Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations.

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation. Here, we propose to sequence the full genomes of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on tag SNPs and to generate an internal population-specific imputation reference panel, such that the remaining array-genotyped cohort could be more accurately imputed. Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on tags to the base H3Africa array.

Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps.

It is increasingly evident that natural selection plays a prominent role in shaping patterns of diversity across the genome. The most commonly studied modes of natural selection are positive selection and negative selection, which refer to directional selection for and against derived mutations, respectively. Positive selection can result in hitchhiking events, in which a beneficial allele rapidly replaces all others in the population, creating a valley of diversity around the selected site along with characteristic skews in allele frequencies and linkage disequilibrium among linked neutral polymorphisms. Similarly, negative selection reduces variation not only at selected sites but also at linked sites, a phenomenon called background selection (BGS). Thus, discriminating between these two forces may be difficult, and one might expect efforts to detect hitchhiking to produce an excess of false positives in regions affected by BGS. Here, we examine the similarity between BGS and hitchhiking models via simulation. First, we show that BGS may somewhat resemble hitchhiking in simplistic scenarios in which a region constrained by negative selection is flanked by large stretches of unconstrained sites, echoing previous results. However, this scenario does not mirror the actual spatial arrangement of selected sites across the genome. By performing forward simulations under more realistic scenarios of BGS, modeling the locations of protein-coding and conserved noncoding DNA in real genomes, we show that the spatial patterns of variation produced by BGS rarely mimic those of hitchhiking events. Indeed, BGS is not substantially more likely than neutrality to produce false signatures of hitchhiking. This holds for simulations modeled after both humans and Drosophila, and for several different demographic histories. These results demonstrate that appropriately designed scans for hitchhiking need not consider BGS's impact on false-positive rates. However, we do find evidence that BGS increases the false-negative rate for hitchhiking, an observation that demands further investigation.

A structural variation reference for medical and population genetics.

Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage6. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings7. This SV resource is freely distributed via the gnomAD browser8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Efficiently Summarizing Relationships in Large Samples: A General Duality Between Statistics of Genealogies and Genomes.

As a genetic mutation is passed down across generations, it distinguishes those genomes that have inherited it from those that have not, providing a glimpse of the genealogical tree relating the genomes to each other at that site. Statistical summaries of genetic variation therefore also describe the underlying genealogies. We use this correspondence to define a general framework that efficiently computes single-site population genetic statistics using the succinct tree sequence encoding of genealogies and genome sequence. The general approach accumulates sample weights within the genealogical tree at each position on the genome, which are then combined using a summary function; different statistics result from different choices of weight and function. Results can be reported in three ways: by site, which corresponds to statistics calculated as usual from genome sequence; by branch, which gives the expected value of the dual site statistic under the infinite sites model of mutation, and by node, which summarizes the contribution of each ancestor to these statistics. We use the framework to implement many currently defined statistics of genome sequence (making the statistics' relationship to the underlying genealogical trees concrete and explicit), as well as the corresponding branch statistics of tree shape. We evaluate computational performance using simulated data, and show that calculating statistics from tree sequences using this general framework is several orders of magnitude more efficient than optimized matrix-based methods in terms of both run time and memory requirements. We also explore how well the duality between site and branch statistics holds in practice on trees inferred from the 1000 Genomes Project data set, and discuss ways in which deviations may encode interesting biological signals.

Testing the utility of dental morphological trait combinations for inferring human neutral genetic variation.

