Background

Genome-wide association studies (GWAS) have demonstrated that psychopathology phenotypes are affected by many risk alleles with small effect (polygenicity). It is unclear how ubiquitously evolutionary pressures influence the genetic architecture of these traits.

Methods

We partitioned SNP heritability to assess the contribution of background (BGS) and positive selection, Neanderthal local ancestry, functional significance, and genotype networks in 75 brain-related traits (8411 ≤ N ≤ 1,131,181, mean N = 205,289). We applied binary annotations by dichotomizing each measure based on top 2%, 1%, and 0.5% of all scores genome-wide. Effect size distribution features were calculated using GENESIS. We tested the relationship between effect size distribution descriptive statistics and natural selection. In a subset of traits, we explore the inclusion of diagnostic heterogeneity (e.g., number of diagnostic combinations and total symptoms) in the tested relationship.

Results

SNP-heritability was enriched (false discovery rate q < 0.05) for loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and loss-of-function (LoF)-intolerant regions (67 phenotypes). These effects were strongest in GWAS of schizophrenia (1.90-fold BGS, 1.16-fold genic, and 1.92-fold LoF), educational attainment (1.86-fold BGS, 1.12-fold genic, and 1.79-fold LoF), and cognitive performance (2.29-fold BGS, 1.12-fold genic, and 1.79-fold LoF). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ² parameter) in 75 brain-related traits (β = 4.39 × 10⁻⁵, p = 1.43 × 10⁻⁵, model r² = 0.548). Considering the number of DSM-5 diagnostic combinations per psychiatric disorder improved model fit (σ² ~ B_Top2% × Genic × diagnostic combinations; model r² = 0.661).

Conclusions

Brain-related phenotypes with larger variance in risk locus effect sizes are associated with loci under BGS. We show exploratory results suggesting that diagnostic complexity may also contribute to the increased polygenicity of psychiatric disorders.

中文翻译：

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表征背景选择对大脑相关性状多基因性的影响

背景

全基因组关联研究 (GWAS) 表明，精神病理学表型受许多影响较小（多基因性）的风险等位基因的影响。目前尚不清楚无处不在的进化压力如何影响这些特征的遗传结构。

方法

我们对 SNP 遗传力进行了划分，以评估背景 (BGS) 和正选择、尼安德特人当地血统、功能意义和基因型网络在 75 个大脑相关性状中的贡献（8411 ≤  N  ≤ 1,131,181，平均N  = 205,289）。我们通过根据全基因组所有得分的前 2%、1% 和 0.5% 对每个度量进行二分法来应用二进制注释。使用 GENESIS 计算效应大小分布特征。我们测试了效应大小分布描述性统计与自然选择之间的关系。在特征的一个子集中，我们探索了在测试关系中包含诊断异质性（例如，诊断组合的数量和总症状）。

结果

 对于具有升高的 BGS（7 种表型）和基因（34 种表型）和功能丧失（LoF）不耐受区域（67 种表型）的基因座，SNP 遗传性得到丰富（错误发现率q < 0.05）。这些影响在精神分裂症的 GWAS（1.90 倍 BGS、1.16 倍基因和 1.92 倍 LoF）、教育程度（1.86 倍 BGS、1.12 倍基因和 1.79 倍 LoF）和认知能力（ 2.29 倍 BGS、1.12 倍基因和 1.79 倍 LoF）。BGS（前 2%）显着预测了 75 个大脑相关性状中性状相关基因座（σ ²参数）的效应大小方差（β = 4.39 × 10 ^-5，p  = 1.43 × 10 ^-5，模型r ² = 0.548）。考虑到每种精神疾病的 DSM-5 诊断组合的数量改进了模型拟合（σ ²  ~ B _Top2%  × Genic ×诊断组合；模型r ²  = 0.661）。

结论

具有较大风险位点效应大小差异的脑相关表型与 BGS 下的位点相关。我们展示的探索性结果表明，诊断复杂性也可能导致精神疾病的多基因性增加。