AK1*2 frequencies observed in the present Muslim material from Punjab fit quite well into this range and in this respect show closeness to several caste populations of the state.

Contingency Chi-square test revealed that neither of the two Muslim sects of the present study showed statistically significant differences with any caste populations reported earlier from Punjab (Table 22). This demonstrates that, like in the PGM1 system, in the AK1 system also the Muslims of Punjab are homogeneous with other populations of the state.

The phenotype and allele frequency data of the AK1 system in the Muslim populations of different states of India, including the present one from Punjab, are presented in Table 18. This table shows that the incidence of the AK1*2 allele varied from a low of 0.062 in the Sunni Muslims of Jammu and Kashmir (Chahal et al, 1989b) to a high of 0.120 in the Muslims of Kerala (Saha et al., 1976). The present values are clearly well within this range.

A close examination of Table 18 revealed that barring the Muslims of Kerala in which Saha et al., (1976) reported an AK1*2 incidence of 0.120, all other Muslim populations studied from India have shown frequencies in a narrow low range i.e., between 0.062 (Sunni Muslims of Jammu and Kashmir) and 0.088 (Muslims of Assam). The AK1*2 values observed in the present Muslim samples from Punjab (0.101 and 0.118) were certainly on the higher side of this range and some of the highest so far reported for the Muslims of India. However, when the Chi-square comparisons were made they revealed statistically significant differences only between the Sunni Muslims of the present study and the Muslims of Jammu and Kashmir (2 = 6.4609, d.f. 2, 0.05 > p > 0.02) as well as the Muslims of Uttar Pradesh (2 = 6.9271, d.f. 2, 0.05 > p > 0.02) (Table 23).

Adenosine Deaminase (ADA) Variation in the Muslims of Punjab

Phenotypes, allele frequencies and goodness of fit Chi-square values for the ADA system among the Muslims of Punjab are given in Table 7. All the three common phenotypes of the system (1, 1,2, 2) were found only in the 29

Sunni sample, while the phenotype ADA 2 could not be detected in the Shia sample, perhaps because of their comparatively small sample size. No rare ADA phenotype was encountered in the present study. Like in the previous enzyme systems, there was a good fit between the observed and expected phenotype numbers in this system also in both the endogamous Muslim sects studied here from Punjab, the differences being statistically non-significant in the either sect. The ADA*2 allele frequency was found to be 0.0940 in the Sunni Muslims and 0.0767 in the Shia Muslims.

The distribution of phenotypes and allele frequencies of the ADA system in various populations of Punjab, including the present Muslims, are listed in Table 13. This table demonstrates great Variation in the ADA*2 frequency in people of Punjab, ranging from 0.077 in the Shia Muslims (present study) to 0.185 in the Kamboh (Chahal et al., 1989a). In fact, the ADA*2 frequency recorded in the Shia Muslims of Malerkotla (0.077) is the lowest so far reported from the state.

When both the Sunni and Shia Muslims of the present study from Punjab were statistically compared with other caste populations of the state, they showed significant differences only with a few of them i.e. with the Ramdasia Sikh, Mahajan Agarwal and Kamboh, all of Patiala district. Barring these three, all other populations reported from Punjab were found to be similar with the Muslim populations of Punjab for the ADA system (Table 22).

The ADA enzyme system distribution in various Muslim populations of India is presented in Table 19. It is clear from this table that the frequency of the ADA*2 allele showed a wide Variation in them, ranging from 0.077 in the Shia Muslims (present study) to a surprisingly high of 0.500 in the Muslims of Assam (Mukherjee et al., 1989). The ADA*2 incidence recorded in the Shia Muslims of the present study (0.077) is the lowest so far reported among the Muslim populations of India. Incidentally, the frequency in the Sunni Muslims of Punjab (0.094) was very similar to that of the Muslims of Uttar Pradesh (0.091) reported by Lanchbury et al. (1996). Inter-group 2 comparisons of the present Muslim material with other Muslim populations of India are given in Table 23 which shows that the Muslim populations from Punjab are dissimilar with their counterparts in Jammu and Kashmir, Uttar Pradesh, Madhya Pradesh, Andhra Pradesh as well as Assam.

Phosphohexose Isomerase (PHI) Variation in the Muslims of Punjab

Results for the PHI system are presented in Table 8. The Chi square test for goodness of fit showed that there was good agreement between the observed and expected number of phenotypes in both the Muslim sects studied from Punjab, the deviations from Hardy-Weinberg equilibrium in either case being statistically non-significant. In addition, to the common PHI phenotype 1, two rare variant phenotypes 1,3 and 1,5 of the system were also recorded in the present material.

Using phenotype data of Table 8 the frequency of the rare PHI*3 allele was calculated to be 0.0013 in the Sunni Muslims and 0.0026 in the Shia Muslims of the present study. Rare variant phenotype PHI 1,5 was encountered only in the Sunni Muslims and frequency of the rare PHI*5 allele was calculated to be 0.0006 in them.

Distribution of the PHI system in people of Punjab is listed in Table 14. It is evident from this table that PHI 1,3 variant is present in most of the caste populations studied from Punjab. This study demonstrated the presence of an another PHI variant viz., PHI 1,5 in people of the state. In Punjab the PHI*3 frequency varied from nil in some groups to as high as 0.053 in the Kamboh (Chahal et al., 1989a). The frequency recorded in the Shia Muslims (0.003) is very similar to that of Khatri (Chahal et al., 1987) and Jat Sikh (Chahal & Papiha, 1981). All the 13 populations reported from Punjab were found to be homogeneous with the present Muslims when compared statistically (Table 22).

As for the Muslims of India, PHI distribution is listed in Table 20. The Muslims of Jammu and Kashmir (Bhasin et al., 1992b), Gujarat (Papiha et al., 1981) and Andhra Pradesh (Roberts et al., 1980) lacked any variant, while in each of the remaining Muslim populations the rare variant PHI 1,3 was present. The detection of the rare PHI phenotype 1,3 in both the Muslims samples of Punjab was therefore in agreement with earlier observations among the Muslims of India. An example of another variant PHI 1,9, was found in the Muslims of Delhi by Papiha et al., (1976). Besides these two, no other variant of the system had been reported in the Muslims of India. Thus, it is interesting to note that PHI 1,5 variant observed in the present material is the first report of this rare phenotype in people of Punjab as well as in the Muslims of India.

The Chi-square analysis showed that statistically significant differences were present between the Sunni Muslims of the present study and the Muslims of Delhi (2 = 12.5416, d.f. 2, 0.01 > p > 0.001) as well as of Kerala (2 = 16.6436, d.f. 2, p > 0.001). All other remaining Muslim populations of India were however found to be homogeneous with the Muslims of Punjab (Table 23). Thus, the typing of the PHI system among the Muslims of Punjab suggests that they are genetically similar to other Muslim populations of India, as well as to several caste populations of the state.

