Genetic analysis of deep phenotyping projects in common disorders

doi:10.1016/j.schres.2017.09.031

Schizophrenia Research

Volume 195, May 2018, Pages 51-57

https://doi.org/10.1016/j.schres.2017.09.031 Get rights and content

Abstract

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

Section snippets

Genetic architecture of common traits and diseases

Quantitative neurobiological traits related to common neuropsychiatric diseases have become of particular interest since the publications on Research Domain Criteria (RDoCs) (Insel et al., 2010, Insel and Cuthbert, 2009), which are expansions of the endophenotype concept that had been proposed decades earlier by Gottesman (Gottesman and Gould, 2003, Gottesman and Shields, 1973, Gottesman and Shields, 1972). Biological markers, phenotypes, and underlying neurobiological functions related to

Genome-wide association study (GWAS) of common variants in polygenic diseases or traits

Strictly speaking, the polygenic model does not include variability among the genetic loci, where some loci have more effect than others. But this is the case when genome-wide markers are applied to large disease datasets. Even though many or all genes may contribute to heritability, it is generally agreed that there are “core genes” that are the most interesting to study, such as genes whose SNPs have the largest effect sizes (Boyle et al., 2017).

It is arguable that GWAS is a fishing

Genotype and phenotype imputation

In almost every human study with deep phenotyping there will be missing observations on a considerable proportion of subjects, which limits the available number of subjects for multiple-phenotype analysis. Many imputation methods have been developed in biomedical statistics to predict the value of missing phenotypes from the existing phenotypes of an individual, based on the relationship of phenotypes to each other in the dataset. Discussion of the different methods and algorithms for

Discussion: the value of multiple phenotypes for biologic discovery

Once there is a database with uniform genotyping and genetic analysis on a large number of phenotypes, the advantages of analyzing multiple phenotypes become apparent. Phenotypes related to another phenotype of interest can boost power to detect new associations, allow measuring heritable covariance between traits, and potentially to make causal inferences between traits (Dahl et al., 2016). As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous

Role of funding sources

The National Institute of Mental Health provides unrestricted research grants, and does not have any control over the publications that reference it.

Conflicts of interest

The corresponding author and co-authors report no conflicts of interest related to submission of this proposal, with the following possible exception:

One of the co-authors is Chief Editor of Schizophrenia Research, and he has undertaken to arrange an arms-length review of the submission, which would not include him.

Acknowledgements

David Glahn provided valuable advice and discussion on this manuscript.

Grant support: ESG: NIMH MH103368, GP: NIMH MH077945, CT: NIMH MH077851, MSK: NIMH MH078113, BC: NIMH MH103366, JAS: NIMH MH077862.

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Genetic analysis of deep phenotyping projects in common disorders

Abstract

Section snippets

Genetic architecture of common traits and diseases

Genome-wide association study (GWAS) of common variants in polygenic diseases or traits

Genotype and phenotype imputation

Discussion: the value of multiple phenotypes for biologic discovery

Role of funding sources

Conflicts of interest

Acknowledgements

Cell

Biol. Psychiatry

Comput. Biol. Chem.

Cell

NeuroImage

NeuroImage

Am. J. Hum. Genet.

A map of human genome variation from population-scale sequencing

Nature

Identification of distinct psychosis biotypes using brain-based biomarkers

Am. J. Psychiatry

Integrated analyses of gene expression and genetic association studies in a founder population

Hum. Mol. Genet.

A multiple-phenotype imputation method for genetic studies

Nat. Genet.

Using the quantitative genetic threshold model for inferences between and within species

Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci.

Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants

Mol. Psychiatry

Genome sequencing identifies major causes of severe intellectual disability

Nature

Association and linkage studies of genetic marker loci in major psychiatric disorders

Psychiatr. Dev.

The endophenotype concept in psychiatry: etymology and strategic intentions

Am. J. Psychiatry

Schizophrenia Genetics: A Twin Study Vantage Point

Genetic theorizing and schizophrenia

Br. J. Psychiatry

Deviation from expected cognitive ability across psychotic disorders

Schizophr. Res.

Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

Nat. Genet.

Research domain criteria (RDoC): toward a new classification framework for research on mental disorders

Am. J. Psychiatry

Non-replication and inconsistency in the genome-wide association setting

Hum. Hered.

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Nat. Genet.

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

Hum. Genet.