The proposed research will be on mathematical and statistical models that can be used for the analysis and interpretation of genetic data from human and other populations. The overall goal is to develop reliable and computationally efficient methods of data analysis that will reveal what forces determine existing patterns of genetic variation. Emphasis in this proposal is on patterns of geographic variation in the frequencies of alleles at a single locus and to sets of alleles (haplotypes) at closely linked loci. The geographic distribution of alleles can reveal contemporary and past patterns of dispersal and the strength of association between alleles at closely linked loci can reveal past episodes of population growth, population fission and population fusion and indicate the kind of natural selection that has been affecting a genomic region. The proposed research will be concerned both with alleles that have no effect on the fitness of an organism and with alleles known or suspected to have fitness effects. The research program will rely on both analysis and computer simulation methods. Computer programs developed as part of the research program will be available from the Principal Investigator's web site. The proposed research has several practical goals. It will facilitate the mapping of loci with alleles affecting the risk of inherited diseases in humans and indicate whether those alleles have been affected by natural selection. (2) It will reveal the extent to which natural selection affecting loci in the major histocompatibility complex (MHC), which play an important role in the immune response in humans, varies among populations. (3) It will indicate the extent to which long term conservation of genomic regions (between rodents and humans) can predict patterns of DNA sequence variation within and between humans and chimpanzees, thereby helping with the choice of candidate loci underlying human genetic diseases and complex phenotypic traits. (4) It will indicate how patterns of genetic variation in humans reflect recent and ancient population growth and movement.

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 :
Slatkin, Montgomery; Racimo, Fernando (2016) Ancient DNA and human history. Proc Natl Acad Sci U S A 113:6380-7
Racimo, Fernando; Renaud, Gabriel; Slatkin, Montgomery (2016) Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans. PLoS Genet 12:e1005972
Slatkin, Montgomery (2016) Statistical methods for analyzing ancient DNA from hominins. Curr Opin Genet Dev 41:72-76
Racimo, Fernando (2016) Testing for Ancient Selection Using Cross-population Allele Frequency Differentiation. Genetics 202:733-50
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
Rogers, Rebekah L (2015) Chromosomal Rearrangements as Barriers to Genetic Homogenization between Archaic and Modern Humans. Mol Biol Evol 32:3064-78
Racimo, Fernando; Sankararaman, Sriram; Nielsen, Rasmus et al. (2015) Evidence for archaic adaptive introgression in humans. Nat Rev Genet 16:359-71

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