Data from: Inference of genetic architecture from chromosome partitioning analyses is sensitive to genome variation, sample size, heritability and effect size distribution

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called “chromosome partitioning” where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.

全基因组关联研究（Genomewide Association Studies）极大地推动了我们对复杂性状遗传基础的认知。此类研究得出的一项核心结论为：多数复杂性状由大量效应微弱的遗传位点调控，这验证了数量遗传学中的无穷小模型（infinitesimal model）。一种用于检验多基因遗传架构的常用方法，即所谓的“染色体分区分析（chromosome partitioning）”：将每条染色体所解释的表型方差对染色体长度开展回归分析。该方法最初针对人类开发，如今已在其他物种中得到广泛应用，但目前尚无研究评估该方法在染色体数目、长度等基因组特征存在差异的物种中的适用性。此外，现有研究亦未考察样本量、性状遗传力、调控性状的位点效应大小分布，以及基因组内因果位点的物理分布等因素对该方法的影响。本研究通过模拟数据证实，上述因素会显著影响通过染色体分区分析所得到的性状遗传架构推断结果。具体而言，染色体长度变异较小、样本量不足、性状遗传力偏低、位点效应大小分布偏态，以及位点成簇分布等情况，均会导致统计效力下降，进而改变染色体分区分析的推断结论。未来采用该方法的研究需结合上述参数，为其研究体系构建合适的零假设模型。本研究的模拟结果可为相关研究提供参考，但仍需开展覆盖更广参数空间的后续研究。