New methods of DNA sequencing have produced a great abundance of DNA sequences from humans and many other species, including species now extinct. The proposed research will be intended to help analyze and interpret DNA sequence data both for understanding basic genetic processes such as natural selection and recombination and for understanding the history of humans and the genetic basis of inherited diseases. The proposed research is in four areas: (i) the analysis of DNA sequence data from Neanderthal bones for the purpose of understanding the relationship between Neanderthals and modern humans and for understanding the recent history of modern humans since their separation from Neanderthals, (ii) the prediction of patterns in gene frequencies in populations that have undergone a recent range expansion, with particular emphasis on past range expansions of modern humans populations in Europe, Asia, and the Americas, (iii) the analysis of DNA sequences of bacterial species obtained in samples from extreme environments, for the purpose of quantifying the extent of genetic recombination and differentiation between different strains, (iv) modeling the genetic basis of complex inherited diseases such as schizophrenia, many cancers, and type 1 and 2 diabetes, for the purpose of quantifying the interactions among genes that create higher risk and for devising new methods for identifying genes associated with higher risk. Although the goals of the four areas are somewhat different, similar mathematical and statistical methods will be used in each. Models of population genetic forces, including natural selection, recombination, genetic drift, and migration will be developed and tested against available data. Computer simulations of those models will be used to generate hypothetical data sets for testing proposed statistical methods.

Public Health Relevance

Diseases, such as cancer, heart disease, and diabetes, that are partly inherited yet are not caused by defects in single genes, are the leading cause of mortality in the US and other developed countries. The proposed research will develop mathematical and statistical tools that will help with understanding the genetic basis of such diseases, both by improving the understanding of the history of modern humans and by finding new ways to identify genes causing a higher risk of such diseases.

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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University of California Berkeley
Schools of Arts and Sciences
United States
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Theunert, Christoph; Racimo, Fernando; Slatkin, Montgomery (2017) Joint Estimation of Relatedness Coefficients and Allele Frequencies from Ancient Samples. Genetics 206:1025-1035
Theunert, Christoph; Slatkin, Montgomery (2017) Distinguishing recent admixture from ancestral population structure. Genome Biol Evol :
Schraiber, Joshua G; Evans, Steven N; Slatkin, Montgomery (2016) Bayesian Inference of Natural Selection from Allele Frequency Time Series. Genetics 203:493-511
Duforet-Frebourg, Nicolas; Slatkin, Montgomery (2016) Isolation-by-distance-and-time in a stepping-stone model. Theor Popul Biol 108:24-35
Slatkin, Montgomery; Racimo, Fernando (2016) Ancient DNA and human history. Proc Natl Acad Sci U S A 113:6380-7
Slatkin, Montgomery (2016) Statistical methods for analyzing ancient DNA from hominins. Curr Opin Genet Dev 41:72-76
Racimo, Fernando; Renaud, Gabriel; Slatkin, Montgomery (2016) Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans. PLoS Genet 12:e1005972
Racimo, Fernando (2016) Testing for Ancient Selection Using Cross-population Allele Frequency Differentiation. Genetics 202:733-50
Racimo, Fernando; Sankararaman, Sriram; Nielsen, Rasmus et al. (2015) Evidence for archaic adaptive introgression in humans. Nat Rev Genet 16:359-71
Rogers, Rebekah L (2015) Chromosomal Rearrangements as Barriers to Genetic Homogenization between Archaic and Modern Humans. Mol Biol Evol 32:3064-78

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