Mixture between populations is a fundamental process that shapes biology, genetic variation, and the risk for disease. Despite its importance, the analytical methods that are available to study mixture on a genome-wide scale are limited. This makes it a priority to develop methods that can analyze this history in the large data sets that are now practical to generate. No federal grant currently supports the development of such methods. This grant proposes to develop methods and make software available for studying mixture.
The Aims are: (1) To develop tools that make inferences about mixture based on allele frequency and haplotype frequency differences. (2) To develop tools that estimate dates of mixture based on admixture linkage disequilibrium and genetic divergence data. We expect that this grant will be of value in three areas. (a) It will support the development of methods and user-friendly software that will be important for evolutionary and medical genetics. (b) It will support work that will result in insights relevant to finding disease genes in human populations that are recently or anciently admixed. (c) It will lead to new insights about human history as well as the history of other organisms. The connection to medical genetics is particularly important. Our laboratory's past work on the evolutionary history of populations has been intertwined with our work on disease gene mapping, and the approaches that we developed in both areas were synergistic. In particular, we have leveraged the history of admixture in human populations to make new gene discoveries and to understand variation in disease risk across populations. We expect to be able to make further connections between evolutionary and medical genetics by developing sophisticated approaches for understanding and modeling population mixture.

Public Health Relevance

The history of population mixture is of importance to public health because it determines the genetic variants that individuals and populations inherit, which in turn make some people more or less susceptible to disease. In the last decade, it has become clear that many human populations are the result of mixtures of populations with divergent ancestries: not only African Americans and Latinos, but also South Asians and all non-Africans (who have Neandertal genetic material). For example, our group has developed methods for studying population mixture and directly applied them to discover genetic risk factors for prostate in African Americans. This grant proposes to develop sophisticated methods to understand and quantify mixture in human populations using modern genomic data. We anticipate that a deeper understanding of human population mixture will assist in the development of more powerful methods to discover genetic risk factors for disease.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genetic Variation and Evolution Study Section (GVE)
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Eckstrand, Irene A
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Harvard University
Schools of Medicine
United States
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Lipson, Mark; Loh, Po-Ru; Patterson, Nick et al. (2014) Reconstructing Austronesian population history in Island Southeast Asia. Nat Commun 5:4689
Sankararaman, Sriram; Mallick, Swapan; Dannemann, Michael et al. (2014) The genomic landscape of Neanderthal ancestry in present-day humans. Nature 507:354-7
Fu, Qiaomei; Li, Heng; Moorjani, Priya et al. (2014) Genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514:445-9
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Lazaridis, Iosif; Patterson, Nick; Mittnik, Alissa et al. (2014) Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513:409-13
Pickrell, Joseph K; Reich, David (2014) Toward a new history and geography of human genes informed by ancient DNA. Trends Genet 30:377-89
Prufer, Kay; Racimo, Fernando; Patterson, Nick et al. (2014) The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505:43-9
Li, Heng (2014) Fast construction of FM-index for long sequence reads. Bioinformatics 30:3274-5
Moorjani, Priya; Patterson, Nick; Loh, Po-Ru et al. (2013) Reconstructing Roma history from genome-wide data. PLoS One 8:e58633
Lipson, Mark; Loh, Po-Ru; Levin, Alex et al. (2013) Efficient moment-based inference of admixture parameters and sources of gene flow. Mol Biol Evol 30:1788-802

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