Data from: Partitioning of genetic variation across the genome using multimarker methods in a wild bird population

The underlying basis of genetic variation in quantitative traits, in terms of the number of causal variants and the size of their effects, is largely unknown in natural populations. The expectation is that complex quantitative trait variation is attributable to many, possibly interacting, causal variants, whose effects may depend upon the sex, age and the environment in which they are expressed. A recently developed methodology in animal breeding derives a value of relatedness among individuals from high-density genomic marker data, to estimate additive genetic variance within livestock populations. Here, we adapt and test the effectiveness of these methods to partition genetic variation for complex traits across genomic regions within ecological study populations where individuals have varying degrees of relatedness. We then apply this approach for the first time to a natural population and demonstrate that genetic variation in wing length in the great tit (Parus major) reflects contributions from multiple genomic regions. We show that a polygenic additive mode of gene action best describes the patterns observed, and we find no evidence of dosage compensation for the sex chromosome. Our results suggest that most of the genomic regions that influence wing length have the same effects in both sexes. We found a limited amount of genetic variance in males that is attributed to regions that have no effects in females, which could facilitate the sexual dimorphism observed for this trait. Although this exploratory work focuses on one complex trait, the methodology is generally applicable to any trait for any laboratory or wild population, paving the way for investigating sex-, age- and environment-specific genetic effects and thus the underlying genetic architecture of phenotype in biological study systems.

自然种群中，数量性状遗传变异的根本基础——即因果变异（causal variants）的数量及其效应大小——在很大程度上仍未被阐明。学界普遍认为，复杂数量性状的变异源自众多可能存在互作的因果变异，其效应会因性别、年龄及表达环境的不同而产生差异。动物育种领域新近开发的一种方法，可通过高密度基因组标记数据计算个体间的亲缘关系值，以此估算畜禽种群内的加性遗传方差。本研究对该方法进行了适配调整，并检验其在个体亲缘关系程度各异的生态研究种群中划分复杂性状基因组区域遗传变异的有效性。我们首次将此方法应用于自然种群，结果证实大山雀（Parus major）的翅长遗传变异来自多个基因组区域的共同贡献。研究表明，多基因加性作用模式最契合本次观测到的遗传模式，且未发现性染色体存在剂量补偿效应的相关证据。我们的结果显示，绝大多数影响翅长的基因组区域在雌雄个体间效应一致；同时还发现雄性个体中存在少量遗传方差来自雌性中无效应的基因组区域，这或可解释该性状所观测到的两性异形现象。尽管此项探索性研究仅聚焦于单一复杂性状，但该方法可普遍适用于任意实验室或野生种群的任意性状，为探究性别、年龄及环境特异性遗传效应，进而解析生物研究体系中表型的潜在遗传架构铺平了道路。