Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. resadmngncdsae.Tog hr aebe infiatcnrbtoso ouaingenomics population of dis- understanding of level genetic contributions genome comprehensive rare significant a of provided been have understanding & have studies few our there Bocchini, very advanced populations Though Amberger, Indian significantly of Scott, diseases. research have OMIM (Hamosh, monogenic efforts 7000 delineated Currently and global been orders diseases. These have accelerated Mendelian has monogenic etiologies 2005). methods with genetic McKusick, detection associated distinct variants throughput with and high and genes phenotypes novel tools of genetic pace in the decade last the over Advancements abroad. living INTRODUCTION Indians and populations Indian the to relevant tools (https://databases.lovd.nl/shared/variants?search screening and genetic appropriate [(http://clinindb.igib.res.in) Metabolism, of ClinIndb Conclusion: Inborn-errors database, (South-Asians). owned viz. ge- a project is genome disorders, cohort created in1000 study representation to have our its linked underrepresented that than We shown or were populations also earlier Indian variants have We of known These representatives not conditions. better hereditary either netically frequencies. other were various their and and informative of cancers Results: hereditary be terms Monogenic-diabetes, to populations. in found Indian databases were distinct ˜12% public geographically phenotype GSA, in and monogenic from variants ethnic of monogenic analyzed diverse individ- profile of 2795 the representing burden frequency from Of cohorts) Illumina) the the (global-screening-array, genomics estimate dataset assessed in-house to genotype have (multiple utilized was we study uals study The information this, this Towards its Methods: of or variants. aim populations. ClinVar The underrepresented associated Indian are datasets. in public populations variants using in Indian causing sparse population disease The world are variants different methods. relevant in clinically based variants sequencing deleterious w.r.t. and and rare genotyping cataloging throughput towards high efforts concerted been have Purpose:There Abstract 2020 28, April 7 6 5 4 3 2 1 Mukerji Mitali Varma Seth Malika Singhal Khushboo Narang Ankita genomic for India resource in A medicine in (ClinIndb): markers populations relevant Indian clinically multi-ethnic and rare of spectrum Frequency nttt fGnmc n nertv Biology Integrative and Genomics biology of integrative Institute Delhi and New genomics Sciences, of Medical Institute of Institute Ladakh UK India Leh London All Way, Hospital, Grafton Memorial 25 Norboo Health Sonam Women’s (IGIB) for Biology Institute Integrative UCL & Biology Genomics Integrative of & Institute Genomics of Institute CSIR by 1 %D2Mhmd2Frq2),t epciiin n eerhr ndanss oneigaddvlpetof development and counseling diagnosis, in researchers and clinicians help to =%3D%22Mohamed%20Faruq%22)], adn Jain Vandana , 1 hit Parveen Shaista , 6 1 n oamdFaruq Mohammed and , hrtrmUppilli Bharathram , 1 zaShamim Uzma , 5 rstaConsortium Trisutra , 1 rhn Vats Archana , 2 oj Sharma Pooja , 1 ie Anand Vivek , 7 1 1 1 hvn Parasher Bhavana , aaHillman Sara , 1 1 aaZahra Sana , aw Naushin Salwa , 3 am Dolma Padma , 1 1 rdaaMathur Aradhana , hnauSengupta Shantanu , 1 riYadav Arti , 4 Binuja , 1 , 1 , 1 , - Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. . act fkoldefrmttossetu n hi rqece,2)lc fsseai characterization systematic of have lack we Therefore, 2.) frequencies, populations. their Indian and in spectrum conducted mutations of studies for implementation knowledge earlier of in for paucity size challenges sample unmet 1.) low few either or a Primarily, India are of populations. there populations genetics Indian these clinical Despite in of needs research medicine India. the genomic genomics in meet translational to system enterprise and healthcare commercial public basic of and in segment funded hospitals government tertiary information of from unit level ongoing genetics are laboratories, genome efforts coun- Asian wide Asian of country different Multiple representation from adequate individuals 2019). for 1700 Consortium, over need (GenomeAsia100K of the data databases data (GAsP) highlighting level thus Project genome 100k tries, covered ail- GenomeAsia