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A Wholegenome Association Study for Pig Reproductive Traits

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A Wholegenome Association Study for Pig Reproductive Traits doi:10.1111/j.1365-2052.2011.02213.x A whole-genome association study for pig reproductive traits S. K. Onteru*, B. Fan*,†, Z-Q. Du*, D. J. Garrick*, K. J. Stalder* and M. F. Rothschild* *Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames, IA 50011, USA. †Key Laboratory of Agricultural Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China. Summary A whole-genome association study was performed for reproductive traits in commercial sows using the PorcineSNP60 BeadChip and Bayesian statistical methods. The traits included total number born (TNB), number born alive (NBA), number of stillborn (SB), number of mummified foetuses at birth (MUM) and gestation length (GL) in each of the first three parities. We report the associations of informative QTL and the genes within the QTL for each reproductive trait in different parities. These results provide evidence of gene effects having temporal impacts on reproductive traits in different parities. Many QTL identified in this study are new for pig reproductive traits. Around 48% of total genes located in the identified QTL regions were predicted to be involved in placental functions. The genomic regions containing genes important for foetal developmental (e.g. MEF2C) and uterine functions (e.g. PLSCR4) were associated with TNB and NBA in the first two parities. Sim- ilarly, QTL in other foetal developmental (e.g. HNRNPD and AHR) and placental (e.g. RELL1 and CD96) genes were associated with SB and MUM in different parities. The QTL with genes related to utero-placental blood flow (e.g. VEGFA) and hematopoiesis (e.g. MAFB) were associated with GL differences among sows in this population. Pathway analyses using genes within QTL identified some modest underlying biological pathways, which are interesting candidates (e.g. the nucleotide metabolism pathway for SB) for pig reproductive traits in different parities. Further validation studies on large populations are warranted to improve our understanding of the complex genetic architecture for pig reproductive traits. Keywords biological pathways, parity, reproductive traits, whole-genome association. gene studies (Rothschild et al. 1996; Vallet et al. 2005; Introduction Spotter et al. 2009) have been conducted to find the QTL The pig, being a highly prolific mammal, could be one of the and associated genes for these traits. A recent candidate best species to study the genetic complexity of lowly heri- gene study established that the genes associated with pig table reproductive traits. Around 30% of culling in pig reproductive traits are primarily involved in energy production systems has primarily been because of repro- metabolism (Rempel et al. 2010). However, genomic ductive problems (Stalder et al. 2004). Reproductive per- improvement in pig reproductive traits requires detailed formance in commercial pig production systems is usually whole-genome association studies (WGAS) to explore the quantified by numerous economically important production chromosomal regions and genetic markers that explain the traits. These traits include total number born (TNB), num- variation in these traits. ber born alive (NBA), number of stillborn (SB), number of The pig genome project (http://www.sanger.ac.uk/ mummified foetuses at birth (MUM), and the gestation Projects/S_scrofa/) and the development of the Illumina length (GL) for each parity. Many linkage (Cassady et al. PorcineSNP60 BeadChip (Ramos et al. 2009) via the efforts 2001; King et al. 2003; Tribout et al. 2008) and candidate of the International Swine Genome Sequencing Consortium have provided an opportunity to carry out WGAS in the pig. Address for correspondence Advanced statistical methods (Meuwissen et al. 2001; M. F. Rothschild, Department of Animal Science, 2255 Kildee Hall, Kizilkaya et al. 2010) and tools (GENSEL software at http:// Ames, IA 50011, USA. bigs.ansci.iastate.edu) based on Bayesian approaches are E-mail: [email protected] available to analyse the large quantities of SNP chip data Accepted for publication 12 February 2011 for genomic selection and WGAS in domestic animal 18 Ó 2011 The Authors, Animal Genetics Ó 2011 Stichting International Foundation for Animal Genetics, 43, 18–26 WGAS for pig reproductive traits 19 populations (Fernando & Garrick 2008). During recent The Bayes C method is derived from the Bayes B approach years, several WGAS have been performed in humans (Meuwissen et al. 2001). The Bayes B method assumes a (http://www.genome.gov/admin/gwascatalog.txt), cattle different variance for every SNP and is heavily influenced (Feugang et al. 2009) and sheep (Becker et al. 2010). by the prior, whereas Bayes C uses a common variance However, WGAS studies using SNP chips