PDF(Pubmed)
Abstract：
BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.
RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).
CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.
RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).
CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.
摘要：
背景：遗传度划分方法估计不同功能类的贡献，如编码或调节变体，遗传变异。这些信息可以更好地了解复杂性状的遗传结构，包括复杂的疾病，但也可以帮助提高家畜物种基因组选择的准确性。然而,方法主要在人类基因组数据上进行了测试，而牲畜种群具有特定的特征，比如高度的亲密关系，有效种群规模小或连锁不平衡的长期水平。
结果：这里，我们使用了14762头奶牛的数据，在全基因组序列水平上估算了11,537,240个变体，为了模拟典型家畜种群的性状，并评估两种最新遗传力划分方法的准确性，GREML和贝叶斯混合模型。在单个功能类对遗传力的贡献增加的模拟中，我们观察到估计量是无偏的，但精度较低。当因果变体富含具有低（<0.05）或高（>0.20）次要等位基因频率或低（低于第1四分位数）或高（高于第3四分位数）连锁不平衡评分的变体时，有必要根据等位基因频率或LD评分将遗传变异分为多个类别，以获得无偏结果。当多个功能类对遗传力有不同的贡献时，估计量显示出更高的变异水平，并且观察到某些类别之间的混淆。此外,来自小类别的估计特别不精确。然而,估计和他们的排名仍然是关于班级贡献的信息。我们还证明了使用一次估计单个类别的贡献的方法，一种常用的方法，导致高估。最后,我们将这些方法应用于肌肉发育和身高的表型，并估计，平均而言,开放染色质区域的变异对遗传变异有更高的贡献(>45%)，而编码区的变体具有最强的个体效应(平均富集>25倍)。相反，基因间或内含子区域的变异体显示出较低水平的富集（平均0.2和0.6倍，分别)。
结论：在牲畜种群中，应谨慎使用遗传度划分方法，特别是小类。一次只适合一个功能类别的两部分方法会导致估计器的偏差，不应使用。
结果：这里，我们使用了14762头奶牛的数据，在全基因组序列水平上估算了11,537,240个变体，为了模拟典型家畜种群的性状，并评估两种最新遗传力划分方法的准确性，GREML和贝叶斯混合模型。在单个功能类对遗传力的贡献增加的模拟中，我们观察到估计量是无偏的，但精度较低。当因果变体富含具有低（<0.05）或高（>0.20）次要等位基因频率或低（低于第1四分位数）或高（高于第3四分位数）连锁不平衡评分的变体时，有必要根据等位基因频率或LD评分将遗传变异分为多个类别，以获得无偏结果。当多个功能类对遗传力有不同的贡献时，估计量显示出更高的变异水平，并且观察到某些类别之间的混淆。此外,来自小类别的估计特别不精确。然而,估计和他们的排名仍然是关于班级贡献的信息。我们还证明了使用一次估计单个类别的贡献的方法，一种常用的方法，导致高估。最后,我们将这些方法应用于肌肉发育和身高的表型，并估计，平均而言,开放染色质区域的变异对遗传变异有更高的贡献(>45%)，而编码区的变体具有最强的个体效应(平均富集>25倍)。相反，基因间或内含子区域的变异体显示出较低水平的富集（平均0.2和0.6倍，分别)。
结论：在牲畜种群中，应谨慎使用遗传度划分方法，特别是小类。一次只适合一个功能类别的两部分方法会导致估计器的偏差，不应使用。
