SUMMARY: Several popular haplotype-based statistics for identifying recent or ongoing positive selection in genomes require knowledge of haplotype phase. Here, we provide an update to selscan which implements a re-definition of these statistics for use in unphased data. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at https://github.com/szpiech/selscan, implemented in C/C++, and supported on Linux, Windows, and MacOS.

Motivation: Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X). Results: We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies. Availability and Implementation: Code is available on Github at: https://github.com/suyashss/variant_validation. Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Runs of homozygosity (ROH) are important genomic features that manifest when identical-by-descent haplotypes are inherited from parents. Their length distributions and genomic locations are informative about population history and they are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. Here, we present software implementing a model-based method ( Pemberton et al., 2012 ) for inferring ROH in genome-wide SNP datasets that incorporates population-specific parameters and a genotyping error rate as well as provides a length-based classification module to identify biologically interesting classes of ROH. Using simulations, we evaluate the performance of this method. AVAILABILITY AND IMPLEMENTATION: GARLIC is written in C ++. Source code and pre-compiled binaries (Windows, OSX and Linux) are hosted on GitHub ( https://github.com/szpiech/garlic ) under the GNU General Public License version 3. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Analysis of the distribution of alleles across populations is a useful tool for examining population diversity and relationships. However, sample sizes often differ across populations, sometimes making it difficult to assess allelic distributions across groups. RESULTS: We introduce a generalized rarefaction approach for counting alleles private to combinations of populations. Our method evaluates the number of alleles found in each of a set of populations but absent in all remaining populations, considering equal-sized subsamples from each population. Applying this method to a worldwide human microsatellite dataset, we observe a high number of alleles private to the combination of African and Oceanian populations. This result supports the possibility of a migration out of Africa into Oceania separate from the migrations responsible for the majority of the ancestry of the modern populations of Asia, and it highlights the utility of our approach to sample size correction in evaluating hypotheses about population history. AVAILABILITY: We have implemented our method in the computer pro-gram ADZE, which is available for download at http://rosenberglab.bioinformatics.med.umich.edu/adze.html.