Calibrating a coalescent simulation of human genome sequence variation.

By S. Schaffner, Catherine K. Foo, Stacey Gabriel, David Reich, Mark J. Daly, David Altshuler. Genome Research, 2005

672 citations recorded by the source corpus.

Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/9c048cb32d987e3cbaca577db2090882422ca0be

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Citations in the corpus

Inferring human population size and separation history from multiple genome sequences (2014, methodology): “Like PSMC it does this without requiring a simplified model with specific bottlenecks, hard population splits and fixed population sizes as do previous methods based on allele frequencies [33-35] or more general summary statistics [36-39].”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, background): “In application to dogs, we show that the boxer and basenji breeds have a considerable component of ancestry from grey wolves subsequent to domestication.”
Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. (2012, methodology): “We generated sequence data of European ancestry from 10,000 chromosomes over 1 Mb regions using the calibrated coalescent model [31].”
MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes (2010, methodology): “As previously described [Schaffner et al., 2005], we simulated 10,000 chromosomes for a series of 1Mb regions.”
Estimating and interpreting FST: The impact of rare variants (2013, background): “For example, selection can result in dif- ferences between FST estimated on genic and nongenic SNPs (Clark et al. 2005; Barreiro et al. 2008; Hernandez et al. 2011); complex demography can cause FST to vary with SNP allele frequency (Schaffner et al. 2005) (see below).”
Balancing Selection and Its Effects on Sequences in Nearby Genome Regions (2006, background): “inferring selection in human populations, despite very large studies [67,68], and it surely applies to other populations.”
Computer programs for population genetics data analysis: a survival guide (2006, background): “jpg" NDATA ITEM> ]> Recent population genetics methods can provide accurate information on the past demography of a population, which is necessary to correctly interpret patterns of linkage disequilibrium”
Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data (2009, background): “Given their many uses, it is not surprising that many studies have inferred demographic models for populations of humans and other species [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].”
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, background): “Because the accurate modelling of linkage disequilibrium is essential in disease genetics [100], recombination must be incorporated.”
Fast and flexible simulation of DNA sequence data. (2008, methodology): “We first inferred phase in the YRI data using a hidden Markov model-based algorithm implemented in the program fastPHASE (Scheet and Stephens 2006).”
Population Structure and Eigenanalysis (2006, methodology): “We thank Stephen Schaffner for sharing a powerful and flexible coalescent simulator [46] which was helpful for many of our simulations, and Mira Bernstein for catching an important error.”