We propose a novel approximate-likelihood method to fit demographic models to human genomewide single-nucleotide polymorphism (SNP) data. We divide the genome into windows of constant genetic map width and then tabulate the number of distinct haplotypes and the frequency of the most common haplotype for each window.

Although high-throughput genotyping arrays have made whole-genome association studies (WGAS) feasible, only a small proportion of SNPs in the human genome are actually surveyed in such studies. In addition, various SNP arrays assay different sets of SNPs, which leads to challenges in comparing results and merging data for meta-analyses. Genome-wide imputation of untyped markers allows us to address these issues in a direct fashion.

Thank parasites for making our interleukins into the inflammatory defenders they are today, according to a population genetics study by Fumagalli et al. However, you might also blame the bugs for sculpting some of those genes into risk factors for intestinal disorders. Parasite-driven selection leaves a footprint on host genomes in the form of single-nucleotide polymorphisms (SNPs). Genetic variation (multiple SNPs) at a particular locus can be maintained within a population if a certain SNP helps the host fend off infections in one environment, but hinders the host in another environment with different parasitic pressures. Here, Fumagalli et al. sift through 1,052 SNPs in human interleukin genes from roughly 1,000 people worldwide. Of 91 genes assessed, 44 bore signatures of selection, meaning that the genetic variation was due neither to chance nor to the migration of populations over time. And some of that variation correlated with parasitic diversity, indicating that parasites drove selection. Parasitic worms appear to have applied a more powerful selective pressure on certain interleukin genes than did viruses, bacteria, or fungi (assuming that pathogen diversity has remained relatively stable over time). That isn’t surprising, says senior author Manuela Sironi, because worms typically evolve slower than bacteria or viruses, giving their hosts time to adapt. Worm-driven selection of SNPs in genes encoding IL-10 and IL-4 might have been predicted based on their known roles in promoting the Th2 responses needed to fight off worm infections. Without the IL-4 receptor, for example, mice cannot expel certain nematodes. SNPs in the gene encoding IL-19 correlated strongly with worm diversity as well. Because this cytokine promotes inflammation in the skin, the authors suggest that it might protect against skin-borne infections. Surprisingly, six of the nine known risk alleles for Crohn’s and celiac disease also appeared to be selected for by pathogen diversity. Like most of these disease-associated SNPs, those in the Crohn’s risk gene IL12B correlated more closely with viral, bacterial, and fungal diversity than with worm diversity. In theory, these risky alleles have been maintained because they promote vigorous Th1 responses, which help fend off viruses and bacteria. But overly exuberant Th1 responses also contribute to inflammatory bowel diseases. Other pathogen-selected SNPs were more puzzling. For example, SNPs in the gene encoding an IL receptor-associate protein, IL1RAPL1, correlated with worm diversity, yet the protein functions in brain development and has no reported role in the immune response.

This article enhances the CLR test for detecting selective sweeps in snp data, published in http://www.ncbi.nlm.nih.gov/pubmed/16251466?ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum The CLR test is very strong against assumptions on population genetics models and different recombination rates. It is also more effective than EHH to detect complete sweeps (snps fixed in a population, with frequency of 1.0), while EHH are best to detect partial sweeps.

In this paper the authors present two methods to identify selection and selective sweeps on genopype data (CLR test). The CLR test doesn't make assumptions on any population genetics model, i.e. it doesn't assume a costant population size or costant recombination rate. In fact, it has been proven to be the one of the most robust test against these factors (Williamson et al 2007) Another keyword for this test is sliding windows. In the simplest implementation, the test calculate the mean allele frequency for a small number of neightbour snps, and compares it with all the windows of the same size of the genome. In a more complex implementation, the test tries to identify windows which resemble a selective sweep (...)

A major goal in human genetics is to understand the role of common genetic variants in susceptibility to common diseases. This will require characterizing the nature of gene variation in human populations, assembling an extensive catalogue of single-nucleotide polymorphisms (SNPs) in candidate genes and performing association studies for particular diseases. At present, our knowledge of human gene variation remains rudimentary. Here we describe a systematic survey of SNPs in the coding regions of human genes. We identified SNPs in 106 genes relevant to cardiovascular disease, endocrinology and neuropsychiatry by screening an average of 114 independent alleles using 2 independent screening methods. To ensure high accuracy, all reported SNPs were confirmed by DNA sequencing. We identified 560 SNPs, including 392 coding-region SNPs (cSNPs) divided roughly equally between those causing synonymous and non-synonymous changes. We observed different rates of polymorphism among classes of sites within genes (non-coding, degenerate and non-degenerate) as well as between genes. The cSNPs most likely to influence disease, those that alter the amino acid sequence of the encoded protein, are found at a lower rate and with lower allele frequencies than silent substitutions. This likely reflects selection acting against deleterious alleles during human evolution. The lower allele frequency of missense cSNPs has implications for the compilation of a comprehensive catalogue, as well as for the subsequent application to disease association.