Natural selection in recent human evolution

项目摘要 我们对人类自然选择的理解仅限于间接的统计推断， 在远亲模式生物或细胞系中进行实验。我们提出了一种新的方法： 目前影响到给定年龄的存活（即，可行性），通过挖掘庞大的生物医学数据集， available.我们的想法是寻找在出生队列中改变频率的变体和变体集， 几代人比预期的更偶然。目标1：我们将使用以下方法识别影响生存率的变异： 来自大型队列研究的遗传数据。我们计划检查出生时等位基因频率的变化 队列，在> 100万个个体中进行基因分型或全基因组重测序，从> 200，000个基因型开始 来自GERA和英国生物银行。控制群体结构，我们将评估（i）等位基因或基因型 频率随年龄的变化比预期的偶然性更大;（ii）如果趋势因性别而异;以及（iii）如果 年轻人和老年人之间效果权衡的证据。我们将对单个位点进行这些测试 在整个基因组中，以及给定注释中的所有基因座（例如，致腐氨基酸 突变）。为了研究当前数量性状的选择压力，我们将考虑一组变量 先前与超过40个数量性状相关（例如，糖尿病风险或身高），并询问多基因 每一项的得分因年龄和性别而异。目标2：我们将识别影响生存的变异， 三重数据中的成年或传播几率。我们建议测试等位基因的不平等传播 从杂合子父母到存活的儿童或年轻成人，在> 35，000个已进行基因分型的三胞胎中， 全基因组重测序这一数据集提供了高功率检测甚至适度的选择效果 作用于生命早期（在单倍体或二倍体生命阶段）和减数分裂驱动的微妙情况。我们会考虑男性 和女性分别以及联合，测试每个SNP和每个单倍型块的畸变。我们将 还检查了导致相同数量表型的基因座组。目标3：我们将联系当前 遗传变异的长期选择压力。通过扩展一个统计模型， 开发，我们将评估是否影响对给定疾病的易感性的变异集， 拟人特征被丰富为积极、消极或平衡选择的特征。这种方法将 允许我们问：（i）哪些选择压力，如果有的话，影响了40多个数量表型中的任何一个， 我们已经整理了映射结果;（ii）目前影响发育和衰老的基因座是否 （在目的1和2中标识）显示平衡或纯化选择的信号。在单独的分析中，我们将 研究自人类种群分裂以来，哪些数量性状有助于当地的适应。这 研究将有助于确定影响发育和衰老的变异，有助于个体预后 以及对疾病遗传学的理解。此外，它还将提供第一个功能强大、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.