An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

Yan, Guorong; Liu, Xianxian; Xiao, Shijun; Xin, Wenshui; Xu, Wenwu; Li, Yiping; Huang, Tao; Qin, Jiangtao; Xie, Lei; Ma, Junwu; Zhang, Zhiyan; Huang, Lusheng

doi:10.1007/s11427-020-1960-9

Cited by 17 publications

(27 citation statements)

References 63 publications

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“…Nonetheless, the resolution of genetic mapping depends not only on SNP density but also on experimental design and genetic structure in the mapping population. Sequence level imputation does not necessarily identify causative mutations in one single step (Yan et al, 2022). The availability of this resource will allow for suitable designs of mapping studies to achieve the highest possible resolution in specific circumstances and potentially nucleotide resolution.…”

Section: Discussionmentioning

confidence: 99%

Nucleotide resolution genetic mapping in pigs by publicly accessible whole genome imputation

Ding

Savegnago

Liu

et al. 2022

Preprint

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Genetic mapping to identify genes and alleles associated with or causing economically important quantitative trait variation in livestock animals such as pigs is a major goal in the genetic improvement animals. Despite recent advances in high throughput genotyping technologies, resolution of genetic mapping in pigs remains poor due in part to the low density of genotyped variant sites. In this study, we overcame this limitation by developing a reference haplotype panel for pigs based on 2,259 whole genome sequenced animals representing 44 pig breeds. We optimized the imputation procedure to achieve an average concordance rate in excess of 97%, non-reference concordance rate 91%, and r2 0.89. We demonstrated that genotype imputation using this resource can dramatically improve resolution of genetic mapping. Finally, we developed a public web server (swimgeno.org) to allow the pig genetics community to fully utilize this resource. We expect the resource and server to significantly facilitate genetic mapping and accelerate genetic improvement in pigs.

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Section: Discussionmentioning

confidence: 99%

Nucleotide resolution genetic mapping in pigs by publicly accessible whole genome imputation

Ding

Savegnago

Liu

et al. 2022

Preprint

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“…A popular strategy to preselect variants for the prediction model is based on association tests. Genome-wide association studies on WGS are expected to confirm associations that were already detected with marker arrays and identify novel associations (e.g., [35,70]). However, preliminary inspection of our empirical GWAS results showed that the added noise could easily offset the added information and fine-mapping remains challenging.…”

Section: Discussionmentioning

confidence: 99%

“…including pigs [36,37]. However, the fine-mapping of causal variants remains challenging due to the pervasive long-range linkage disequilibrium across extremely dense variation.…”

Section: Introductionmentioning

confidence: 99%

“…This has enabled the imputation of WGS for cattle populations. The lack of such available reference panels has been cited as an important limiting factor for performing similar studies in other species, such as pigs [36].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, these examples indicate how identifying causal variants could enhance genomic prediction with WGS. Whole-genome sequence data has already been applied in genome-wide association studies (GWAS) to identify variants associated to a variety of traits in livestock [2, [32][33][34], including pigs [35,36]. However, the fine-mapping of causal variants remains challenging due to the pervasive long-range linkage disequilibrium across extremely dense variation [37].…”

Section: Introductionmentioning

confidence: 99%

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Genomic prediction with whole-genome sequence data in intensely selected pig lines

Ros‐Freixedes

Johnsson

Whalen

et al. 2022

Preprint

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Background: Early simulations indicated that whole-genome sequence data (WGS) could improve prediction accuracy and its persistence across generations and breeds. However, results in real datasets have been ambiguous so far. Large data sets that capture most of the genome diversity in a population must be assembled so that allele substitution effects are estimated with higher accuracy. The objectives of this study were to use a large pig dataset to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays, to identify scenarios in which WGS provides the largest advantage, and to identify potential pitfalls for its effective implementation. Methods: We sequenced 6,931 individuals from seven commercial pig lines with different numerical size. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for 8 real traits and 9 simulated traits with different genetic architectures. Genomic predictions were performed using either data from a marker array or variants preselected from WGS based on linkage disequilibrium, functional annotation, or association tests. Both single and multi-line training sets were explored. Results: Using WGS improved prediction accuracy relative to the marker array, provided that training sets were sufficiently large, especially for traits with high heritability and low number of quantitative trait nucleotides. The performance of each set of predictor variants was not robust across traits and lines. The most robust results were obtained when preselected variants with statistically significant associations were added to the marker array. Under this method, average improvements of prediction accuracy of 2.5 and 4.2 percentage points were observed in within-line and multi-line scenarios, respectively, with training sets of around 80k individuals. Conclusions: Our results evidenced the potential for WGS to improve genomic prediction accuracy in intensely selected pig lines. Although the prediction accuracy improvements achieved so far were modest at best, we would expect that more robust improvements could be attained with a combination of larger training sets and optimised pipelines.

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Research progress and applications of genome‐wide association study in farm animals

Tan

Fahey

et al. 2023

Animal Research and One Health

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Exploring the genetic loci underlying economic traits is foundational for innovation in modern animal breeding technology. Genome‐wide association studies (GWAS) have been valid and commonly used tools to dissect the genomic variants associated with phenotypes for the past ∼20 years and have contributed to our understanding of genetic and molecular bases of various traits. Here, we comprehensively review the recent research progress on GWAS methods. We highlight the methodological advancements enabled by the combination of new proteome, transcriptome, epigenome, and metagenome information and multi‐omics analysis algorithms. The advances in GWAS investigations of chickens, pigs, cattle, and other animals during the last 5 years are also described. Finally, we discuss the current applications of GWAS in cutting‐edge breeding technologies and overall future perspectives of the post‐GWAS era.

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An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

Cited by 17 publications

References 63 publications

Nucleotide resolution genetic mapping in pigs by publicly accessible whole genome imputation

Nucleotide resolution genetic mapping in pigs by publicly accessible whole genome imputation

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Research progress and applications of genome‐wide association study in farm animals

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