disorders, comprehensively neurogenetic storage published other a Lysosomal recently and provided infor- in genodermatosis A aggregate studies immunodeficiencies, that reports (http://guardian.meragenome.com/). primary disorders case of ments genetic multiple disorders based Indian and sequencing of dysplasias single knowledgebase skeletal and representative Dystrophy NGS also et Muscular from now (Pradhan mation Duchenne is cardiomyopathies anemia), There disorders, on Mitochondrial cell 2010). information ataxias, sickle made al., provides spinocerebellar and been v1.0 (CF), have (thalassemia database fibrosis contributions cystic noteworthy hemoglobinopathies disease (DMD), Other Genetic of (#IGDD). Indian genetics patients South The Indian the 2018), 3500 others. in al., over few (INDEX-db) et in individuals a (Hariprakash disease Indians and genetic Exome) south 2019) 1000 and the 2015), al., Consortium, Genome With P. informa- et Asian variant (G. P provide (South Project populations. (Ahmed to SAGE Genomes efforts Indian 1000 - additional from of scale diverse in genomes wide spectrum Asian the put genome have the across groups the understand research out at et global to tion carried other Sinha efforts Indian not 2009; systematic NGS, have Habib, P. of platforms variants advent 2007; & throughput monogenic al., Agarwal, high et Arya, and of Gupta 2007; Sinha, Mendelian availability 2010; 2009; al., limited al., al., et to et et Biswas Grover Due Kumar A 2017). 2014; 2015; al., al., 2008; al., et et al., Talwar et Giri et 2008; Kanchan 2011; al., Bhattacharjee al., 2012; et 2010; al., Chaki et al., and related 2010; Jha et adaptation associations al., Aggarwal altitude et days disease high 2015; Biswas et pre-NGS studies, and Arindam al., during Thanseem cutaneous pharmacogenomics et demonstrated al., neurological, 1999; HIV), (Aggarwal various been al., et for Malaria, disorders has et (Bamshad populations variability example, Majumder at-risk baseline populations (For of 2003; specific Indian diseases identification India al., diverse infectious an et of in of Kivisild based utility - pool 2011; haplogroup The distinct Agrawal, gene Y-chromosome are 2006). & and of al., group Khan, mitochondrial characterization there Ahmad, addition, AA in and Borkar, In and helped admixture 2001; DR 2008). of also Consortium, from degree have V. large specifically studies a G. on populations, exhibit (I. based isolated to unique (TB)) known however, and Tibeto-Burman are genetic sub-clusters, determinants large four and multiple major into IE (IE) are broadly are and Indo-European divided DR language be (DR), and classification. 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This with past, shaped 2016; 2009). admixtures been Singh, Majumder, also migration & also & as of Price, Sarkar-Roy, has art history Basu, and vast populations 2003; trade its the clines, of al., of geographical exchange consanguinity, diversity intercontinental and during ancestries genetic endogamy shared events the as and lineages Further, such linguistic factors and socio-cultural populations. cultural ethnic, global diverse many from diagnosis people with rapid billion facilitate 1.3 delivery of that healthcare comprises India populations guided need across medicine solutions. urgent biomarkers genomic healthcare an for genomic gen 2 u to due ako ersnaino ieetethnic different of representation of lack Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. aaaotgne,aeaddsaesau fsmlsicue nCrimdadGMDchr sprovided is cohort GOMED and Cardiomed included. in were setting, included disorders clinical samples hereditary in of various status chip (AIIMS, of disease genotyping in regions investigations and throughput age genetic Indian high gender, for North of about altitude clinicians utility from Data high from the lineage from referred understand IE subjects subjects to coro- Indo-European 19-40 included 287 study: of and study from GOMED 552patients Leh HAPS were iv) from The and Delhi). subjects iii) (TB) individuals based lineage the details). comprised897healthy control -Burman cohort of it case for Tibeto for group and (Table-S1 recruited disease age disease subjects artery artery 1449 the conditions. nary included coronary and cohort morbid for CARDIOMED genders non-mendelian ii) study other both GWAS 2017). related of al., and et representation (IPGTRA) health (Prasher –West equal various years IE of near (KLE), study was DR-South genetic (CBPACS), (IE) There for various IE-North Indo-European (JBR) for from from following IE-East asso- individuals lineages (in-house) from and included phenotype linguistic datasets studies cohorts (DR) mongenic genotype Ayurgenomics cohort Dravidian The and TRISUTRA and based conditions. i) Mendelian physiological genomics analyzed: of related were ongoing other analysis cohorts from or frequency polygenic outcome) populations and or pregnancy Indian non-mendelian curation and in at adaptation variants aimed (Hypoxia cohorts (n=287). ciated cohort HAP study Ayurgenomics study diverse TRISUTRA this GOMED 3.) represent Since, 1.) 4.) 1449), cohorts and – (n= these ) were cohort four (n=438 in cohorts CARDIOMED of cohort included These part study were subjects 2.) 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(K. variants Karczewski populations (ExAC) (K. diverse pathogenic Genome Consortium covers (gnomAD) 1000 SAS like it Database of repositories Aggregation i) Mendelian global other Genome representation : other and (iii) as with disorders compared unique hematological populations, distribution when Metabolism, and frequency Indian of novel provides errors ii) is in Inborn subjects, study disorders healthy for our variants 2795 ge- of of pathogenic size known content affordable sample of the and large Screening with brief, Global throughput cohorts using In high variants Indian annotated multiethnic a Illumina. clinical utilized global from 19,538 linked study (GSA) over disease of Array Our information monogenic provides of (n=2795). that novel catalogue tool populations comprehensive nomics of Indian a characterization provides diverse study in our 4.) variants primarily, issues disorders, genetic these of genetic address knowledge prevalent To of other scarcity and 3.) phenotypes disorders, OMIM mutations. monogenic 7000 various to of linked spectrum mutations pathogenic known of o ,3 ape eelae nteGnmSui eso . o lseigadcliggntps Out genotypes. calling and data clustering Raw for processing. 2.0 data version illumina GenomeStudio for the practices in best loaded follow were to tried samples we 3,132 the errors, for per calling GWAS as genotype ClinVar, reduce from performed To markers were ( important PharmaGKB. Experiments clinically from Screen- of information probes. representation Global pharmacogenomics genome-wide has Infinium and chip 642,824 throughput This high contains the that protocol. using manufacturer’s 1.0 system version iScan Array Illumina the ing in performed was (QC) Genotyping control Quality and processing Data Genotyping, al S1 Table . 3 Figure-1 ) Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. h ain sdltrosi IT osbyDmgn P oyhn, Polyphen Probably n Uncertain SIFT, (P) of Damaging in Variant Possibly deleterious assigned SIFT, - been in VUS-II. criteria has deleterious following is 3 with variant of , pathogenicity the Polyphen score predict in A tools (D) Polyphen three Damaging 2010). scores, all CADD Hakonarson, if used & com- We Significance a Li, effect. using such (Wang, their analysed other ANNOVAR and ascertain and selected to from were pathogenicity tools affects” of three factor, interpretation” of risk bination uncertain association, pathogenic”. or significance, Likely manuscript. “uncertain “no or as this and query “Pathogenic keywords significance throughout of “conflicting” our clinical pathogenic keywords filter narrowed as used keyword with further designated Variants we applied are we variants, then variants variants these and pathogenic relevant retrieve database likely clinically ClinVar To or in and Pathogenic variants. given rare pathogenic 1 [?]0.05. only Clin- likely frequency of in was allele RSID value and matches alternate dbSNP analysis pathogenic exact with and our with only Indels) coordinates alleles and of to (SNPs those the focus variants only Both 19,538 the retained selected we . 