Sequence-based GWAS meta-analyses for beef production traits.

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Marie-Pierre Sanchez, Thierry Tribout, Naveen K Kadri, Praveen K Chitneedi, Steffen Maak, Chris Hozé, Mekki Boussaha, Pascal Croiseau, Romain Philippe, Mirjam Spengeler, Christa Kühn, Yining Wang, Changxi Li, Graham Plastow, Hubert Pausch, Didier Boichard
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引用次数: 1

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

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基于序列的牛肉生产性状GWAS荟萃分析。
背景:将基于全基因组序列的群体内全基因组关联研究(GWAS)的结果结合到单一荟萃分析(MA)中,是识别与复杂性状相关的变异的准确而有力的方法。作为H2020 BovReg项目的一部分,我们对牛肉生产性状进行了序列水平MA。来自法国、瑞士、德国和加拿大的五个合作伙伴对来自15个纯种或杂交种群的54782只动物进行了基于序列的GWAS的汇总统计。我们使用固定效应和z评分方法,将4个生长、9个形态和15个胴体性状的汇总统计数据合并为16个MA。结果:尽管我们在每个MA中结合了显著不同的性状,但固定效应方法通常更能提供潜在因果变异的指示。与群体内GWAS相比,该方法突出了(i)更多的数量性状基因座(QTL),(ii)QTL更频繁地位于因其对生长和肉/胴体性状的影响而已知的基因组区域,(iii)QTL内较少的基因组变体,以及(iv)更频繁地定位在基因中的候选变体。MA精确定位了以前与形态和胴体性状相关的基因变体,包括MSTN、LCORL和PLAG1。我们还鉴定了数十种其他变体,这些变体位于与生长和胴体性状相关的基因中,或与可能与肉类生产相关的功能相关的基因(例如,HS6ST1、HERC2、WDR75、COL3A1、SLIT2、MED28和ANKAR)。其中一些变体与牛基因组型组织表达图谱(CattleGTEx)中报道的表达或剪接QTL重叠,因此可以调节基因表达。结论:通过鉴定与牛牛肉生产性状相关的候选基因和潜在的因果变异,MA在研究这些性状的生物学机制方面显示出巨大的潜力。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。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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