Researchers commonly rely on human dental morphological features in order to reconstruct genetic affinities among past individuals and populations, particularly since teeth are often the best preserved part of a human skeleton. Tooth form is considered to be highly heritable and selectively neutral and, therefore, to be an excellent proxy for DNA when none is available. However, until today, it remains poorly understood whether certain dental traits or trait combinations preserve neutral genomic signatures to a greater degree than others. Here, we address this long-standing research gap by systematically testing the utility of 27 common dental traits and >134 million possible trait combinations in reflecting neutral genomic variation in a worldwide sample of modern human populations. Our analyses reveal that not all traits are equally well-suited for reconstructing population affinities. Whereas some traits largely reflect neutral variation and therefore evolved primarily as a result of genetic drift, others can be linked to nonstochastic processes such as natural selection or hominin admixture. We also demonstrate that reconstructions of population affinity based on many traits are not necessarily more reliable than those based on only a few traits. Importantly, we find a set of highly diagnostic trait combinations that preserve neutral genetic signals best (up to [Formula: see text] r = 0.580; 95% r range = 0.293 to 0.758; P = 0.001). We propose that these trait combinations should be prioritized in future research, as they allow for more accurate inferences about past human population dynamics when using dental morphology as a proxy for DNA.

The curse of observer experience: Error in noninvasive genetic sampling.

Noninvasive genetic sampling (NGS) is commonly used to study elusive or rare species where direct observation or capture is difficult. Little attention has been paid to the potential effects of observer bias while collecting noninvasive genetic samples in the field, however. Over a period of 7 years, we examined whether different observers (n = 58) and observer experience influenced detection, amplification rates, and correct species identification of 4,836 gray wolf (Canis lupus) fecal samples collected in Idaho and Yellowstone National Park, USA and southwestern Alberta, Canada (2008-2014). We compared new observers (n = 33) to experienced observers (n = 25) and hypothesized experience level would increase the overall success of using NGS techniques in the wild. In contrast to our hypothesis, we found that new individuals were better than experienced observers at detecting and collecting wolf scats and correctly identifying wolf scats from other sympatric carnivores present in the study areas. While adequate training of new observers is crucial for the successful use of NGS techniques, attention should also be directed to experienced observers. Observer experience could be a curse because of their potential effects on NGS data quality arising from fatigue, boredom or other factors. The ultimate benefit of an observer to a project is a combination of factors (i.e., field savvy, local knowledge), but project investigators should be aware of the potential negative effects of experience on NGS sampling.

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations.

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Reconstructing the population history of the sandy beach amphipod Haustorioides japonicus using the calibration of demographic transition (CDT) approach.

Calibration of the molecular rate is one of the major challenges in marine population genetics. Although the use of an appropriate evolutionary rate is crucial in exploring population histories, calibration of the rate is always difficult because fossil records and geological events are rarely applicable for rate calibration. The acceleration of the evolutionary rate for recent coalescent events (or more simply, the time dependency of the molecular clock) is also a problem that can lead to overestimation of population parameters. Calibration of demographic transition (CDT) is a rate calibration technique that assumes a post-glacial demographic expansion, representing one of the most promising approaches for dealing with these potential problems in the rate calibration. Here, we demonstrate the importance of using an appropriate evolutionary rate, and the power of CDT, by using populations of the sandy beach amphipod Haustorioides japonicus along the Japanese coast of the northwestern Pacific Ocean. Analysis of mitochondrial sequences found that the most peripheral population in the Pacific coast of northeastern Honshu Island (Tohoku region) is genetically distinct from the other northwestern Pacific populations. By using the two-epoch demographic model and rate of temperature change, the evolutionary rate was modeled as a log-normal distribution with a median rate of 2.2%/My. The split-time of the Tohoku population was subsequently estimated to be during the previous interglacial period by using the rate distribution, which enables us to infer potential causes of the divergence between local populations along the continuous Pacific coast of Japan.

Extending Tests of Hardy-Weinberg Equilibrium to Structured Populations.

Testing for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors, or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of "structural HWE." Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.

Optimization of Population Frequency Cutoffs for Filtering Common Germline Polymorphisms from Tumor-Only Next-Generation Sequencing Data.

