Natural selection in recent human evolution

项目总结 我们对人类自然选择的理解一直局限于间接的统计推断和 在远缘关系的模式生物或细胞系中的实验。我们提出了一种新的方法：识别基因座 通过挖掘现在巨大的生物医学数据集，目前影响到给定年龄的存活率(即生存能力) 可用。我们的想法是寻找在出生队列中改变频率的变异和变异集 一代又一代的人都是偶然的。目标1：我们将识别影响存活率的变异 来自大型队列研究的基因数据。我们计划检查出生过程中等位基因频率的变化 在全基因组范围内对100万个个体进行基因分型或重新测序，从&gt；200,000个基因类型开始 来自GERA和英国生物库。控制种群结构，我们将评估(I)等位基因或基因 频率随年龄的变化比预期的更大；(Ii)如果趋势因性别而不同；以及(Iii)如果 在年轻和年老的影响之间权衡的证据。我们将对单基因座进行这些测试 在整个基因组中，以及给定注释中的所有基因座(例如，假定具有破坏性的氨基酸 突变)。为了检验当前数量性状的选择压力，我们将考虑一组变种 以前与40多个数量性状相关(例如，糖尿病风险或身高)，并询问多基因 每个人的得分都会随着年龄和性别的不同而不同。目标2：我们将找出影响生存的变异 成年期或三人组数据中的传播几率。我们建议测试等位基因的不平等传递 从杂合子父母到35,000个已进行基因分型或 全基因组重新测序。该数据集提供了强大的能力来检测选择的适度影响 在生命早期(在单倍体或二倍体生命阶段)和细微的减数分裂驱动案例。我们将考虑男性 和雌性分开以及联合，在每个SNP和每个单倍型区块测试失真。我们会 也要检查对同一数量表型有贡献的几组基因座。目标3：我们将联系当前 遗传变异对长期选择压力的影响。通过扩展我们最近建立的统计模型 我们将评估一组影响特定疾病易感性的变异或 拟人化特征被丰富为积极、消极或平衡选择的标志。这一方法将 请允许我们问：(I)选择压力(如果有的话)影响了40多种数量表型中的任何一种 (Ii)目前影响发育和衰老的基因座 (在目标1和2中确定)显示平衡或净化选择的信号。在另一项分析中，我们将 研究自人类种群分裂以来，哪些数量性状对当地适应做出了贡献。这 研究将有助于识别影响发育和衰老的变异，有助于个人预后 以及对疾病遗传学的理解。此外，它将提供第一个动力良好的、全面的 看看现存人类的生存能力选择及其与长期选择压力的关系。

PROJECT SUMMARY Our understanding of natural selection in humans has been limited to indirect statistical inferences and experiments in distantly related model organisms or in cell lines. We propose a new approach: to identify loci that currently affect survival to a given age (i.e., viability), by mining the huge biomedical data sets now available. Our idea is to look for variants and sets of variants that change frequency over birth cohorts and generations more than expected by chance. Aim 1: We will identify variants that impact survival using genetic data from large cohort studies. We plan to examine changes in allele frequencies across birth cohorts, in >1 million individuals genotyped or resequenced genome-wide, starting with >200,000 genotypes from GERA and the UK Biobank. Controlling for population structure, we will assess (i) if allele or genotype frequencies change more with age than expected by chance; (ii) if the trends differ by sex; and (iii) if there is evidence for a trade-off between effects at young and old ages. We will perform these tests for single loci throughout the genome, as well as all loci in a given annotation (e.g., putatively damaging amino acid mutations). To examine current selection pressures on quantitative traits, we will consider sets of variants previously associated with over 40 quantitative traits (e.g., diabetes risk or height) and ask how the polygenic score for each varies with age and sex. Aim 2: We will identify variants that influence survival to adulthood or transmission odds in trio data. We propose to test for the unequal transmission of alleles from heterozygous parents to surviving children or young adults in >35,000 trios that have been genotyped or resequenced genome-wide. This data set provides high power to detect even moderate effects of selection acting early on in life (at haploid or diploid life stages) and subtle cases of meiotic drive. We will consider males and females separately as well as jointly, testing for distortion at each SNP and each haplotype block. We will also examine sets of loci that contribute to the same quantitative phenotype. Aim 3: We will relate current genetic variation to long-term selection pressures. By extending a statistical model that we recently developed, we will assess whether the set of variants that influences susceptibility to a given disease or anthropomorphic trait is enriched for signatures of positive, negative or balancing selection. This approach will allow us to ask: (i) Which selective pressures, if any, have influenced any of over 40 quantitative phenotypes for which we have collated mapping results; and (ii) Whether loci that currently affect development and aging (identified in Aims 1 and 2) show signals of balancing or purifying selection. In a separate analysis, we will examine which quantitative traits have contributed to local adaptation since human populations split. This research will help to identify variation that influences development and aging, aiding in individual prognosis and the understanding of disease genetics. Moreover, it will provide the first well-powered, comprehensive look at viability selection in extant humans and its relationship to long-term selective pressures.