New sequencing technologies and increasingly dense SNP arrays are generating a flood of genetic data. Sample sizes are increasing and the spectrum of genotyped variation is broadening to include structural and multi-allelic variants. This research will develop improved genotype calling methods that are designed for these data and that use information from large sample sizes and from related individuals in novel and powerful ways. The result will be improved genotype data accuracy which will benefit all research on the genetic determinants of health and disease.

Public Health Relevance

The genetic variants carried by an individual can increase or decrease the individual's risk of heritable diseases such as cardiovascular disease and diabetes. This research will develop new methods and software that improve our ability to identify genetic variants that increase or decrease risk of disease. This research will contribute to the prevention, diagnosis, and treatment of heritable diseases in the United States and throughout the world.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Brooks, Lisa
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University of Washington
Internal Medicine/Medicine
Schools of Medicine
United States
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Browning, Brian L; Browning, Sharon R (2016) Genotype Imputation with Millions of Reference Samples. Am J Hum Genet 98:116-26
Browning, Sharon R; Browning, Brian L (2015) Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. Am J Hum Genet 97:404-18
1000 Genomes Project Consortium; Auton, Adam; Brooks, Lisa D et al. (2015) A global reference for human genetic variation. Nature 526:68-74
Zhang, Qian S; Browning, Brian L; Browning, Sharon R (2015) Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol. Eur J Hum Genet 23:672-7
Qian, Yu; Browning, Brian L; Browning, Sharon R (2014) Efficient clustering of identity-by-descent between multiple individuals. Bioinformatics 30:915-22
Yu, Z; Li, C F; Mkhikian, H et al. (2014) Family studies of type 1 diabetes reveal additive and epistatic effects between MGAT1 and three other polymorphisms. Genes Immun 15:218-23
Shirts, Brian H; Jacobson, Angela; Jarvik, Gail P et al. (2014) Large numbers of individuals are required to classify and define risk for rare variants in known cancer risk genes. Genet Med 16:529-34
Browning, Sharon R; Browning, Brian L (2013) Identity-by-descent-based heritability analysis in the Northern Finland Birth Cohort. Hum Genet 132:129-38
de Candia, Teresa R; Lee, S Hong; Yang, Jian et al. (2013) Additive genetic variation in schizophrenia risk is shared by populations of African and European descent. Am J Hum Genet 93:463-70
Browning, Brian L; Browning, Sharon R (2013) Detecting identity by descent and estimating genotype error rates in sequence data. Am J Hum Genet 93:840-51

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