This project is about the development of better statistical methods to dissect complex trait variation and to predict outcome from genome-wide marker data. It anticipates that individual risk prediction for disease will become an integral part of Genomic Medicine in the USA and elsewhere. To predict an individual's risk of disease from genetic data it is not necessary to have identified the causal variant or fully understand the biology - all that is needed is a predictor that is correlated with outcome. The statistically best predictor depends on the genetic architecture of the trait: the distribution of effect sizes of causal variants, the distribution of their allele frequency, and the correlation between the two. Therefore, methods to better understand the genetic architecture of complex traits will lead to better statistical prediction methods and the performance of prediction methods will lead to new inference on genetic architecture. We will develop, test and apply statistical genetic methods that utilize whole-genome genotype or sequence data from population based samples that have also been phenotyped for one or more complex traits, estimate locus-specific, chromosome-wide and whole genome matrices of genetic covariance between all pairs of individuals, and estimate variance components associated with these. We will use the results and those from large genomewide association studies to estimate the distribution of SNP and chromosome segment effects by fitting mixture models using an EM-algorithm. We will use simulation models to calibrate the observed distribution of risk allele frequencies for disease with evolutionary models that include the mode of natural selection and pleiotropic relationships in effects on fitness and disease as parameters. We will develop and test Bayesian and non-Bayesian statistical linear mixed models that utilize all available genetic data simultaneously to predict an individual's risk of disease. We will implement prediction methods using data from the Program Grant investigators, from large international research consortia and from data in the public domain, and test their efficiency by correlating outcome with predictors in independent data sets.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-GGG-M)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Washington
United States
Zip Code
Shirasaka, Y; Chaudhry, A S; McDonald, M et al. (2016) Interindividual variability of CYP2C19-catalyzed drug metabolism due to differences in gene diplotypes and cytochrome P450 oxidoreductase content. Pharmacogenomics J 16:375-87
Fohner, Alison E; Wang, Zhican; Yracheta, Joseph et al. (2016) Genetics, Diet, and Season Are Associated with Serum 25-Hydroxycholecalciferol Concentration in a Yup'ik Study Population from Southwestern Alaska. J Nutr 146:318-25
Fu, Wenqing; Browning, Sharon R; Browning, Brian L et al. (2016) Robust Inference of Identity by Descent from Exome-Sequencing Data. Am J Hum Genet 99:1106-1116
Chen, Han; Wang, Chaolong; Conomos, Matthew P et al. (2016) Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. Am J Hum Genet 98:653-66
Browning, Brian L; Browning, Sharon R (2016) Genotype Imputation with Millions of Reference Samples. Am J Hum Genet 98:116-26
Morrison, Jean; Laurie, Cathy C; Marazita, Mary L et al. (2016) Genome-wide association study of dental caries in the Hispanic Communities Health Study/Study of Latinos (HCHS/SOL). Hum Mol Genet 25:807-16
Zheng, Xiuwen; Weir, Bruce S (2016) Eigenanalysis of SNP data with an identity by descent interpretation. Theor Popul Biol 107:65-76
Gratten, Jacob; Visscher, Peter M (2016) Genetic pleiotropy in complex traits and diseases: implications for genomic medicine. Genome Med 8:78
Schick, Ursula M; Jain, Deepti; Hodonsky, Chani J et al. (2016) Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans. Am J Hum Genet 98:229-42
Zhao, Jing; Akinsanmi, Idowu; Arafat, Dalia et al. (2016) A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood. Am J Hum Genet 98:299-309

Showing the most recent 10 out of 92 publications