Detecting positive selection from genome scans of linkage disequilibrium

Abstract

BackgroundThough a variety of linkage disequilibrium tests have recently been introduced to measure the signal of recent positive selection, the statistical properties of the various methods have not been directly compared. While most applications of these tests have suggested that positive selection has played an important role in recent human history, the results of these tests have varied dramatically.ResultsHere, we evaluate the performance of three statistics designed to detect incomplete selective sweeps, LRH and iHS, and ALnLH. To analyze the properties of these tests, we introduce a new computational method that can model complex population histories with migration and changing population sizes to simulate gene trees influenced by recent positive selection. We demonstrate that iHS performs substantially better than the other two statistics, with power of up to 0.74 at the 0.01 level for the variation best suited for full genome scans and a power of over 0.8 at the 0.01 level for the variation best suited for candidate gene tests. The performance of the iHS statistic was robust to complex demographic histories and variable recombination rates. Genome scans involving the other two statistics suffer from low power and high false positive rates, with false discovery rates of up to 0.96 for ALnLH. The difference in performance between iHS and ALnLH, did not result from the properties of the statistics, but instead from the different methods for mitigating the multiple comparison problem inherent in full genome scans.ConclusionsWe introduce a new method for simulating genealogies influenced by positive selection with complex demographic scenarios. In a power analysis based on this method, iHS outperformed LRH and ALnLH in detecting incomplete selective sweeps. We also show that the single-site iHS statistic is more powerful in a candidate gene test than the multi-site statistic, but that the multi-site statistic maintains a low false discovery rate with only a minor loss of power when applied to a scan of the entire genome. Our results highlight the need for careful consideration of multiple comparison problems when evaluating and interpreting the results of full genome scans for positive selection.

Genetic determinants of Tibetan high-altitude adaptation

“…Tests based on extreme differences in allele frequencies are useful for identifying single-site differences between populations, in contrast to within-population, haplotype-based sweeps that may be present and strongly detectable at intermediate allele frequencies (Voight et al 2006;Huff et al 2010). One example is the PPARA haplotype, which exhibits a significant iHS signal and is correlated with hemoglobin concentration in the Qinghai-Tibetan population (Simonson et al 2010) but has not been reported as a selection candidate in other studies.…”

“…Haplotype tests of selection, for example, are based on patterns of extended homozygosity across a genomic region rather than differences in allele frequency. The within-population integrated haplotype score [iHS (Voight et al 2006)], which was used by Simonson et al (2010) to determine EGLN1/ PHD2 and PPARA haplotype SNPs, has the greatest power to identify selective sweeps when the selected variant is present at intermediate (20-80%) frequency in a single population (Voight et al 2006;Huff et al 2010). Extreme allele frequency differences are, therefore, not necessarily expected to be observed among a subset of SNPs used to tag a region that exhibits an intermediate selective sweep signal.…”

Teaching Diversity

“…However, functional mutations (SNPs) that have occurred relatively recently in human history have much longer chunks of unchanged DNA surrounding them. Several new statistical tests have been developed to evaluate the presence of selective sweeps and their population frequency (Huff, Harpending, & Rogers, 2010;Pickrell et al, 2009;Sabeti et al, 2002;Voight, Kudaravalli, Wen, & Pritchard, 2006). These tests are particularly effective at identifying selective sweeps at moderate frequency (~50%-80%), and at high frequency (>80% to fixation) within a population (Pickrell et al, 2009).…”

Efficient Sample Tracking With OpenLabFramework

“…Some generic LIMS target specific laboratory tasks, such as sample management3910, laboratory work-flows and protocols2111213, documentation, management of lab stocks, or clinical studies14. Further solutions exist for molecular genetics and the creation of vector libraries15.…”