Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, Victoria 3086, Australia.
BMC Genomics. 2014 Jun 6;15(1):436. doi: 10.1186/1471-2164-15-436.
In livestock, as in humans, the number of genetic variants that can be tested for association with complex quantitative traits, or used in genomic predictions, is increasing exponentially as whole genome sequencing becomes more common. The power to identify variants associated with traits, particularly those of small effects, could be increased if certain regions of the genome were known a priori to be enriched for associations. Here, we investigate whether twelve genomic annotation classes were enriched or depleted for significant associations in genome wide association studies for complex traits in beef and dairy cattle. We also describe a variance component approach to determine the proportion of genetic variance captured by each annotation class.
P-values from large GWAS using 700K SNP in both dairy and beef cattle were available for 11 and 10 traits respectively. We found significant enrichment for trait associated variants (SNP significant in the GWAS) in the missense class along with regions 5 kilobases upstream and downstream of coding genes. We found that the non-coding conserved regions (across mammals) were not enriched for trait associated variants. The results from the enrichment or depletion analysis were not in complete agreement with the results from variance component analysis, where the missense and synonymous classes gave the greatest increase in variance explained, while the upstream and downstream classes showed a more modest increase in the variance explained.
Our results indicate that functional annotations could assist in prioritization of variants to a subset more likely to be associated with complex traits; including missense variants, and upstream and downstream regions. The differences in two sets of results (GWAS enrichment depletion versus variance component approaches) might be explained by the fact that the variance component approach has greater power to capture the cumulative effect of mutations of small effect, while the enrichment or depletion approach only captures the variants that are significant in GWAS, which is restricted to a limited number of common variants of moderate effects.
在畜牧业中，与人类一样，随着全基因组测序变得越来越普遍，可用于检测与复杂数量性状相关的遗传变异或用于基因组预测的数量呈指数级增长。如果某些基因组区域预先被认为与关联富集，那么识别与性状相关的变异（尤其是那些小效应的变异）的能力可能会提高。在这里，我们研究了在牛科动物的复杂性状全基因组关联研究中，十二个基因组注释类别是否在全基因组范围内富集或缺失显著关联。我们还描述了一种方差分量方法，用于确定每个注释类别的遗传方差捕获比例。
在奶牛和肉牛中分别使用 700K SNP 进行的大型 GWAS 的 P 值可用于 11 和 10 个性状。我们发现，在错义类中，与性状相关的变异（在 GWAS 中显著的 SNP）以及编码基因上下游 5kb 区域存在显著富集。我们发现，非编码保守区域（跨哺乳动物）与性状相关的变异没有富集。富集或缺失分析的结果与方差分量分析的结果不完全一致，其中错义类和同义类对解释方差的增加最大，而上游和下游类则对解释方差的增加更为适度。
我们的结果表明，功能注释可以帮助优先考虑与复杂性状相关的变异子集，包括错义变异和上下游区域。两套结果（GWAS 富集缺失与方差分量方法）之间的差异可能是由于方差分量方法具有更大的捕获小效应突变累积效应的能力，而富集或缺失方法仅捕获 GWAS 中显著的变异，这受到有限数量的中等效应常见变异的限制。
