Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits.
Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits.
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DOI：
10.1371/journal.pgen.1003520
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发表时间：
2013-05
期刊：
影响因子：
4.5
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Zaitlen N;Kraft P;Patterson N;Pasaniuc B;Bhatia G;Pollack S;Price AL
关于复杂人类表型决定因素的重要知识可以从遗传力的估计中获得，遗传力是由遗传因素决定的群体中表型变异的比例。在这里，我们利用冰岛广泛的表型数据、长期分阶段基因型和全人群谱系数据库来检查 38,167 名个体样本中 11 种定量表型和 12 种二分表型的遗传力。以前对遗传力的大多数估计都是来自基于家庭的方法，例如双胞胎研究，这些方法可能会因上位相互作用或共享环境而出现向上偏差。我们基于近亲和远亲个体对遗传力的估计明显低于之前研究的估计。我们检查了从兄弟姐妹到堂兄弟姐妹等一系列关系中的表型相关性，发现这些相关个体中过度的表型相关性主要是由于共享环境而不是优势或上位性。我们还开发了一种新方法来联合估计狭义遗传力和基因型 SNP 解释的遗传力。与现有方法不同，这种方法允许使用来自密切和远亲个体的信息，从而减少由基因分型 SNP 解释的遗传力估计的方差，同时防止向上偏差。我们的结果表明，常见的 SNP 解释的遗传力比例比之前认为的要大，Illumina 300K 基因分型芯片上存在的 SNP 解释了本研究中检查的 23 种表型的一半以上的遗传力。大部分剩余的遗传力可能是由于标准基因分型芯片未捕获的稀有等位基因造成的。表型是基因组及其环境的功能。遗传力是由群体中遗传因素决定的表型变异的分数。目前估计遗传力的方法依赖于密切相关个体的表型相关性，并且由于上位性和共享环境的影响，可能存在向上偏差。我们开发了新方法来估计近亲和远亲个体的遗传力。通过检查不同类型相关个体（例如兄弟姐妹、同父异母兄弟姐妹和堂兄弟姐妹）之间的表型相关性，我们发现共享环境是遗传力估计值夸大的主要决定因素。对于大量表型，目前尚不清楚当前基因分型平台上包含的 SNP 可以解释多少遗传力。估计遗传力这一组成部分的现有方法在存在相关个体的情况下存在偏差。我们开发了一种方法，允许在估计由基因分型 SNP 解释的遗传力时纳入近亲和远亲个体，并用它来估计 23 种医学相关表型。这些估计可用于增加我们对功能相关变异的分布和频率的理解，从而为未来研究的设计提供信息。
Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays. Phenotype is a function of a genome and its environment. Heritability is the fraction of variation in a phenotype determined by genetic factors in a population. Current methods to estimate heritability rely on the phenotypic correlations of closely related individuals and are potentially upwardly biased, due to the impact of epistasis and shared environment. We develop new methods to estimate heritability over both closely and distantly related individuals. By examining the phenotypic correlation among different types of related individuals such as siblings, half-siblings, and first cousins, we show that shared environment is the primary determinant of inflated estimates of heritability. For a large number of phenotypes, it is not known how much of the heritability is explained by SNPs included on current genotyping platforms. Existing methods to estimate this component of heritability are biased in the presence of related individuals. We develop a method that permits the inclusion of both closely and distantly related individuals when estimating heritability explained by genotyped SNPs and use it to make estimates for 23 medically relevant phenotypes. These estimates can be used to increase our understanding of the distribution and frequency of functionally relevant variants and thereby inform the design of future studies.
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影响因子：
64.8
作者：
Deary, Ian J.;Yang, Jian;Visscher, Peter M.
通讯作者：
Visscher, Peter M.
影响因子：
4.5
作者：
Pasaniuc B;Zaitlen N;Lettre G;Chen GK;Tandon A;Kao WH;Ruczinski I;Fornage M;Siscovick DS;Zhu X;Larkin E;Lange LA;Cupples LA;Yang Q;Akylbekova EL;Musani SK;Divers J;Mychaleckyj J;Li M;Papanicolaou GJ;Millikan RC;Ambrosone CB;John EM;Bernstein L;Zheng W;Hu JJ;Ziegler RG;Nyante SJ;Bandera EV;Ingles SA;Press MF;Chanock SJ;Deming SL;Rodriguez-Gil JL;Palmer CD;Buxbaum S;Ekunwe L;Hirschhorn JN;Henderson BE;Myers S;Haiman CA;Reich D;Patterson N;Wilson JG;Price AL
通讯作者：
Price AL
影响因子：
7
作者：
Gusev, Alexander;Lowe, Jennifer K.;Pe'er, Itsik
通讯作者：
Pe'er, Itsik
稀有变体会产生整个基因组的关联。
影响因子：
9.8
作者：
Dickson SP;Wang K;Krantz I;Hakonarson H;Goldstein DB
通讯作者：
Goldstein DB
影响因子：
5.8
作者：
Murabito, JM;Yang, Q;Cupples, LA
通讯作者：
Cupples, LA