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）对我们理解复杂性状的遗传基础做出了巨大贡献。此类研究得出的一项核心结论为：绝大多数复杂性状由大量效应微弱的基因座（locus，复数形式为loci）调控，这验证了数量遗传学中的无穷小模型（infinitesimal model）。当前用于检测多基因结构的主流方法之一为所谓的“染色体分区分析（chromosome partitioning）”：即对每条染色体解释的表型方差与染色体大小进行回归分析。该方法最初针对人类开发，如今已被反复应用于其他物种，但目前尚无研究评估该方法在染色体数目、大小等基因组特征存在差异的物种中的适用性。此外，样本量、性状遗传力、调控性状的基因座效应大小分布，以及因果基因座在基因组中的物理分布等因素对该方法的影响，均未得到考察。本研究通过模拟数据证实，上述因素会对利用染色体分区分析推断性状遗传结构的结果产生显著影响。具体而言，染色体大小差异微小、样本量偏小、性状遗传力偏低、效应大小分布偏态，以及基因座成簇分布等情况，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果。未来采用该方法的研究需将上述参数纳入考量，为自身的研究体系构建恰当的零模型（null model）。本研究的模拟结果可为上述问题提供一定参考，但仍需开展覆盖更广参数空间的后续研究。