30

Esterase D (ESD) Variation in the Muslims of Punjab

Results of the ESD system in the Muslims of Punjab are shown in Table 9. Common phenotypes of the system (1, 1,2, 2) were found to be present in both the Muslim sects investigated from Punjab but no rare phenotype was detected in either of them. The Muslims of the present study were found to be in genetic equilibrium for the ESD system also i.e. no statistically significant differences were found between the observed and expected number of phenotypes by the goodness of fit 2 test. The frequency of the ESD*2 allele was found to be 0.2522 in the Sunni Muslims and comparatively lower, 0.1905, in the Shia Muslims. Statistical treatment between the two endogamous Muslim groups of the present study showed significant differences only for this system (2 = 8.9486, d.f. 2, 0.02 > p > 0.01) (Table 24).

Data available on the ESD system in various populations of Punjab are presented in Table 15. The frequency of the ESD*2 allele in them varied from a low of 0.157 in the Khatri to a high of 0.253 in the Ramdasia Sikh, both of Patiala district (Chahal et al., 1986b). Frequency of this allele observed in the present Muslim material thus fits quite well into the range observed for other non-Muslim people of Punjab.

Comparison with other caste populations of Punjab showed that only the Sunni sect of the Muslims of the present study were significantly different from some of them (Table 22) while the rest of the populations were found to be homogeneous with the Muslims of Punjab.

Distribution of the ESD system in various Muslim populations of India is shown in Table 21. The ESD*2 frequency ranged from 0.185 in the Muslims of Gujarat (Papiha et al., 1981) to 0.293 in the Muslims of Assam (Mukherjee et al., 1989).

The Chi-square comparisons revealed very few differences and that too only between the Shia Muslims of present study and the Muslims of Andhra Pradesh (2 = 6.4606, d.f. 2, 0.05 > p > 0.02) and West Bengal (2 = 8.0324, d.f. 2, 0.02 > p > 0.01) (Table 23). Thus it would appear that like other enzyme systems investigated here, overall the Muslims of Punjab showed homogeneity with other Muslim populations of India for the ESD system also.

Thus, the present enzyme variation data on the Muslims of Punjab demonstrate that barring the ESD system, there were no significant differences in the distribution of the biochemical systems studied between the Sunni and Shia sects. Furthermore, the present Muslim populations of Punjab show similarities in allele frequencies with various caste populations of the state as well as with other Muslim populations reported from different states of India.

Using the present allele frequency data, an attempt has been made in the following to study the genetic structure of the Muslims of Punjab.

Inbreeding Coefficient In The Muslims of Punjab

Table 25 sets out the values for inbreeding coefficient (F) for the two Muslim populations studied from Punjab together with the corresponding 2, for each of the six enzyme systems investigated. In the Sunni Muslims four values out of 6, and in the Shia Muslims three out of 6 were positive and none of the negative values showed a significant 2. Barring a significant positive value for ACP1 system among the Sunni Muslims, all other positive F values were also non-significant in both the Muslim sects.

Comparing the two, for the Sunni Muslims the mean value of F was +0.0201 which was higher than that found in the Shia sample (+0.0068) (Table 25). These values suggest that there is tendency among the Sunni Muslims to inbreeding to a greater degree than in the Shia Muslims and here the internal endogamous structure of these two Muslim populations which are known to prescribe consanguineal matings may be responsible. These F values also reflect the effectiveness of the division of the Muslim population into two endogamous sects, but with isolation being less pronounced among the Shia Muslims.

Genic Differentiation in The Muslims of Punjab

Gene Diversity Analysis

Estimates of Nei’s measures of gene diversity in the Muslims of Punjab are presented in Table 26.This table shows that intra-populational gene diversity (HS) ranged from a low of 0.004490 at PHI locus to a high of 0.455805 at the ACPI locus. Similarly, inter-populational gene diversity (DST) was lowest at the PHI locus (0.000001) but highest at the ESD locus (0.001903). The mean DST for these two Muslim sects studied here from Punjab was calculated to be 0.000412 over all the six loci and this value was much lower than the mean Hs (0.260045). This shows that the gene diversity between the two Muslim popualtions of the present study was very much lower than the gene diversity within them.

31

Coefficient of gene differentiation (GST) in the present Muslim material was calculated the lowest (0.000202) at the ACP1 locus and the highest (0.005521) at the ESD locus. Thus, gene diversity analysis demonstrates that the former locus was the least differentiated and the latter the most between the Sunni and Shia Muslims of Punjab.

Wahlund’s Variance Analysis

Estimates of Wahlund’s variance (f) in the Muslims of Punjab are set out in Table 27. The f value showed a great heterogeneity over the studied loci, varying from 0.000015 at the ACP1 locus to 0.005522 at the ESD locus. This suggests that genic differentiation in the Muslims of the present study was not uniform over loci; it was lowest at the former locus and highest at the latter.

Thus observations regarding genic differentiation among the Muslims of Punjab by Wahlund’s variance analysis are essentially the same as noted above by the gene diversity analysis. In fact, the mean f value (0.00130) (Table 27) over all the six enzyme loci studied in the Muslims of Punjab was very close to the mean GST (0.00135) (Table 26).

GENIC DIFFERENTIATION IN THE MUSLIMS OF INDIA

Gene Diversity Analysis

As for the Muslims of India, locus-wise estimates of gene diversity are given in Table 28. HS (intra-populational gene diversity) showed great heterogeneity in the Muslims of India, and again the PHI locus showed the minimum value (0.010857) and the PGM1 locus the maximum (0.416303). The mean HS in the Muslims of India comes out to be 0.263473 (Table 28). This value was very much similar to the mean HS value obtained earlier for the Muslims of Punjab (0.260045) (Table 26).

Inter-populational gene diversity (DST) was highest at the ADA locus (0.027547) and lowest at the PHI locus (0.000086). Like in the Muslims of Punjab, in the Muslims of India also the mean DST (0.006140) was comparatively much lower than the mean HS (0.263473) in them. As for the GST values, the ADA locus showed the maximum value (0.105068) and the ESD locus the lowest (0.005143). The mean GST value over all six loci for the Muslim populations of India was found to be 0.023226, which is very much higher than the value recorded for the present two Muslim populations of Punjab (0.00135) (Table 26).

This demonstrates that the degree of genic differentiation among the various Muslim populations of different states of India is much greater than that between two of the Punjab state investigated in the present study.

Wahlund’s Variance Analysis

Estimates of genic differentiation studied by the Wahlund’s variance (f) in the Muslim populations of India are given in Table 29. This table shows that the ESD locus was the least differentiated (f = 0.005143) and the ADA the most differentiated (f = 0.104841). The mean f value over the six studied loci (0.023390) was very close to the mean GST value recorded in them (0.023226) (Table 28).

Nei’s Standard Genetic Distances In People of Punjab

Genetic affinities between two Muslim populations of the present study and other populations of Punjab, as well as various other Muslim populations of India were assessed by calculating Nei’s standard genetic distance (D) among them. Estimates of D, based on allele frequency data from the six erythrocyte enzyme systems studied, among the present Muslims and other populations of Punjab are presented in Table 30.

