BACKGROUND: Population genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its "Best Practices" bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification. RESULTS: We surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK "Best Practices" are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called. CONCLUSIONS: We conclude that applying the GATK "Best Practices" pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations.

BACKGROUND: In ecology and forensics, some population assignment techniques use molecular markers to assign individuals to known groups. However, assigning individuals to known populations can be difficult if the level of genetic differentiation among populations is small. Most assignment studies handle independent markers, often by pruning markers in Linkage Disequilibrium (LD), ignoring the information contained in the correlation among markers due to LD. RESULTS: To improve the accuracy of population assignment, we present an algorithm, implemented in the HaploPOP software, that combines markers into haplotypes, without requiring independence. The algorithm is based on the Gain of Informativeness for Assignment that provides a measure to decide if a pair of markers should be combined into haplotypes, or not, in order to improve assignment. Because complete exploration of all possible solutions for constructing haplotypes is computationally prohibitive, our approach uses a greedy algorithm based on windows of fixed sizes. We evaluate the performance of HaploPOP to assign individuals to populations using a split-validation approach. We investigate both simulated SNPs data and dense genotype data from individuals from Spain and Portugal. CONCLUSIONS: Our results show that constructing haplotypes with HaploPOP can substantially reduce assignment error. The HaploPOP software is freely available as a command-line software at www.ieg.uu.se/Jakobsson/software/HaploPOP/.

SUMMARY: The program COMPASS can generate samples that have been collected at various points in time from a population that is evolving according to a Wright-Fisher model. The samples are generated using coalescence simulations permitting various demographic scenarios and the program uses an infinite sites model to generate polymorphism data for the samples. By generating serially sampled population-genetic data, COMPASS allows investigating properties of polymorphism data that has been collected at different time points, and aid in making inference from ancient polymorphism data. AVAILABILITY: The program and the manual are available at: http://www.egs.uu.se/evbiol/Research/JakobssonLab/compass.html CONTACT: [email protected].

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.

MOTIVATION: Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) 'label switching' of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) 'genuine multimodality,' truly distinct solutions across replicates. RESULTS: To facilitate the interpretation of population-genetic clustering results, we describe three algorithms for aligning multiple replicate analyses of the same data set. We have implemented these algorithms in the computer program CLUMPP (CLUster Matching and Permutation Program). We illustrate the use of CLUMPP by aligning the cluster membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breeds. AVAILABILITY: CLUMPP is freely available at http://rosenberglab.bioinformatics.med.umich.edu/clumpp.html.