In this application, we request continuation of MH057881, """"""""Genetic Association in Schizophrenia and Other Disorders"""""""". In our previous aims, covering the last fourteen years, we targeted the development of statistical methods for identifying genetic variants affecting liability to simple and complex disease. Specifically we have developed novel methods to control for population substructure and to fine-map risk variants, targeted methods to characterize linkage disequilibrium (LD) and use LD for association analysis, explored false discovery rate (FDR) procedures for genetic analysis, especially in the setting of high dimensional models, and developed robust methods for common variant associations with disease via genome-wide association. During the next five years, we propose to pursue methods related to association of rare variants with disease status, an area currently of keen interest to human genetics. Next Generation Sequencing (NGS) data have only recently become economically feasible to generate and application of the technology for genetic analysis of complex diseases is underway. As expected NGS data are noisy and signals for association emanating from it are often modest. Nonetheless it is reasonable to expect these data will enhance our understanding of the genetic etiology of complex diseases, but we need good tools to dissect the data. The overarching aim for this R01 is to develop novel statistical methods and evaluate existing methods to extract association signal from NGS data, with particular emphasis on data from disorders affecting mental health. Motivating this proposal we have recently shown the utility of de novo events - mutations in children not found in parents - for identification of genes involved in risk for autism. A portion of our research will build on his foundational observation and how to integrate de novo and inherited variation. Moreover, to garner additional power, we propose to develop methods to incorporate biological information, such as the nature of sequence variation (loss-of-function, missense, and silent) and gene networks, with the distribution of rare variation in subjects to identify disease genes. As has been true for our last three funding periods, our theoretical work will be guided by real data from the evolving field of human genetics. We are well positioned to move between theory and data because we have a diverse team of investigators lead by the PI (Devlin) and subcontract PI (Roeder) who have decades of experience in the statistical genetics field.

Public Health Relevance

Whether diseases are common in the population, such as major depression and heart disease, or relatively uncommon, such as psychiatric disorders, their genetic causes are often obscure. Yet determining the genetic variants underlying these diseases could be a major step toward improving the health and well being of mankind. To accomplish this goal, researchers need the right tools: our research group develops statistical tools to identify risk genes, especially those affecting mental health.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Method to Extend Research in Time (MERIT) Award (R37)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Addington, Anjene M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Schools of Medicine
United States
Zip Code
Huckins, L M; Hatzikotoulas, K; Southam, L et al. (2018) Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa. Mol Psychiatry 23:1169-1180
Wang, Daifeng; Liu, Shuang; Warrell, Jonathan et al. (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362:
Yip, Benjamin Hon Kei; Bai, Dan; Mahjani, Behrang et al. (2018) Heritable Variation, With Little or No Maternal Effect, Accounts for Recurrence Risk to Autism Spectrum Disorder in Sweden. Biol Psychiatry 83:589-597
Mitchell, A C; Javidfar, B; Pothula, V et al. (2018) MEF2C transcription factor is associated with the genetic and epigenetic risk architecture of schizophrenia and improves cognition in mice. Mol Psychiatry 23:123-132
Bryois, Julien; Garrett, Melanie E; Song, Lingyun et al. (2018) Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat Commun 9:3121
Fazio, Leonardo; Pergola, Giulio; Papalino, Marco et al. (2018) Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory. Proc Natl Acad Sci U S A 115:5582-5587
Gusev, Alexander; Mancuso, Nicholas; Won, Hyejung et al. (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50:538-548
An, Joon-Yong; Lin, Kevin; Zhu, Lingxue et al. (2018) Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362:
Gandal, Michael J; Zhang, Pan; Hadjimichael, Evi et al. (2018) Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362:
Girdhar, Kiran; Hoffman, Gabriel E; Jiang, Yan et al. (2018) Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nat Neurosci 21:1126-1136

Showing the most recent 10 out of 72 publications