Genetic distance (D) between the Sunni and Shia Muslims of the present study was recorded 0.000447. Compared to this, the D values found between either of these two Muslim sects of Punjab and several other caste populations of the state were lower. Thus, for example, genetic distance between the Sunni Muslims and the Jat Sikh of Bhatinda district was only 0.000314 and that between the Shia Muslims and the Jat Sikh of Patiala (0.000260) and Bhatinda (0.000266) districts even lower. Even the D value between the Sunni Muslims and the Jat Sikh of Patiala district (0.000453) was only slightly higher than that between the two Muslim sects studied here from Punjab (0.000447) (Table 30). On the other hand, barring a Punjabi sample which showed a low genetic distance with the Shia Muslims (0.000264), all other D values between the present two Muslim sects and the Ramdasia Sikh as well as the Mahajan Agarwal caste populations of the state were consistently rather high (range 0.000583-0.001568).

It is interesting to note that genetic distance between the Sunni and Shia Muslims of Punjab (0.000447) was higher than that between these two Muslim sects and two Jat Sikh samples of the state (range 0.000260- 0.000453). The genetic distance analysis therefore demonstrates that among different caste populations of Punjab, apparently the Muslims of Punjab are genetically closer to the Jat Sikh, suggesting genetic affinities between these two diverse and religiously different endogamous populations of the state.

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The overall genetic relationships among the Muslims and other populations of Punjab were revealed by dendrogram shown in Fig. 5, which is based on genetic distance matrix of Table 30. This figure shows that ignoring an early divergence of the Mahajan Agarwal in a single line cluster, the remaining populations of the state fall into two sub-clusters. One is composed of the Punjabi and Ramdasia Sikh populations and the other of the two different Jat Sikh samples and the present two Muslim samples. Thus, dendrogram approach clearly demonstrated close genetic relationship of the Muslims of Punjab with the Jat Sikh rather than any other caste population of the state.

NEI’S Standard Genetic Distances Among Muslim Populations of India

Nei’s genetic distance (D) matrix among different Muslim populations of India, including the present ones from Punjab, is set out in Table 31. As far the Sunni and Shia Muslims of the present study, they showed the lowest D value with each other (0.000447). While the former Muslim sect of Punjab showed highest genetic distance with the Muslims of Gujarat (0.001588), the latter showed it with the Sunni Muslims of Jammu and Kashmir (0.001586). In fact, all other Muslim populations of India had also shown consistently high D values with the Sunni Muslims of Jammu and Kashmir (range 0.001101–0.001586) as well as the Muslims of Gujarat (range 0.000586-0.001194) (Table 31).

Table 4 Distribution of the Acid Phosphatase locus 1 (ACP1) polymorphism among the Muslims of Punjab.

Population Number ACP1 Phenotypes ACP1 Allele frequencies 2 tested ACP1 A ACP1 A,B ACP1 B ACP1 A, C ACP1B, C ACP1*A ACP1*B ACP1*C (d.f.= 2) Sunni 803 Obs. 111 327 362 2 1 0.3431 0.6550 0.0019 6.3721 Muslims Exp. 94.52 360.93 344.55 1.03 1.97 Shia Muslims 189 Obs. 22 79 83 1 4 0.3280 0.6587 0.0132 0.0114 Exp. 20.34 81.68 82.01 1.64 3.29 Table 5 Distribution of the Phosphoglucomutase locus 1 (PGM1) polymorphism among the Muslims of Punjab.

PGM1 Allele Population Number PGM1 Phenotypes frequencies 2(d.f. = 2) tested PGM1 1 PGM1 1,2 PGM1 2 PGM1 1,3 PGM1*1PGM1*2PGM1*3 Sunni Muslims 802 Obs. 432 301 69 – 0.7263 0.2737 – 2.5134 Exp. 423.08 318.85 60.07 – Shia Muslims 189 Obs. 93 80 15 1 0.7064 0.2910 0.0026 0.2071 Exp. 94.30 77.70 16.00 0.70

Table 6 Distribution of the Adenylate Kinase locus 1 (AK1) polymorphism among the Muslims of Punjab.

Number AK1 Phenotypes AK1 Allele frequencies Population 2 (d.f. = 2) tested AK1 1 AK1 1,2 AK1 2 AK1*1 AK1*2 Sunni Muslims 803 Obs. 626 165 12 0.8823 0.1177 0.0892 Exp. 625.12 166.75 11.12 Shia Muslims 189 Obs. 153 34 2 0.8995 0.1005 0.0052 Exp. 152.92 34.17 1.91

Table 7 Distribution of the Adenosine Deaminase (ADA) polymorphism among the Muslims of Punjab Number ADA Phenotypes ADA Allele frequencies Population 2 (d.f. = 2) tested ADA 1 ADA 1,2 ADA 2 ADA*1 ADA*2 Sunni Muslims 803 Obs. 656 143 4 0.9060 0.0940 1.6479 Exp. 659.10 136.80 7.10 Shia Muslims 189 Obs. 160 29 – 0.9233 0.0767 1.3050 Exp. 161.11 26.78 1.11

Table 8 Distribution of the Phosphohexose Isomerase (PHI) polymorphism among the Muslims of Punjab Number PHI Phenotypes PHI Allele frequencies Population 2 (d.f. = 2) tested PHI 1 PHI 1,3 PHI 1,5 PHI*1 PHI*3 PHI*5 Sunni 803 Obs. 800 2 1 0.9981 0.0013 0.0006 0.0000 Muslims Exp. 800.00 2.00 1.00 Shia Muslims 189 Obs. 188 1 - 0.9974 0.0026 - 0.0000 Exp. 188.01 0.98 -

Table 9 Distribution of the Esterase D (ESD) polymorphism among the Muslims of Punjab

Number ESD Phenotypes ESD Allele frequencies Population 2 (d.f. = 2) tested ESD 1 ESD 1,2 ESD 2 ESD*1 ESD*2 Sunni Muslims 803 Obs. 450 301 52 0.7478 0.2522 0.0305 Exp. 449.06 302.87 51.07 Shia Muslims 189 Obs. 128 50 11 0.8095 0.1905 3.8194 Exp. 123.86 58.29 6.85

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For visual appraisal and better comprehension of the genetic affinities among the Muslims of India, genetic distance matrix of Table 31 was reduced to a dendrogram (Fig. 6). It is clear from this figure that barring the Sunni Muslims of Jammu and Kashmir and the Muslims of Gujarat, which differentiate themselves at an early stage, the rest of the Muslim populations of India form two sub-clusters; one of the Sunni and Shia Muslim populations of Punjab, and the other of the Muslims of Uttar Pradesh, Delhi, Jammu and Kashmir and Andhra Pradesh (Fig. 6).