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Data may be preliminary. l,21) ute,w xlddte10 eoe F,ARadERsprppltosa ela the as et well Shah as 2011; populations al., super et EUR (Narang and populations AMR African AFR, super genomes and western other 1000 Indian from any the between population excluded than cline Indo-African we a population Further, admixed in super an 2011). present be EAS al., is of to to Indian majority demonstrated closer populations of to earlier IGV part is proximal was as respect which group is well with descent), TB SAS as cohorts African genomes while Though study 1000 distant our of are populations. of population populations large diversity ( super genetic IGV populations (AFR) the the IGV assess African and and and genomes compare American(AMR) 1000 to used the PCA to analysis, this cohorts In study the of analysis diversity likely Genetic or pathogenic the to DISCUSSION belong AND information. markers and RESULTS the 9853 Annotation, retrieve GSA, to HTML5, and graphs. 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Data may be preliminary. gawl . ei . h,P,Snh .K,Sodn . ah,M . uej,M 21) EGLN1 (2010). M. types Mukerji, constitution . extreme . of . analysis Q., M. genetic Pasha, through T., Ayurveda. revealed Stobdan, in K., adaptation defined P. high-altitude Singh, P., in Jha, genetic involvement approach. S., Combined Ayurgenomics Negi, by (2015). S., revealed M. Aggarwal, hypoxia Mukerji, in 13 & outcome medicine, B., thrombotic translational Prasher, modulate of VWF S., Journal and Ghosh, Sara. EGLN1 A., from of Agrawal, hear effects A., I Gheware, once Leh S., for Aggarwal, no assistance grant financial the the give all would for REFERENCES I MLP901 and supported grant CSIR project facility. a genotyping as their for IARI acknowledge We work. respective their – for Data Variation Genome Indian work. respective analysis, their acknowledged. and for duly genotyping edged are quantitation, Cohort Khanna QC, Ayurgenomics Sangeeta Sample Kunj, TRISUTRA For Kalpana and AIIMS. Bhattacharyya and Aniket Leh Mukerji, from Mitali coordination and collection BSC0122, – MLP1802, Cohort MLP1601, - HAPS grants from funding restrictions the ethical acknowledge or Authors privacy to due available publicly not ACKNOWLEGEMNTS are data The author. sponding interest of Sharing: conflict Data no is there that declare Authors ailments. INTERST determining genetic OF in suspected and CONFLICT diagnostics with genetic patients rapid 2010 evolving in after for variants implications evident actionable direct quite has clinically study is our relevant growth of clinically content this yet. data of India, The updated growth in not and exponential is sequencing been database generation comparison has data this next Frequency there Importantly, and of Moreover, computed. advent biases. be absent. the from cannot after is 1993-2010 variants suffer estimates populations during might frequency global published carrier studies other literature therefore individual with from genetic patients, These collated from Indian was collated communications. data limitations. is personal This own in as diseases 2010). their well 52 al., have as from et they mutations (Pradhan however 6647 individuals catalogs of 5760 data such houses create (http://www.igdd.iicb.res.in/home.htm) to database disease efforts earlier were There Mtl uej,Bnj am,Rsn hms nbuiTiatiadAkt aag-ON data OMNI - Narang Ankita and management Triparthi and Anubhuti development Thomas, resource Roshni for Varma, Mukerji Binuja Mitali Mukerji, and Mitali Consortium 2. Variation Genome Indian study 1. field the supervising for Prasher analysis Bhavana and and genotyping study array genomic GSA the for supervising Khanna for Sangeeta Mukerji quan- and repository, Mitali Singhal DNA 6. Khushboo processing, and Vats, coordination, Archana identification collection, 5. sample sample for establishment, Yadav Arti field and for Varma Binuja Mukerji 4. Mitali studies Prasher, field Bhavana from study development Varma, the resource Binuja of for 3. coordination consortium for Ayurgenomics Mukerji TRISUTRA Mitali 2. and Prasher Bhavana 1. eoyigadanalysis and genotyping titation collection h aata upr h nig fti td r vial nrqetfo h corre- the from request on available are study this of findings the support that data The eakoldetecnrbto fMtl uej n hvn rse nSample in Prasher Bhavana and Mukerji Mitali of contribution the acknowledge We rceig fteNtoa cdm fSine,107 Sciences, of Academy National the of Proceedings 1,184. 