Dataset Information

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs.

ABSTRACT: 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 R 2) 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.

SUBMITTER: Wu P

PROVIDER: S-EPMC6813215 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs.

Wu Pingxian P Wang Kai K Zhou Jie J Chen Dejuan D Yang Qiang Q Yang Xidi X Liu Yihui Y Feng Bo B Jiang Anan A Shen Linyuan L Xiao Weihang W Jiang Yanzhi Y Zhu Li L Zeng Yangshuang Y Xu Xu X Li Xuewei X Tang Guoqing G

Frontiers in genetics 20191018

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 i ...[more]

PMID: 31681435

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Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Project description:Confinement to farrowing crates is known to prevent sows from performing natural behavior, impairing animal welfare and possibly causing chronic stress. Hair cortisol analyses are increasingly used to detect chronic stress in animals. In the present study, hair samples were collected in the neck of sows kept either in farrowing crates (FC, n = 31) or in a loose-housing system (LH, n = 30) in six batches. Cortisol was extracted and analyzed using chemiluminescence immunoassay. Mean hair cortisol concentrations (HCC) did not differ significantly between the systems (LH: 1.85 ± 0.82 pg/mg, FC: 2.13 ± 1.53 pg/mg, P = 0.631). HCC was also not affected by other factors, such as sows' parity, number of piglets, skin lesion score or sow's weight loss during the farrowing period. However, highly significant differences were found in hair growth rates between different regions within the 20 × 30 cm shaving area. While the hair in both lateral parts of the shaving area grew almost identically (left: 7.48 ± 3.52 mm, right: 7.44 ± 3.24 mm, P = 1.00), the hair grew more in the area above the spine (12.27 + 3.95 mm, P < 0.001). In both systems, the mean individual lesion score of sows declined from the beginning to the end of the housing period (P < 0.001). No difference was found between FC and LH sows at any time (P > 0.05). Since neither the amount of skin lesions nor HCC differed between LH and FC sows, it may be concluded that confining sows in farrowing crates did not affect chronic stress levels. However, results may be affected by a downregulation of the hypothalamic-pituitary-adrenal axis during long-term stress, resulting in lower cortisol levels over time. HCC in sows may also be influenced by a dominant stressor, such as farrowing or the presence of suckling piglets. Thus, for a comparison of different farrowing systems regarding chronic stress, the use of hair cortisol measurement seems to be limited. The present results revealed that differences in hair growth rate within the same body region exist. This important finding should be considered when collecting hair samples in pigs, since hair cortisol concentrations may vary depending on hair growth and length.

Dataset Information

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs.

Publications

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs.

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