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Estimating the genetic and environmental architecture of psychiatric disorders

估计精神疾病的遗传和环境结构

基本信息

批准号：
10376051
负责人：
Matthew Charles Keller
金额：
$ 60.69万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-02-04 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10376051
关键词：
Accounting Address Affect Alleles Architecture Behavioral Behavioral Genetics Bipolar Disorder Bypass Code Complex Data Data Set Development Disease Environment Environmental Risk Factor Family Family Study Funding Future Gene Frequency Genes Genetic Genetic Research Genetic Variation Genetic study Genome Genomics Goals Grant Heritability Individual Influentials Investments Lead Major Depressive Disorder Manic Measures Mental disorders Methodology Methods Minor Modeling Partner in relationship Sampling Schizophrenia Siblings Single Nucleotide Polymorphism Software Tools Source Trans-Omics for Precision Medicine Twin Multiple Birth Twin Studies Variant Work base biobank case control causal variant design gene environment interaction genetic approach genetic architecture genetic pedigree genetic variant genome-wide insight large datasets model development novel novel strategies psychiatric genomics psychogenetics rare variant risk variant tool trait whole genome

项目摘要

PROJECT SUMMARY Understanding the genetic and environmental architecture of traits has been one of the central goals of behavioral genetics over the last fifty years. Traditional approaches using twins and families have shown that most traits, including psychiatric disorders, are highly heritable. More recently, methods that estimate heritability (h2) from single nucleotide polymorphisms (SNPs) in unrelated individuals (h2SNP) have demonstrated the importance of common variants to the genetic variation underlying complex traits. In turn, the realization that common variants are responsible for substantial trait heritability has motivated continued investment in large whole-genome datasets, which have allowed the discovery of thousands of SNPs reliably associated with complex traits. In the midst of this deluge of data, however, fundamental questions about the genetic and environmental architecture of traits remain unanswered, and new methodological approaches that leverage increasingly large whole-genome datasets are needed to answer them. In this Renewal application, we build on our previous methodological work to answer four high-level questions about the genetic and environmental architecture of complex traits. First, estimates of h2SNP for psychiatric disorders remain lower than estimates of h2 from twin and family studies. How much of this “still missing” heritability is due to rare risk variants? Using methods developed during the previous period of our grant, we will provide the best estimates to date of the importance of rare versus common risk variants of schizophrenia, bipolar disorder, and major depression. Second, there appears to be substantial overlap between common risk alleles for psychiatric disorders such as schizophrenia and bipolar disorder. Do rare risk alleles overlap to the same degree, or do they tend to be disorder-specific? We will use extensions of our previously developed methods to help answer this question. Third, the availability of large whole-genome datasets is growing at an unprecedented rate. Can this data be leveraged to answer fundamental questions about the importance of genes and the environment, traditionally the domain of twin and family designs? We propose the development of methodological approaches that use measured genetic data among relatives that exist in large datasets to help answer old questions in new ways that bypass earlier limitations. Finally, it is crucial to understand factors that can bias estimates and lead to incorrect conclusions. We show how assortative mating and gene-by- environment interactions bias existing estimates of h2SNP, and we propose the development of models and software tools that mitigate these biases. By project's end, we anticipate having tools that open up new vistas to behavioral genetics research, allowing for a clearer understanding of the genetic and environmental architecture of psychiatric disorders and other complex traits. Doing so will help guide future analytic and investment decisions.

项目总结

项目成果

期刊论文数量（40）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution.

DOI：
10.1016/j.biopsych.2013.09.006
发表时间：
2014-01-01
期刊：
BIOLOGICAL PSYCHIATRY
影响因子：
10.6
作者：
Keller, Matthew C.
通讯作者：
Keller, Matthew C.

Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment.

一般认知能力与教育程度遗传位点之间关联的独立证据。

DOI：
10.1002/ajmg.b.32319
发表时间：
2015
期刊：
American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
影响因子：
0
作者：
Trampush,JoeyW;Lencz,Todd;Knowles,Emma;Davies,Gail;Guha,Saurav;Pe'er,Itsik;Liewald,DavidC;Starr,JohnM;Djurovic,Srdjan;Melle,Ingrid;Sundet,Kjetil;Christoforou,Andrea;Reinvang,Ivar;Mukherjee,Semanti;DeRosse,Pamela;Lundervold
通讯作者：
Lundervold

Explaining additional genetic variation in complex traits.

DOI：
10.1016/j.tig.2014.02.003
发表时间：
2014-04
期刊：
Trends in genetics : TIG
影响因子：
0
作者：
Robinson MR;Wray NR;Visscher PM
通讯作者：
Visscher PM

Global genetic differentiation of complex traits shaped by natural selection in humans.

DOI：
10.1038/s41467-018-04191-y
发表时间：
2018-05-14
期刊：
Nature communications
影响因子：
16.6
作者：
Guo J;Wu Y;Zhu Z;Zheng Z;Trzaskowski M;Zeng J;Robinson MR;Visscher PM;Yang J
通讯作者：
Yang J

Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits.

DOI：
10.1038/s41467-018-03371-0
发表时间：
2018-03-02
期刊：
Nature communications
影响因子：
16.6
作者：
Wu Y;Zeng J;Zhang F;Zhu Z;Qi T;Zheng Z;Lloyd-Jones LR;Marioni RE;Martin NG;Montgomery GW;Deary IJ;Wray NR;Visscher PM;McRae AF;Yang J
通讯作者：
Yang J