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.

Public Health Relevance

This project will develop methodological tools that use whole-genome data to gain a much clearer understanding of the genetic and environmental architecture of complex traits. We will use approaches developed in our previously funded grant to estimate the importance of rare versus common risk variants for the genetic variance within, and genetic covariation between, three psychiatric disorders. Furthermore, we will develop methods that leverage relatedness that exists in large whole-genome datasets to estimate the importance of genetic and environmental factors to complex trait variation.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH100141-06
Application #
9595771
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Addington, Anjene M
Project Start
2013-02-04
Project End
2023-03-31
Budget Start
2018-07-05
Budget End
2019-03-31
Support Year
6
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Colorado at Boulder
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
007431505
City
Boulder
State
CO
Country
United States
Zip Code
80303
Xue, Angli; Wu, Yang; Zhu, Zhihong et al. (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:2941
Johnson, Emma C; Evans, Luke M; Keller, Matthew C (2018) Relationships between estimated autozygosity and complex traits in the UK Biobank. PLoS Genet 14:e1007556
Wu, Yang; Zeng, Jian; Zhang, Futao et al. (2018) Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918
Evans, Luke M; Tahmasbi, Rasool; Vrieze, Scott I et al. (2018) Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nat Genet 50:737-745
Evans, Luke M; Tahmasbi, Rasool; Jones, Matt et al. (2018) Narrow-sense heritability estimation of complex traits using identity-by-descent information. Heredity (Edinb) 121:616-630
Wills, Amanda G; Evans, Luke M; Hopfer, Christian (2017) Phenotypic and Genetic Relationship Between BMI and Drinking in a Sample of UK Adults. Behav Genet 47:290-297
Visscher, Peter M; Wray, Naomi R; Zhang, Qian et al. (2017) 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet 101:5-22
Border, Richard; Keller, Matthew C (2017) Commentary: Fundamental problems with candidate gene-by-environment interaction studies - reflections on Moore and Thoemmes (2016). J Child Psychol Psychiatry 58:328-330
Johnson, Emma C; Border, Richard; Melroy-Greif, Whitney E et al. (2017) No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes. Biol Psychiatry 82:702-708
Tahmasbi, Rasool; Keller, Matthew C (2017) GeneEvolve: a fast and memory efficient forward-time simulator of realistic whole-genome sequence and SNP data. Bioinformatics 33:294-296

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