Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland.
Genet Sel Evol. 2023 Oct 12;55(1):70. doi: 10.1186/s12711-023-00848-5.
Combining the results of within-population genome-wide association studies (GWAS) based on whole-genome sequences into a single meta-analysis (MA) is an accurate and powerful method for identifying variants associated with complex traits. As part of the H2020 BovReg project, we performed sequence-level MA for beef production traits. Five partners from France, Switzerland, Germany, and Canada contributed summary statistics from sequence-based GWAS conducted with 54,782 animals from 15 purebred or crossbred populations. We combined the summary statistics for four growth, nine morphology, and 15 carcass traits into 16 MA, using both fixed effects and z-score methods.
The fixed-effects method was generally more informative to provide indication on potentially causal variants, although we combined substantially different traits in each MA. In comparison with within-population GWAS, this approach highlighted (i) a larger number of quantitative trait loci (QTL), (ii) QTL more frequently located in genomic regions known for their effects on growth and meat/carcass traits, (iii) a smaller number of genomic variants within the QTL, and (iv) candidate variants that were more frequently located in genes. MA pinpointed variants in genes, including MSTN, LCORL, and PLAG1 that have been previously associated with morphology and carcass traits. We also identified dozens of other variants located in genes associated with growth and carcass traits, or with a function that may be related to meat production (e.g., HS6ST1, HERC2, WDR75, COL3A1, SLIT2, MED28, and ANKAR). Some of these variants overlapped with expression or splicing QTL reported in the cattle Genotype-Tissue Expression atlas (CattleGTEx) and could therefore regulate gene expression.
By identifying candidate genes and potential causal variants associated with beef production traits in cattle, MA demonstrates great potential for investigating the biological mechanisms underlying these traits. As a complement to within-population GWAS, this approach can provide deeper insights into the genetic architecture of complex traits in beef cattle.
将基于全基因组序列的群体内全基因组关联研究（GWAS）的结果结合到单个荟萃分析（MA）中，是识别与复杂性状相关的变异的一种准确而强大的方法。作为 H2020 BovReg 项目的一部分，我们针对牛肉生产性状进行了基于序列的 MA。来自法国、瑞士、德国和加拿大的五家合作伙伴提供了来自 15 个纯种或杂交群体的 54782 头动物进行基于序列的 GWAS 的汇总统计数据。我们将四个生长性状、九个形态性状和 15 个胴体性状的汇总统计数据合并到 16 个 MA 中，使用固定效应和 z 分数方法。
固定效应方法通常更具信息性，能够提供潜在因果变异的指示，尽管我们在每个 MA 中合并了大量不同的性状。与群体内 GWAS 相比，这种方法突出了（i）更多的数量性状位点（QTL），（ii）更频繁地位于已知对生长和肉/胴体性状有影响的基因组区域中的 QTL，（iii）QTL 内的基因组变异较少，以及（iv）更频繁地位于基因中的候选变异。MA 确定了与形态和胴体性状相关的基因中的变体，包括 MSTN、LCORL 和 PLAG1。我们还鉴定了数十个其他位于与生长和胴体性状相关的基因或与肉生产相关的功能（例如 HS6ST1、HERC2、WDR75、COL3A1、SLIT2、MED28 和 ANKAR）的基因中的变体。其中一些变体与牛基因组织表达图谱（CattleGTEx）中报道的表达或剪接 QTL 重叠，因此可以调节基因表达。
通过鉴定与牛肉生产性状相关的候选基因和潜在因果变异，MA 证明了在牛中研究这些性状的生物学机制具有巨大潜力。作为群体内 GWAS 的补充，这种方法可以更深入地了解肉牛复杂性状的遗传结构。
