ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland.
ETH Zürich, Tannenstrasse 1, 8092, Zürich, Switzerland.
BMC Genomics. 2023 Apr 10;24(1):192. doi: 10.1186/s12864-023-09295-4.
BACKGROUND: Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). RESULTS: We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. CONCLUSIONS: Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
背景：复杂性状之间的遗传相关性表明，多效变异对性状变异有贡献。全基因组关联研究（GWAS）旨在揭示性状的遗传基础。多变量关联检验和单性状 GWAS 汇总统计数据的荟萃分析能够检测与多种表型相关的变异。在这项研究中，我们使用基于阵列的基因型和 24 个繁殖、生产和 conformation 性状的表型来探索这两种方法之间的差异，并使用已推断的序列变异基因型对六个数量性状位点（QTL）进行精细定位。
结果：我们考虑了瑞士大白猪 5753 头猪的 44733 个 SNP 的基因型，这些猪的 24 个性状都有去回归的育种值。单性状关联分析揭示了 11 个影响 15 个性状的 QTL。多性状关联检验和单性状 GWAS 的荟萃分析分别在三个性状组中揭示了 3 到 6 个 QTL。多性状方法揭示了三个在单性状 GWAS 中未检测到的位点。在单性状 GWAS 中鉴定出的四个 QTL 在多性状分析中未被检测到。为了确定 QTL 的候选因果变异，我们使用包含 421 头猪的测序参考面板将基于阵列的基因型推断到序列水平。这种方法提供了 1600 万个推断的序列变异的基因型，平均推断准确性为 0.94。使用推断的序列变异基因型对六个 QTL 进行精细定位，揭示了前四个建议的因果突变。
结论：在一个中等大小的猪群中，我们的发现表明，多变量关联检验和单性状 GWAS 汇总统计数据的荟萃分析提供了非常相似的结果。虽然多性状关联方法为在作图群体中分离的多效位点提供了有用的概述，但仍建议对单性状关联研究进行调查，因为多性状方法可能会错过在单性状 GWAS 中发现的 QTL。
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