The goal of this project is to identify genes that influence variation in brain structure and function using high- density genome-wide association (GWA) analysis. The ultimate promise of this research is the discovery of genes that predispose to brain disorders and mental illnesses. Our focus is on the genetic analysis of variation in brain structure and function in randomly sampled extended pedigrees to provide significant clues regarding the specific genes that are involved in both normal and pathological brain function. In 2006, we began collecting brain-related endophenotypes on related Mexican American individuals for linkage-based analyses (MH078111 &MH078143). However, given the number of recent successes using GWA, we believe that shifting our design to exploit the availability of high density SNPs will dramatically speed gene discovery by substantially reducing the genomic region of interest nominated in our linkage-based study. Using alternative funding, we have begun this process of high-density genotyping. Because of power issues due to multiple testing inherent in GWA, it is necessary to expand our original sample to obtain sufficient power for gene identification. By adding 500 new individuals from the same large pedigrees and completing the high-density genotyping in the original sample (n=1,000), we will have 80 percent power to detect relatively small genetic effects on brain-related endophenotypes.
Our specific aims for this independent R01 are to: 1) extend our existing study by performing high quality brain magnetic resonance imaging and neuropsychological examinations on an additional 500 Mexican Americans who are members of 30 previously studied extended families, 2) perform GWA analysis to prioritize potential genes involved in brain structure/function, using 1 million SNPs genotyped on all 1,500 individuals, 3) increase our genome-wide transcriptional profile data by performing identical assays on the additional 500 samples to identify genes whose lymphocyte-derived expression levels correlate with measures of brain structure/function in the total sample, 4) identify the most likely functional variations within the five best empirically nominated candidate genes by resequencing 192 founder individuals, and 5) confirm the strongest association in an independent data set. Combining these new samples with those currently being collected represents the most cost effective and rapid approach for the discovery of genes associated with brain-related traits. The co-principal investigators on this single application include Dr. David Glahn, University of Texas HSC at San Antonio, and Dr. John Blangero, Southwest Foundation for Biomedical Research. If funded, our data and biomaterials will be incorporated into the NIMH Human Genetics Initiative, making them available to qualified researchers in the wider scientific community.

Public Health Relevance

Brain-related mental diseases are a major public health burden whose biology is still largely unknown. By identifying genes involved in brain function and structure, we will provide novel biological candidates for the determinants of such diseases and thus improve potential for intervention. The use of genome-wide association methods should significantly speed gene discovery.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Application #
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Senthil, Geetha
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Yale University
Schools of Medicine
New Haven
United States
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Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum Brain Mapp 39:4893-4902
Knowles, Emma E M; Curran, Joanne E; Meikle, Peter J et al. (2018) Disentangling the genetic overlap between cholesterol and suicide risk. Neuropsychopharmacology 43:2556-2563
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Kulkarni, Hemant; Mamtani, Manju; Wong, Gerard et al. (2017) Genetic correlation of the plasma lipidome with type 2 diabetes, prediabetes and insulin resistance in Mexican American families. BMC Genet 18:48
Mamtani, Manju; Kulkarni, Hemant; Wong, Gerard et al. (2016) Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts. Lipids Health Dis 15:67

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