Thus the dendrogram analysis has clearly illustrated genetic affinities among various Muslim populations of different states of India. While the genetic constitution of the Sunni Muslims of Jammu and Kashmir and Muslims of Gujarat appear to be quite different from that of other Muslim populations of India, genetic affinities between the present two endogamous Muslim populations of Punjab are also equally evident from their placement together in a single sub-cluster. This shows that there are close genetic affinities between the Sunni and Shia Muslims of Punjab. Furthermore, the present two Muslim populations from Punjab were found to be genetically closer to the Muslims of Uttar Pradesh, Delhi, Jammu and Kashmir and Andhra Pradesh but distant only from the Sunni Muslims of Jammu and Kashmir as well as the Muslims of Gujarat.

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Table 10 Distribution of the Acid Phosphatase locus 1 (ACP1) system in people of Punjab.

Area / Number ACP1 Phenotypes ACP1 Allele frequencies Reference Population tested ACP1 A ACP1 A,B ACP1 B ACP1 A,C ACP1 B,C ACP1*A ACP1*B ACP1*C Punjabi 140 17 54 67 1 1 0.318 0.675 0.007 Papiha et al. (1972) Brahmin 106 8 44 54 - - 0.283 0.717 - Singh et al. (1974) Khatri 130 7 52 71 - - 0.254 0.746 - Singh et al. (1974) Arora 103 8 42 52 1 - 0.286 0.709 0.005 Singh et al. (1974) Jat 158 10 59 86 - 3 0.250 0.741 0.009 Singh et al. (1974) Patiala

district Jat Sikh 106 11 49 45 - 1 0.335 0.660 0.005 Chahal et al. (1985) Ramdasia 88 9 36 42 1 - 0.312 0.682 0.006 Chahal et al. (1985) Sikh Ramgarhia 30 6 11 13 - - 0.383 0.617 - Chahal et al. (1985) Sikh Khatri 152 23 56 72 - 1 0.336 0.661 0.003 Chahal et al. (1985) Brahmin 99 10 47 41 - 1 0.338 0.657 0.005 Chahal et al. (1985) Bania 112 10 40 62 - - 0.268 0.732 - Chahal et al. (1985) Kamboh 167 28 86 52 - 1 0.425 0.572 0.003 Chahal et al. (1989a) Faridkot

district Jat Sikh 58 7 21 30 - - 0.302 0.698 - Chahal et al. (1985) Bhatinda

district Jat Sikh 145 16 69 56 - 4 0.348 0.638 0.014 Chahal & Papiha (1981) Ludhiana

district Jat Sikh 156 24 86 46 - - 0.430 0.570 - Wooley et al. (1983) Sangrur

district Sunni 803 111 327 362 2 1 0.343 0.655 0.002 Present study Muslims Shia Muslims 189 22 79 83 1 4 0.328 0.659 0.013 Present study

Table 11 Distribution of the Phosphoglucomutase locus 1 (PGM1) system in people of Punjab.

PGM1 Phenotypes PGM1 Allele frequencies Area/ Number PGM1 Reference Population tested PGM1 1 PGM1 1,2 PGM1 rare PGM1*1 PGM1*2 PGM1*3 PGM1*6 PGM1*7 2 Punjabi 140 63 60 16 1 0.668 0.329 – – 0.003 Papiha et al. (1972) Brahmin 106 57 40 9 – 0.726 0.274 – – – Singh et al. (1974) Khatri 130 68 52 10 – 0.723 0.277 – – – Singh et al. (1974) Arora 103 52 41 10 – 0.704 0.296 – – – Singh et al. (1974) Jat 158 88 60 9 1 0.750 0.247 – 0.003 – Singh et al. (1974) Patiala

district Jat Sikh 102 53 42 7 – 0.725 0.275 – – – Papiha et al. (1982) Ramdasia 76 30 38 8 – 0.645 0.355 – – – Papiha et al. (1982) Sikh Mahajan 104 61 37 6 – 0.764 0.236 – – – Papiha et al. (1982) Agarwal Bhatinda

district Chahal & Papiha Jat Sikh 145 79 50 14 2 0.724 0.269 – – 0.007 (1981) Ludhiana

district Wooley et al. Jat Sikh 150 82 54 14 – 0.727 0.273 – – – (1983) Sangrur

district Sunni 802 432 301 69 – 0.726 0.274 – – – Present study Muslims Shia Muslims 189 93 80 15 1 0.706 0.291 0.003 – – Present study

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Table 12 Distribution of the Adenylate Kinase locus 1 (AK1) system in people of Punjab

AK1 Phenotypes AK1 Allele frequencies Area / Population Number tested Reference AK1 1 AK1 1,2 AK1 2 AK1*1 AK1*2 Punjabi 140 112 28 – 0.900 0.100 Papiha et al. (1972) Brahmin 106 88 18 – 0.915 0.085 Singh et al. (1974) Khatri 131 108 23 – 0.912 0.088 Singh et al. (1974) Arora 103 83 20 – 0.903 0.097 Singh et al. (1974) Jat 149 117 32 – 0.893 0.107 Singh et al. (1974) Patiala district Jat Sikh 102 86 16 – 0.922 0.078 Papiha et al. (1982) Jat Sikh 105 83 20 2 0.886 0.114 Chahal et al. (1986a) Ramdasia Sikh 76 62 14 – 0.908 0.092 Papiha et al. (1982) Ramdasia Sikh 89 72 16 1 0.899 0.101 Chahal et al. (1986a) Ramgarhia Sikh 31 23 8 – 0.871 0.129 Chahal et al. (1986a) Khatri 150 126 24 – 0.920 0.080 Papiha et al. (1982) Brahmin 99 76 21 2 0.874 0.126 Chahal et al. (1986a) Mahajan Agarwal 104 88 16 – 0.923 0.077 Papiha et al. (1982) Bania 112 92 17 3 0.897 0.103 Chahal et al. (1986a) Kamboh 170 134 35 1 0.891 0.109 Chahal et al. (1989a) Faridkot district Jat Sikh 58 47 10 1 0.897 0.103 Chahal et al. (1986a) Bhatinda district Jat Sikh 145 108 37 – 0.872 0.128 Chahal & Papiha (1981) Ludhiana district Jat Sikh 156 119 36 1 0.879 0.121 Wooley et al. (1983) Sangrur district Sunni Muslims 803 626 165 12 0.882 0.118 Present study Shia Muslims 189 153 34 2 0.899 0.101 Present study

Table 13 Distribution of the Adenosine Deaminase (ADA) system in people of Punjab

Number ADA Phenotypes ADA Allele frequencies Area / Population Reference tested ADA 1 ADA 1,2 ADA 2 ADA*1 ADA*2 Punjabi 140 112 25 3 0.889 0.111 Papiha et al. (1972) Brahmin 106 86 20 – 0.906 0.094 Singh et al. (1974) Khatri 131 104 27 – 0.897 0.103 Singh et al. (1974) Arora 103 82 21 – 0.898 0.102 Singh et al. (1974) Jat 1491 115 33 – 0.886 0.111 Singh et al. (1974) Patiala district Jat Sikh 102 79 22 1 0.882 0.118 Papiha et al. (1982) Ramdasia Sikh 76 63 8 5 0.882 0.118 Papiha et al. (1982) Mahajan Agarwal 104 77 22 5 0.846 0.154 Papiha et al. (1982) Kamboh 170 111 55 4 0.815 0.185 Chahal et al. (1989a) Bhatinda district Jat Sikh 145 112 33 – 0.886 0.114 Chahal & Papiha (1981) Sangrur district Sunni Muslims 803 656 143 4 0.906 0.094 Present study Shia Muslims 189 160 29 – 0.923 0.077 Present study