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A rus )Aslt vrg rqec ieecso lnbadGs SS eecmae with compared were (SAS) GAsP and Clindb of across groups differences SAS compared gnoMAD frequency were and average term ExAc Absolute pathogenic” 1000G, gnomAD B) “likely databases. or groups. global “pathogenic” SAS in either populations containing SAS variants different of Number genomes A) -1000 different sign represent plus figure populations, main Figure-4: IGV the - populations. outside circles super Unfilled Keys study, EAS groups. this and isolated in SAS Cohorts of - exception circles the Filled genomes with populations: study 1000 plus this the populations, in of IGV genomes. cohorts any 1000 - by from Circles 3: covered populations distinct populations: Figure super not is different EAS group populations represent and EAS DR figure SAS group. and main - SAS AA sign the by covered Isolated outside are India. Keys populations of populations. large populations DR and TB IE from the of Majority populations. 2: Figure databse. ClinIndb shot (2012). Bioinformatics M. B. analysis. Figure-1 Neale, population . and . . genetics M., legends: Fromer, genotyping: Figure J., array-based doi:10.1093/bioinformatics/bts479 Maguire, for 2543–2545. B., 28(19), caller G. from England), variant Grant, variants (Oxford, J., rare genetic Carey, a of A., L. zCall: Crenshaw, annotation Singh, I., functional . J. ANNOVAR: Goldstein, . (2010). . H. chromosome Y Hakonarson, M., data. from & B. sequencing inference Reddy, M., high-throughput India: Li, V., of K., L. groups Wang, Bhaskar, tribal and K., castes V. DNA. lower Genetic Singh, mitochondrial the (2017). interethnicand among G., M. and affinities Chaubey, Singh, Genetic functional K., . (2006). a . Thangaraj, patients: . I., epilepsy G., Thanseem, in Arora, outcome S., treatment Grover, on R., reconstruction variant perspective. Baghel, haplotype CYP1A1 S., for of Mahendru, method (2013). contribution N., statistical J. new Kanojia, Zielenski, A P., Talwar, . (2001). P. . Donnelly, gene. . & data. regulator N., J., population conductance Sharma, from N. transmembrane Smith, H., fibrosis Yu, M., cystic Stephens, K., the Kaniecki, in F., variants 45 Goor, of genetics, Van liability Nature Variations R., disease (2008). the K. K. S. Defining Siklosi, Sharma, R., . . P. India. . Sosnay, in S., malaria falciparum S. to Pati, susceptibility N., and G. molecules 7 Jha, adhesion P., encoding areas Anand, genes in K., host residing Kanchan, in populations T., of Qidwai, differentiation S., Genetic Sinha, India. (2009). in S. endemicity Habib, malaria & (2011). high S., G. Agarwal, of A. V., Reddy, Arya, . S., . Sinha, . G., admixture. Kulkarni, P., Indian Govindaraj, with 154-161. S., descendants (1), D. African Rani, P., siddis: dataset. Moorjani, Indian R., project Tamang, genomes M., A. 1000 Shah, the underrepresen- in and diversity stratification Population genetic 8 (2016). subcontinent evolution, M. Ramsay, Indian & of A., tation Basu, A., Choudhury, D., Sengupta, 1,250. (1), okflwtesuyaddtiso eoyig aapoesn n ult otosadscreen and controls quality and processing data Genotyping, of details and study the flow Work : iest n eaens fIda ouain ihrsett 00gnmsSSadESsuper EAS and SAS genomes 1000 to respect with populations Indian of relatedness and Diversity ucetcvrg fI n Rlreppltosa ela Bcutr(yhglnes your by highlanders) (by cluster TB as well as populations large DR and IE of coverage Sufficient pcrmo ahgncVrat nCid n te eoi aaae SSPopulations) (SAS databases genomic other and Clindb in Variants Pathogenic of Spectrum h hraoeoisjunl 17 journal, pharmacogenomics The 1) 3460-3470. (11), 1) 1160. (10), h mrcnJunlo ua eeis 68 Genetics, Human of Journal American The M eeis 7 genetics, BMC uli cd eerh 38 research, acids Nucleic ora fgntc,88 genetics, of Journal 1,42. (1), 3,242. (3), 13 A srpeettv fbt nmDadExAc and gnomAD both of representative is SAS h mrcnJunlo ua eeis 89 Genetics, Human of Journal American The 1,77-80. (1), 1) e164-e164. (16), 4,978-989. (4), eoebooyand biology Genome aai journal, Malaria Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. figures/Figure-1/Figure-1-eps-converted-to.pdf 14 Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. figures/Fig-2/Fig-2-eps-converted-to.pdf 15 Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. figures/Fig-3/Fig-3-eps-converted-to.pdf 16 Posted on Authorea 4 Apr 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158602488.82271538 — This a preprint and has not been peer reviewed. Data may be preliminary. figures/Fig-4/Fig-4-eps-converted-to.pdf 17