GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs
The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individu...
Main Authors: | , , , , , , , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2019-10-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01012/full |
id |
doaj-79fe35732a364a3586f6a4bd97307079 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pingxian Wu Kai Wang Jie Zhou Dejuan Chen Qiang Yang Xidi Yang Yihui Liu Bo Feng Anan Jiang Linyuan Shen Weihang Xiao Yanzhi Jiang Li Zhu Yangshuang Zeng Xu Xu Xuewei Li Guoqing Tang |
spellingShingle |
Pingxian Wu Kai Wang Jie Zhou Dejuan Chen Qiang Yang Xidi Yang Yihui Liu Bo Feng Anan Jiang Linyuan Shen Weihang Xiao Yanzhi Jiang Li Zhu Yangshuang Zeng Xu Xu Xuewei Li Guoqing Tang GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs Frontiers in Genetics imputation genome-wide association study genotyping-by-sequencing resequencing farrowing interval pigs |
author_facet |
Pingxian Wu Kai Wang Jie Zhou Dejuan Chen Qiang Yang Xidi Yang Yihui Liu Bo Feng Anan Jiang Linyuan Shen Weihang Xiao Yanzhi Jiang Li Zhu Yangshuang Zeng Xu Xu Xuewei Li Guoqing Tang |
author_sort |
Pingxian Wu |
title |
GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs |
title_short |
GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs |
title_full |
GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs |
title_fullStr |
GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs |
title_full_unstemmed |
GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs |
title_sort |
gwas on imputed whole-genome resequencing from genotyping-by-sequencing data for farrowing interval of different parities in pigs |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2019-10-01 |
description |
The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs. |
topic |
imputation genome-wide association study genotyping-by-sequencing resequencing farrowing interval pigs |
url |
https://www.frontiersin.org/article/10.3389/fgene.2019.01012/full |
work_keys_str_mv |
AT pingxianwu gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT kaiwang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT jiezhou gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT dejuanchen gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT qiangyang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT xidiyang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT yihuiliu gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT bofeng gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT ananjiang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT linyuanshen gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT weihangxiao gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT yanzhijiang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT lizhu gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT yangshuangzeng gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT xuxu gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT xueweili gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs AT guoqingtang gwasonimputedwholegenomeresequencingfromgenotypingbysequencingdataforfarrowingintervalofdifferentparitiesinpigs |
_version_ |
1725421290688348160 |
spelling |
doaj-79fe35732a364a3586f6a4bd973070792020-11-25T00:06:34ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-10-011010.3389/fgene.2019.01012468336GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in PigsPingxian Wu0Kai Wang1Jie Zhou2Dejuan Chen3Qiang Yang4Xidi Yang5Yihui Liu6Bo Feng7Anan Jiang8Linyuan Shen9Weihang Xiao10Yanzhi Jiang11Li Zhu12Yangshuang Zeng13Xu Xu14Xuewei Li15Guoqing Tang16Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaSichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, ChinaSichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaCollege of Life Science, Sichuan Agricultural University, Yaan, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaSichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, ChinaSichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaFarm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, ChinaThe whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.https://www.frontiersin.org/article/10.3389/fgene.2019.01012/fullimputationgenome-wide association studygenotyping-by-sequencingresequencingfarrowing intervalpigs |