1Include one rare phenotype ADA 1,3

Table 14 Distribution of the Phosphohexose Isomerase (PHI) system in people of Punjab

Number PHI Phenotypes PHI Allele frequencies Area / Population Reference tested PHI 1 PHI 1,3 PHI 3 PHI 1,5 PHI*1 PHI*3 PHI*5 Punjabi 100 99 1 – – 0.995 0.005 – Papiha et al. (1972) Patiala district Jat Sikh 78 78 – – – 1.000 – – Papiha & Chahal (1984) Ramdasia Sikh 76 76 – – – 1.000 – – Papiha & Chahal (1984) Mahajan Agarwal 104 98 5 1 – 0.966 0.034 – Papiha & Chahal (1984) Jat Sikh 105 105 – – – 1.000 – – Chahal et al. (1987) Ramdasia Sikh 89 88 1 – – 0.994 0.006 – Chahal et al. (1987) Ramgarhia Sikh 30 30 – – – 1.000 – – Chahal et al. (1987) Khatri 148 147 1 – – 0.997 0.003 – Chahal et al. (1987) Brahmin 99 99 – – – 1.000 – – Chahal et al. (1987) Bania 112 110 2 – – 0.991 0.009 – Chahal et al. (1987) Kamboh 170 152 18 – – 0.947 0.053 – Chahal et al. (1989a) Faridkot district Jat Sikh 57 56 1 – – 0.991 0.009 – Chahal et al. (1987) Bhatinda district Jat Sikh 145 144 1 – – 0.997 0.003 – Chahal & Papiha (1981) Sangrur district Sunni Muslims 803 800 2 – 1 0.998 0.001 0.001 Present study Shia Muslims 189 188 1 – – 0.997 0.003 – Present study

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Table 15 Distribution of the Esterase D (ESD) system in people of Punjab.

Number ESD Phenotypes ESD Allele frequencies Area/Population Reference tested ESD 1 ESD 1,2 ESD 2 ESD*1 ESD*2 Punjabi 40 26 12 2 0.800 0.200 Papiha & Nahar (1977) Patiala district Jat Sikh 105 70 31 4 0.814 0.186 Chahal et al. (1986b) Jat Sikh 102 64 35 3 0.799 0.201 Papiha et al. (1982) Ramdasia Sikh 89 52 29 8 0.747 0.253 Chahal et al. (1986b) Ramdasia Sikh 76 51 21 4 0.809 0.191 Papiha et al. (1982) Ramgarhia Sikh 30 19 8 3 0.767 0.233 Chahal et al. (1986b) Khatri 150 106 41 3 0.843 0.157 Chahal et al. (1986b) Brahmin 99 59 35 5 0.773 0.227 Chahal et al. (1986b) Mahajan Agarwal 104 69 28 7 0.798 0.202 Papiha et al. (1982) Bania 112 65 43 4 0.772 0.228 Chahal et al. (1986b) Kamboh 170 98 62 10 0.759 0.241 Chahal et al. (1989a) Faridkot district Jat Sikh 58 43 11 4 0.836 0.164 Chahal et al. (1986b) Bhatinda district Jat Sikh 145 91 49 5 0.797 0.203 Chahal & Papiha (1981) Ludhiana district Jat Sikh 156 102 50 4 0.814 0.186 Woolley et al. (1983) Sangrur district Sunni Muslims 803 450 301 52 0.748 0.252 Present study Shia Muslims 189 128 50 11 0.809 0.191 Present study

Table 16 Acid Phosphatase locus 1 (ACP1) variation in different Muslim populations of India.

ACP1 Phenotypes ACP1 Allele frequencies Number Area/Population ACP1 A, ACP1 ACP1 ACP1 Reference tested ACP1 A ACP1*A ACP1*B ACP1*C B B A, C B, C JAMMU AND KASHMIR Pulwama district Sunni Muslims 86 11 32 41 1 1 0.320 0.669 0.012 Chahal et al. (1989b) Srinagar district Muslims 122 9 41 71 1 - 0.246 0.750 0.004 Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 803 111 327 362 2 1 0.343 0.655 0.002 Present study Shia Muslims 189 22 79 83 1 4 0.328 0.659 0.013 Present study UTTAR PRADESH Lucknow Lanchbury et al. Muslims 110 14 33 63 - - 0.277 0.723 - (1996) DELHI Muslims 117 12 44 59 - 2 0.290 0.701 0.009 Papiha et al. (1976) GUJARAT Surat Muslims 61 6 19 35 - 1 0.254 0.738 0.008 Papiha et al. (1981) MADHYA PRADESH Indore Muslims 163 13 80 69 1 - 0.328 0.669 0.003 Roberts et al. (1974) ANDHRA PRADESH Hyderabad Muslims 86 10 34 41 1 - 0.320 0.674 0.006 Roberts et al. (1980) KARNATAKA North Kanara district Vijayakumar et al. Muslims 25 2 7 16 - - 0.220 0.780 - (1987) KERALA Calicut Muslims 119 2 45 72 - - 0.206 0.794 - Saha et al. (1976) WEST BENGAL Calcutta Mukherjee et al. Muslims 118 7 40 71 - - 0.229 0.771 - (1974) ASSAM Guwahati Mukherjee et al. Muslims 105 10 36 59 - - 0.267 0.733 - (1989)

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Table 17 Phosphoglucomutase locus 1 (PGM1) variation in different Muslim populations of India. PGM1 Phenotypes PGM1 Allele frequencies Number Area/Population PGM1 PGM1 Reference tested PGM1 1 PGM1 2 PGM1*1 PGM1*2 PGM1*3PGM1*5PGM1*7 1, 2 rare JAMMU AND

KASHMIR Pulwama district Sunni Muslims 84 35 35 13 1 0.631 0.363 - - 0.006 Chahal et al. (1989b) Srinagar district Muslims 122 61 51 8 2 0.717 0.275 - - 0.008 Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 802 432 301 69 - 0.726 0.274 - - - Present study Shia Muslims 189 93 80 15 1 0.706 0.291 0.003 - - Present study UTTAR PRADESH Lucknow Lanchbury et al. Muslims 108 59 39 10 - 0.727 0.273 - - - (1996) DELHI 1 Muslims 117 68 33 12 4 0.739 0.252 - 0.004 0.004 Papiha et al. (1976) GUJARAT Surat Muslims 61 26 27 6 21 0.667 0.325 - - 0.008 Papiha et al. (1981) MADHYA PRADESH Indore Muslims 164 90 58 16 - 0.726 0.274 - - - Roberts et al. (1974) ANDHRA PRADESH Hyderabad Muslims 85 50 24 10 1 0.735 0.259 - 0.006 - Roberts et al. (1980) KERALA Calicut Muslims 125 58 52 15 - 0.672 0.328 - - - Saha et al. (1976)

1 Include unidentified rare variants.

Table 18 Adenylate Kinase locuse 1 (AK1) variation in different Muslim populations of India.

