Session: CSAS Graduate Student Oral Competition: MS

149 - Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

Wednesday, July 14, 2021

1:00 PM – 1:15 PM EST

Location: Kentucky International Convention Center (KICC), M100/M103

Presenting Author(s)

Seyed Milad Vahedi, DVM, DVSc

Master's student of Animal Genetics and Breeding
Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N 5E3, Canada
Truro, Nova Scotia

Author(s)

Siavash Salek Ardestani

Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N 5E, Canada
Truro, Nova Scotia
Duy Ngoc Do

Postdoc
Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N 5E3, Canada
Truro, Nova Scotia
KK

Karim Karimi, n/a

Postdoc
Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N 5E, Canada
Truro, Nova Scotia
Younes Miar, n/a

Assistant Professor
Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N 5E, Canada
Truro, Nova Scotia

Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry's selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.