One of the most fundamental problems in genetics is the problem of identifying genetic variants that are shared identical by descent (IBD) from a recent common ancestor. Usually the concept of IBD is applied to data from families, but it can be applied to unrelated individuals because such individuals are related, even if only very distantly. This project will develop methods to detect and utilize IBD information from genome- wide data in related and unrelated individuals. These methods will make it possible to detect additional genes and variants that are involved in human disease, including common, complex diseases such as heart disease and diabetes The detected IBD information will be useful for determining regions where affected individuals in a pedigree share genetic material (linkage analysis). Current methods for linkage analysis are limited to relatively small families and to low-density panels of genetic markers. This work will enable linkage analysis to be performed on much larger families, and even on whole populations, particularly when these populations are small and isolated. This work will also enable linkage analysis to be performed with very high density genetic information, even to the level of DNA sequence data. The detected IBD will also be useful for obtaining highly accurate estimates of variants that were inherited together from each parent (the haplotype phase), and for imputing ungenotyped variation. Association studies, which correlate genotypic or haplotypic variation with disease status, do not directly use IBD information, but will have increased power due to these improved estimates obtained using IBD.
Heritable genetic variants contribute to many common diseases, such as cardiovascular disease and diabetes. This research will develop new statistical and computational methods for genetic data analysis that will improve our ability to identify genetic variants that increase risk of disease. These methods will contribute to the prevention, diagnosis, and treatment of heritable diseases in the United States and throughout the world.
|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|
|Browning, Sharon R; Browning, Brian L (2013) Erratum to: Identity-by-descent-based heritability analysis in the Northern Finland Birth Cohort. Hum Genet 132:957-8|
|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|
|Browning, Brian L; Browning, Sharon R (2013) Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194:459-71|
|Kim, Jerry H; Jarvik, Gail P; Browning, Brian L et al. (2013) Exome sequencing reveals novel rare variants in the ryanodine receptor and calcium channel genes in malignant hyperthermia families. Anesthesiology 119:1054-65|
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