AK1 Allele Number AK1 Phenotypes Area/Population frequencies Reference tested AK1 1 AK1 1,2 AK1 2 AK1*1 AK1*2 JAMMU AND

KASHMIR Pulwama district Sunni Muslims 89 78 11 - 0.938 0.062 Chahal et al. (1989b) Srinagar district Muslims 120 105 15 - 0.937 0.063 Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 803 626 165 12 0.882 0.118 Present study Shia Muslims 189 153 34 2 0.899 0.101 Present study UTTAR

PRADESH Lucknow Muslims 110 97 11 2 0.932 0.068 Lanchbury et al. (1996) DELHI Muslims 117 97 20 - 0.915 0.085 Papiha et al. (1976) GUJARAT Surat Muslims 61 51 10 - 0.918 0.082 Papiha et al. (1981) ANDHRA

PRADESH Hyderabad Muslims 86 75 11 - 0.936 0.064 Roberts et al. (1980) KERALA Calicut Muslims 125 97 26 2 0.880 0.120 Saha et al. (1976) ASSAM Guwahati Muslims 34 28 6 - 0.912 0.088 Mukherjee et al. (1989)

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Table 19 Adenosine Deaminase (ADA) variation in different Muslim populations of India

ADA Phenotypes ADA Allele frequencies Number Area/Population ADA Reference tested ADA 1 ADA 2 ADA*1 ADA*2 1,2 JAMMU AND

KASHMIR Pulwama district Sunni Muslims 89 63 25 1 0.848 0.152 Chahal et al. (1989b) Srinagar district Muslims 121 92 27 2 0.872 0.128 Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 803 656 143 4 0.906 0.094 Present study Shia Muslims 189 160 29 - 0.923 0.077 Present study UTTAR PRADESH Lucknow Muslims 110 94 12 4 0.909 0.091 Lanchbury et al. (1996) DELHI Muslims 1171 90 25 1 0.880 0.116 Papiha et al. (1976) GUJARAT Surat 1 Muslims 61 47 13 - 0.885 0.107 Papiha et al. (1981) MADHYA PRADESH Indore Muslims 163 122 39 2 0.868 0.132 Roberts et al. (1974) ANDHRA PRADESH Hyderabad Muslims 86 62 24 - 0.860 0.140 Roberts et al. (1980) ASSAM Guwahati Muslims 34 - 34 - 0.500 0.500 Mukherjee et al. (1989)

1 Include one rare phenotype ADA 1, 4

Table 20 Phosphohexose Isomerase (PHI) variation in different Muslim population of India

PHI Phenotypes PHI Allele frequencies Number Area/Population PHI PHI PHI PHI Reference tested PHI*1 PHI*3 PHI*5 PHI*9 1 1,3 1,5 1, 9 JAMMU AND

KASHMIR Pulwama district Sunni Muslims 87 86 1 - - 0.994 0.006 - - Chahal et al. (1989b) Srinagar district Muslims 122 122 - - - 1.000 - - - Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 803 800 2 1 - 0.998 0.001 0.001 - Present study Shia Muslims 189 188 1 - - 0.997 0.003 - - Present study UTTAR PRADESH Lucknow Muslims 110 109 1 - - 0.996 0.005 - - Lanchbury et al. (1996) DELHI Muslims 117 113 3 - 1 0.983 0.013 - 0.004 Papiha et al. (1976) GUJARAT Surat Muslims 61 61 - - - 1.000 - - - Papiha et al. (1981) MADHYA

PRADESH Indore Muslims 164 163 1 - - 0.997 0.003 - - Papiha (1974) ANDHRA

PRADESH Hyderabad Muslims 86 86 - - - 1.000 - - - Roberts et al. (1980) KERALA Calicut Muslims 125 120 5 - - 0.980 0.020 - - Saha et al. (1976)

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Table 21 Esterase D (ESD) variation in different Muslim populations of India.

ESD Allele Number ESD Phenotypes Area / Population frequencies Reference tested ESD 1 ESD 1,2 ESD 2 ESD*1 ESD*2 JAMMU AND

KASHMIR Pulwama district Sunni Muslims 88 49 33 6 0.744 0.256 Chahal et al. (1989b) Srinagar district Muslims 121 68 47 6 0.756 0.244 Bhasin et al. (1992b) PUNJAB Sangrur district Sunni Muslims 803 450 301 52 0.748 0.252 Present study Shia Muslims 189 128 50 11 0.809 0.191 Present study UTTAR PRADESH Lucknow Muslims 109 67 34 8 0.771 0.229 Lanchbury et al. (1996) DELHI Muslims 33 20 11 2 0.773 0.227 Ghosh (1977) GUJARAT Surat Muslims 62 43 15 4 0.815 0.185 Papiha et al. (1981) ANDHRA PRADESH Hyderabad Muslims 350 198 122 30 0.740 0.260 Kumar & Rao (1982) Muslims 86 50 32 4 0.767 0.233 Roberts et al. (1980) ASSAM Guwahati Muslims 104 53 41 10 0.707 0.293 Mukherjee et al. (1989)

Table 22 Inter-group2 values of Sunni Muslims and Shia Muslims of Punjab with other caste populations of the state. Only significant values (p < 0.05) are shown.

ACP1 PGM1 AK1 ADA PHI ESD Caste population Sunni Shia Sunni Shia Sunni Shia Sunni Shia Sunni Shia Sunni Shia (Area) Muslims Muslims Muslims Muslims Muslims Muslims Muslims Muslims Muslims Muslims Muslims Muslims Punjabi (Punjab) ------Brahmin (Punjab) ------Khatri (Punjab) 8.5240 ------Arora (Punjab) ------Jat (Punjab) 8.4756 ------Jat Sikh (Punjab) ------Ramdasia Sikh ------27.2286 13.3552 - - - - (Patiala) Ramgarhia Sikh ------(Patiala) Mahajan Agarwal ------18.4676 11.3225 - - - - (Patiala) Khatri (Patiala) ------12.651 - Brahmin (Patiala) ------Bania (Patiala) ------Kamboh (Patiala) 11.0242 6.5566 - - - - 25.2422 19.957 - - - - Jat Sikh (Faridkot) ------8.2284 - Jat Sikh ------(Bhatinda) Jat Sikh 13.621 7.6362 ------6.4579 - (Ludhiana)

Table 24 Chi-square test for heterogeneity among Muslim populations of Punjab, North-West India and India.

