Genomic scans for selective sweeps using SNP data.

By Rasmus Nielsen, S. Williamson, Yuseob Kim, Melissa J. Hubisz, Andrew G. Clark, Carlos D. Bustamante. Genome Research, 2005

1,022 citations recorded by the source corpus.

Detecting selective sweeps from genomic SNP data is complicated by the intricate ascertainment schemes used to discover SNPs, and by the confounding influence of the underlying complex demographics and varying mutation and recombination rates. Current methods for detecting selective sweeps have little or no robustness to the demographic assumptions and varying recombination rates, and provide no method for correcting for ascertainment biases. Here, we present several new tests aimed at detecting selective sweeps from genomic SNP data. Using extensive simulations, we show that a new parametric test, based on composite likelihood, has a high power to detect selective sweeps and is surprisingly robust to assumptions regarding recombination rates and demography (i.e., has low Type I error). Our new test also provides estimates of the location of the selective sweep(s) and the magnitude of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence for selective sweeps is also found in many other regions, including genes known to be associated with disease risk such as DPP10 and COL4A3.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dc4e5438da0790d51b6fa234c49c166defab8cc9

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Citations in the corpus

Signals of recent positive selection in a worldwide sample of human populations. (2009, methodology): “(2006), XP-EHH as inSabeti et al. (2007), and CLR as in Nielsen et al. (2005). iHS was calculated on all SNPs with a minor allele frequency of at least 5%.”
Natural variations and genome-wide association studies in crop plants. (2014, background): “, simple diversity ratio, cross-population composite likelihood ratio, and cross-population extended haplotype homozygosity) (13, 79).”
More grist for the mill? Species delimitation in the genomic era and its implications for conservation (2019, methodology): “For example, Zhou et al. (2018) identified evidence of selective sweeps in a number of genomic regions across the porpoise genome using a method that looks for distinctive patterns of allele frequencies along a chromosome (Nielsen et al. 2005).”
SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes (2013, methodology): “Nielsen et al. (2005) introduced two major modifications to the CLR method by Kim and Stephan (2002) for detecting selective sweeps in whole-genome data.”
Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits (2008, background): “, 2005), or because individual regions of the genome show allele frequency distributions that differ from global, genome-wide allele frequency distributions (Nielsen et al., 2005).”
Approximate Bayesian Computation in Evolution and Ecology (2010, background): “A variety of analytical approximations have been developed here (Li & Stephens 2003, Nielsen 2000) and are increasingly applied to large-scale genomic data from humans (Hellenthal et al. 2008, Nielsen et al. 2005).”
Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags (2010, neutral): “natural populations [14–20].”
Detecting loci under selection in a hierarchically structured population (2009, neutral): “Several procedures have been proposed to detect loci under selection based on the patterns of genetic diversity found in a population (Tajima, 1989; Fu, 1997; Fay and Wu, 2000; Kim and Stephan, 2002; Sabeti et al., 2002; Nielsen et al., 2005; Voight et al., 2006; Zeng et al., 2006).”
PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R (2014, background): “…diversity (Hudson, Boos et al. 1992); (Nei 1979); see " Neutrality statistics " for a list of calculated Theta values Selective sweeps CL, CLR (Nielsen et al. 2005) FST estimates G ST (Nei 1973); F ST (Hudson, Slatkin et al. 1992); G ST , H ST , K ST (Hudson, Boos et al. 1992); S nn (Hudson…”
Computer programs for population genetics data analysis: a survival guide (2006, neutral): “For instance, the use of non-random SNPs can lead to biased demographic parameters or alter tests of selective sweep”
Robust Demographic Inference from Genomic and SNP Data (2013, methodology): “Demography is indeed often estimated on a set of markers and the best neutral model is used as a null for evidencing markers under selection [3,4] or for finding global patterns of selection across the genome [e.g. 5].”
Supervised Machine Learning for Population Genetics: A New Paradigm (2018, background): “…is able to detect sweeps under challenging demographic scenarios, often with no loss in power even when the demographic history is grossly mis-specified during training (e.g., if there is an unknown population bottleneck), a scenario which catastrophically compromises many other methods [17,87].”