are just now being that is reliably estimated from the SNP data. The Bayes B reported for the pig (Duijvesteijn et al. 2010). Therefore, a method is more sensitive to the given priors than is WGAS study was carried out using the PorcineSNP60 Bayes C. The Bayes C approach has been explained pre- BeadChip, which is the most powerful genomic platform for viously by Kizilkaya et al. (2010). Briefly, the basic model studying pig reproductive traits, including TNB, NBA, SB, of Bayes C is as follows: MUM and GL for the first three parities. XK y ¼ l þ xjbjdj þ e j¼i Materials and methods where y is the phenotype vector, l is the overall mean, K is Animals and phenotypes the total number of SNPs, xj is the column vector of a covariate SNP at locus j, b is the substitution effect of a SNP A total of 683 female pigs born over a period of 6 months j at locus j, and d is a random 0/1 variable that represents were included in the study. The sows were from a commer- j the absence (with the selected prior probability p) or pres- cial operation which utilized breeding stock from Newsham ence (with the probability 1 ) p) of the locus j in the model. Choice Genetics (West Des Moines, IA, USA). These animals b is conditional on r2 and is considered to be normally belonged to a Large White grandparent maternal line and a j b distributed N (0, r2). The e is the vector of random residuals Large White · Landrace parent maternal line and were used b assumed to be normally distributed. In this study, the for an earlier candidate gene study (Fan et al. 2009). To following modified statistical model from Kizilkaya et al. understand the genetic differences between these two lines, (2010) was used: population stratification was examined using an identical- by-state distance clustering method in the PLINK program y ¼ Xb þ Zu þ e (Purcell et al. 2007). This analysis clustered both of the lines into one cluster, suggesting that there are limited genetic where y is the vector of phenotypes, X is an incidence ma- differences between these lines. However, based on the ped- trix of fixed effects (b), Z is a matrix of SNP genotypes that 2 igree information and the desire to account for the limited were fitted as random effects (u) distributed N (0, ru ), and e differences, line was considered in the models for analyses. is the vector of random residual effects assumed to be nor- 2 All 683 sows produced a first parity litter, and subsets of 558 mally distributed N (0, re ). The fixed factors used in this and 442 sows produced litters in parities 2 and 3, respec- statistical model were gilt line, cohort group based on ani- tively. The reproductive traits, including TNB, NBA, SB, mal entry date on farm, and season for each trait in each MUM and GL, were recorded in these three parities. parity; l was set as an intercept. Most of the phenotypic data were normally distributed for TNB, NBA and GL in this population. However, the phenotypic distributions for SB DNA isolation, SNP array genotyping and quality control and MUM were not normal, and hence they were analysed The methods for DNA isolation and quantification have by ordered categorical threshold analysis using GENSEL been outlined in an earlier publication (Fan et al. 2009). software. Individual SNP effects were estimated from a DNA samples of 700–1000 ng with a ratio of A260/280 mixture model with a probability of 0.995 that any SNP higher than 1.50 and a concentration >20 ng/ll were used would have a zero effect such that approximately 250–300 for PorcineSNP60 BeadChip genotyping. Genotyping was non-zero SNP effects were fitted per iteration of a Markov performed commercially at GeneSeek, Inc. (Lincoln, NE, chain. This probability (0.995) was selected on the USA). The SNPs with call rate £80%, Gentrain score £40%, assumption that 250–300 SNP markers (0.005 of 57 814 minor allele frequency £0.001 and P-value <0.0001 for a SNP markers) may explain the variation in the pig repro- v2 test for Hardy–Weinberg equilibrium were excluded from ductive traits. This high value of probability (0.995) has the data set. After these quality control measures, a total of been shown to give faster convergence in the model aver- 57 814 SNPs out of a total of 64 232 SNPs qualified for aging procedures, yet still results in every SNP being in- association analyses. cluded in some small proportion of the models. A total of 50 000 iterations in a Markov chain with burn-in of 1000 iterations were run for the analyses. The results from this Genome-wide association analyses analysis included posterior distributions for the effects of The analyses were implemented with a Bayes C model each of the 57 814 markers, adjusted for the portfolio of all averaging approach using the GENSEL software (http:// the other fitted marker effects in the model, which were bigs.ansci.iastate.edu) for each trait in individual parities.
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