6 Muslim populations of North- 2 Muslim populations of Punjab 10 Muslim populations of India System West India 2 2 2  d.f. Significance  d.f. Significance  d.f. Significance ACP1 1.0012 2 N.S. 16.1089 10 N.S. 52.5338* 24 p<0.001 PGM1 1.8325 2 N.S. 12.9633 10 N.S. 21.2082 18 N.S. AK1 0.8802 2 N.S. 21.2251 10 0.02>p>0.01 26.4133 18 N.S. ADA 1.6405 2 N.S. 27.1931 10 0.01>p>0.001 165.6985 18 p<0.001 PHI 0.0921 1 N.S. 5.1124 5 N.S. 21.8167 9 N.S. ESD 8.9486 2 0.02>p>0.01 11.0266 10 N.S. 20.0556 18 N.S.

* For 13 groups N.S. = Non-significant

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Table 25 Estimates of inbreeding coefficient (F) in the Muslims of Punjab.

Enzyme Inbreeding Coefficient (F) 2 (d.f.2) system Sunni Muslims Shia Muslims Sunni Muslims Shia Muslims ACP1 +0.0932 +0.0301 6.3721* 0.0114 PGM1 +0.0560 –0.0332 2.5134 0.2071 AK1 +0.0105 +0.0050 0.0892 0.0052 ADA –0.0453 –0.0829 1.6479 1.3050 PHI 0.0000 –0.0204 0.0000 0.0000 ESD +0.0062 +0.1422 0.0305 3.8194 Mean +0.0201 +0.0068

* 0.05 > p > 0.02 Table 26 Estimates of Nei’s measures of gene diversity in the Muslims of Punjab.

H (Intra- Dsr (Inter- Gst (Coefficient of Enzyme Number of H (Gene diversity S T populational populational gene gene locus populations in total population) gene diversity) diversity) differentiation) ACP1 2 0.455897 0.455805 0.000092 0.000202 PGM1 2 0.407119 0.406944 0.000175 0.000430 AK1 2 0.194394 0.194246 0.000148 0.000761 ADA 2 0.156131 0.155981 0.000150 0.000959 PHI 2 0.004491 0.004490 0.000001 0.000223 ESD 2 0.344708 0.342805 0.001903 0.005521 Mean 0.260457 0.260045 0.000412 0.0001349

Table 27 Estimates of Wahlund’s variance (f) in the Muslims of Punjab.

Mean allele Variance of allele Wahlund’s variance Number of Enzyme locus Allele frequency frequency (f) populations 2 2 ( p) (p ) [p / p (1- p)] ACP1 ACP1*B 2 0.656850 0.000003 0.000015 PGM1 PGM1*1 2 0.716350 0.000099 0.000487 AK1 AK1*1 2 0.890900 0.000074 0.000761 ADA ADA*1 2 0.914650 0.000075 0.000958 PHI PHI*1 2 0.997750 0.000000 0.000055 ESD ESD*1 2 0.778650 0.000952 0.005522 Mean 0.001300

Table 28 Estimates of Nei’s measures of gene diversity calculated in the Muslim populations of India.

H (Gene H (Intra- D (Inter- G (Coefficient of Enzyme Number of T S ST ST diversity in total populational gene populational gene gene locus populations population) diversity) diversity) differentiation ACP1 13 0.408559 0.404389 0.004170 0.010207 PGM1 10 0.418612 0.416303 0.002309 0.005516 AK1 10 0.155716 0.154850 0.000866 0.005561 ADA 10 0.262182 0.234635 0.027547 0.105068 PHI 10 0.010943 0.010857 0.000086 0.007859 ESD 10 0.361662 0.359802 0.001860 0.005143 Mean 0.269612 0.263473 0.006140 0.023226

Table 29 Estimates of Wahlund’s variance (f) calculated in the Muslim population of India.

Mean allele Variance of allele Wahlund’s Number of Enzyme locus Allele frequency frequency variance (f) populations ( p) [p2) [p2 / p (1- p)] ACP1 ACP1*B 13 0.716615 0.002197 0.010816 PGM1 PGM1*1 10 0.704600 0.001157 0.005561 AK1 AK1*1 10 0.914900 0.000433 0.005563 ADA ADA*1 10 0.845100 0.013724 0.104841 PHI PHI*1 10 0.994500 0.000046 0.008419 ESD ESD*1 10 0.763000 0.000930 0.005143 Mean 0.023390

Table 30 Nei’s genetic distance (D) matrix among the present Muslim and other populations of Punjab.

Sunni Muslims Shia Jat Sikh Mahajan Agarwal Ramdasia Sikh Jat Sikh Population (Area) (Sangrur) Muslims(Sangrur) (Bhatinda) (Patiala) (Patiala) (Patiala) FC Punjabi 0.000697 0.000264 0.000526 0.001471 0.000085 0.000368 Jat Sikh (Patiala) 0.000453 0.000260 0.000258 0.000858 0.000695 Ramdasia Sikh (Patiala) 0.001221 0.000583 0.000978 0.001879 Mahajan Agarwal 0.001568 0.001490 0.001384 (Patiala) Jat Sikh (Bhatinda) 0.000314 0.000266 Shia Muslims (Sangrur) 0.000447 41

Table 31 Nei’s genetic distance (D) matrix among different Muslim populations of India.

Sunni Shia Muslims Muslims Muslims Muslims Muslims Population (Area) Muslims Muslims (Andhra (Uttar (Jammu & (Gujarat) (Delhi) (Punjab) (Punjab) Pradesh) Pradesh) Kashmir) Sunni Muslims (Jammu & 0.001527 0.001586 0.001101 0.001194 0.001510 0.001379 0.001319 Kashmir) Muslims (Jammu & Kashmir) 0.001289 0.001415 0.000597 0.000652 0.000358 0.000242 Muslims (Uttar Pradesh) 0.000664 0.000620 0.000430 0.000586 0.000140 Muslims (Delhi) 0.000518 0.000609 0.000201 0.000830 Muslims (Gujarat) 0.001588 0.000853 0.001182 Muslims (Andhra Pradesh) 0.000583 0.000797 Shia Muslims (Punjab) 0.000447

Fig.5 Dendrogram showing genetic relationships among the Muslims and other populations of Punjab.

Fig.6 Dendrogram showing genetic relationships among different Muslim populations of India.

42

CHAPTER – V

CONCLUSIONS

Genetically determined polymorphisms of various enzymes present within the membrane of red cell of human blood make it possible to study natural heritable variations and thus offer an excellent opportunity to investigate genetic makeup and structure of different human populations. During last three decades, population surveys employing red cell markers considering as many enzyme systems as possible have been a popular biochemical approach to understand the present day vast diversity of our species. The South Asian region of the Indian subcontinent with its large number of caste, religious, linguistic and tribal groups provides a unique situation for the study of genetic Variation and gene differentiation in man. From the higher altitude conditions of Ladakh (Jammu and Kashmir) and Lahaul-Spiti (Himachal Pradesh) in the north to the tropical and high background radiation areas of Kerala in the south, the vastly different and varied ecological conditions of India provide a potentially rewarding biosphere for the study of human population genetics.

As for the people of the North-West Indian state of Punjab, the bulk of the population falls into two major religious groups viz., the Hindus and Sikhs; the Muslims and Christians are in minority. From this border state, data pertaining to the distribution of biochemical genetic markers are essentially limited to various caste populations of the two majorities and except for a solitary G-6-PD deficiency report on the Muslims, the minorities have been totally neglected. It is therefore that this detailed genetic investigation based on as many as six red cell enzyme markers was planned among two endogamous sects of the Muslims of Punjab viz., the Sunni and Shia, firstly, to establish their genetic constitution using allele frequency data from polymorphic red cell enzyme systems of the ACP1, PGM1, AK1, ADA, PHI and ESD. Second, to calculate the level of inbreeding in the Muslims of Punjab using genetic data. Thirdly, this study aimed at assessing similarities and differences among the Muslim populations of Punjab with other caste populations of the state and various other Muslim populations reported from different other states of India.

Both Sunni and Shia Muslim populations of Punjab studied here were found to be in genetic (Hardy-Weinberg) equilibrium, for all the 6 enzyme systems investigated; the sole exception was the distribution of the ACP1 system in the Sunni sect which showed an unusually low proportion of heterozygotes. As for the allele frequencies, barring the ESD system, these were found to be almost similar in the remaining 5 systems in both the Muslim sects of Punjab (Tables 4-9). In fact, the frequencies recorded here in the Muslims of Punjab fit quite well into the ranges reported earlier in the people of the state (Tables 10-15), barring only the ADA*2 frequency recorded in the Shia Muslims of Punjab (0.077) which was the lowest so far reported for people of this North-West Indian state (Table 13). Compared to Muslim populations of different other states of India, the frequency of both the ACP1*A and AK1*2 alleles observed in the present Sunni material was found to be the highest and that of the ADA*2 allele in the Shia sample the lowest so far reported in the Muslims of India (Tables 16-21).

Statistically, there were no significant genetic differences between the Sunni and Shia sects of the present study, except for the ESD system (Table 24). Thus, these two different Muslim sects of Punjab are genetically quite homogeneous populations. However, both these Muslim populations showed heterogeneity with certain caste populations of Punjab, especially in the ACP1, ADA and ESD systems (Table 22). Intergroup χ2 comparisons of the Sunni and Shia Muslims of Punjab with the Muslim populations of various other states of India revealed that they were genetically close to some and different from others (Table 23).

When the present Muslims of Punjab were considered together with four other Muslim populations reported from North-West India, significant heterogeneity was observed in the AK1 and ADA systems, while such significant differences were obtained in other two systems (ACP1 and ADA), when all the ten Muslim populations reported so far from India were pooled together (Table 24).

The mean value of inbreeding coefficient (F) was found higher in the Sunni Muslims (+0.0201) than in the Shia Muslims (+0.0068) (Table 25). This suggests that the former sect of the Muslims of Punjab is more inbred than the latter. Estimates of Nei’s coefficient of differentiation (GST) and Wahlund’s variance (f) demonstrated that the genic differentiation between the two Muslim sects of Punjab (Tables 26-27) was much lower than that calculated among various Muslim populations of India (Tables 28-29).

Both the Sunni and Shia Muslim populations of Punjab showed comparatively low values of genetic distance (D) with the Jat Sikh rather than any other caste population of the state (Table 30). The dendrogram analysis depicted that these Muslim sects were genetically closer to the Jat Sikh and distant from the Ramdasia Sikh as well as the Mahajan Agarwal caste populations of Punjab (Fig. 5).

43

Compared to other Muslim populations of India, the Muslims of Punjab showed comparatively low genetic distances with the Muslims of Uttar Pradesh, Delhi, Jammu and Kashmir and Andhra Pradesh. On the other hand, genetic distances with certain other Muslim populations of the country viz., the Sunni Muslims of Jammu and Kashmir and the Muslims of Gujarat were found to be consistently high (Table 31). The dendrogram based on genetic distances clearly demonstrated strong genetic affinities between the present two endogamous Muslim populations of Punjab with their placement together in a single subcluster (Fig. 6). Among other Muslim populations of India, the present two Muslim populations from Punjab showed genetic affinities especially with the Muslims of Uttar Pradesh, Delhi, Jammu and Kashmir and Andhra Pradesh.

Thus in conclusion, the present biochemical genetics investigation among the Muslims of Punjab revealed that genetic constitution of two of its sects studied here from Punjab was very similar. Furthermore, they showed genetic affinities with other caste populations of Punjab (especially the Jat Sikh) and several Muslim populations inhabiting different other states of India. One possible explanation for this similarity could well be the fact that all over India the contemporary Muslim populations are known to be persecuted Hindu converts during the time of the Mughal rulers.

SUMMARY

To characterize genetically the Muslims of Punjab, a minority community of this North-West border Indian state, a biochemical genetics investigation using red cell enzyme markers was planned. The subjects were Muslim individuals inhabiting Malerkotla town (the only formerly Muslim state among the erstwhile princely states of Punjab) and blood samples from a total of 992 subjects belonging to the Sunni (803) and Shia (189) endogamous sects were collected in batches. In the laboratory at Patiala, haemolysates were analysed for the phenotypes of six different red cell enzyme marker systems of Acid Phosphatase locus 1 (ACP1), Phosphoglucomutase locus1 (PGM1), Adenylate Kinase locus1 (AK1), Adenosine Deaminase (ADA), Phosphohexose Isomerase (PHI) and Esterase D (ESD), following original standard starch gel electrophoresis techniques.

The present results demonstrate that there were no appreciable genetic differences between the Sunni and Shia Muslim populations studied here from Punjab and their genetic constitution was apparently homogeneous. However, both these endogamous Muslim populations showed heterogeneity with certain caste populations of the state, especially in the ACP1, ADA and ESD systems. When compared with their counterparts reported from various other states of India, the Muslims from Punjab were found genetically homogeneous with most of them and different from few others.

The present genetic data revealed that of the two sects, the Sunni Muslims were more inbred than the Shia Muslims. Furthermore, estimates of Nei’s coefficient of differentiation (GST) as well as Wahlund’s variance (f) demonstrate that the degree of genic differentiation between the two Muslim sects of Punjab state was much lower than that calculated among various Muslim populations inhabiting different states of India.

The Muslims of Punjab showed comparatively low genetic distances with the Jat Sikh rather than any other caste population of the state. Also, the dendrogram based on matrix of genetic distances with other Muslim populations of India revealed that although the present two Muslim populations from Punjab have very close genetic affinities, they are also close to the Muslims of Uttar Pradesh, Delhi, Jammu and Kashmir and Andhra Pradesh.

This baseline biochemical genetics study among the Muslims of Punjab demonstrates that the genetic constitution of two of its sects investigated here was very similar indeed. Furthermore, they showed genetic affinities with some caste populations of Punjab (especially the Jat Sikh) and several Muslim populations inhabiting different other states of India. One possible explanation for this similarity could well be the fact that all over India, the contemporary Muslim populations are known to be persecuted Hindu converts during the time of the Mughal rulers.

44

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