Clinical next-generation sequencing assays are often run on tumor specimens without a matched normal specimen, which complicates the differentiation of germline from somatic variants. In tumor-only testing, population data are often used to infer germline status, though no consensus exists on the exact population frequency (PF) cutoff above which a variant should be considered likely germline. In this study, five population databases plus the Catalog of Somatic Mutations in Cancer were used to demonstrate the impact of changing the PF cutoff on assignment of variants as germline versus somatic. The 1% to 2% PF cutoffs widely used in bioinformatic pipelines resulted in high sensitivity for classification of somatic variants, but unnecessarily reduced sensitivity for germline variants. Using optimized PF cutoffs, the source of variants in The Cancer Genome Atlas (TCGA) data could be predicted with >95% accuracy. Further exploration of four TCGA cancer data sets indicated that the optimal cutoff is influenced by both cancer type and the assay region of interest. Comparing TCGA data to data generated from a clinical, hybridization capture test (approximately 615 kb capture space) showed that PF cutoffs may not be transferable between assays, even when the gene set is held constant. Thus, filtering approaches need to be carefully designed and optimized, and should be assay-specific to support tumor-only testing until tumor-normal testing becomes routine in the clinical setting.

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction.

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV.

Testing for Hardy-Weinberg equilibrium in structured populations using genotype or low-depth next generation sequencing data.

Testing for deviations from Hardy-Weinberg equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of particular evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analysing these data set, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low-depth NGS data, as well as in genotype data, for both simulated and real data set, where the use of genotype likelihoods enables us to model the uncertainty.

Continued misuse of multiple testing correction methods in population genetics-A wake-up call?

Population geneticists often use multiple independent hypothesis tests of Hardy-Weinberg Equilibrium (HWE), Linkage Disequilibrium (LD), and population differentiation, to make broad inferences about their systems of choice. However, correcting for Family-Wise Error Rates (FWER) that are inflated due to multiple comparisons, is sparingly reported in our current literature. In this issue of Molecular Ecology Resources, perform a meta-analysis of 215 population genetics studies published between 2011 and 2013 to show (i) scarce use of FWER corrections across all three classes of tests, and (ii) when used, inconsistent application of correction methods with a clear bias towards less-conservative corrections for tests of population differentiation, than for tests of HWE, and LD. Here we replicate this meta-analysis using 205 population genetics studies published between 2013 and 2018, to show the same continued disuse, and inconsistencies. We hope that both studies serve as a wake-up call to population geneticists, reviewers, and editors to be rigorous about consistently correcting for FWER inflation.

Pedigree reconstruction from poor quality genotype data.

Marker genotype data could suffer from a high rate of errors such as false alleles and allelic dropouts (null alleles) in situations such as SNPs from low-coverage next-generation sequencing and microsatellites from noninvasive samples. Use of such data without accounting for mistyping properly could lead to inaccurate or incorrect inferences of family relationships such as parentage and sibship. This study shows that markers with a high error rate are still informative. Simply discarding them could cause a substantial loss of precious information, and is impractical in situations where virtually all markers (e.g. SNPs from low-coverage next-generation sequencing, microsatellites from noninvasive samples) suffer from a similarly high error rate. This study also shows that some previous error models are valid for markers of low error rates, but fail for markers of high error rates. It proposes an improved error model and demonstrates, using simulated and empirical data of a high error rate (say, >0.5), that it leads to more accurate sibship and parentage inferences than previous models. It suggests that, in reality, markers of high error rates should be used rather than discarded in pedigree reconstruction, so long as the error rates can be estimated and used properly in the analyses.

On the standardization of fitness and traits in comparative studies of phenotypic selection.

Comparisons of the strength and form of phenotypic selection among groups provide a powerful approach for testing adaptive hypotheses. A central and largely unaddressed issue is how fitness and phenotypes are standardized in such studies; standardization across or within groups can qualitatively change conclusions whenever mean fitness differs between groups. We briefly reviewed recent relevant literature, and found that selection studies vary widely in their scale of standardization, but few investigators motivated their rationale for chosen standardization approaches. Here, we propose that the scale at which fitness should be relativized should reflect whether selection is likely to be hard or soft; that is, the scale at which populations (or hypothetical populations in the case of a contrived experiment) are regulated. We argue that many comparative studies of selection are implicitly or explicitly focused on soft selection (i.e., frequency and density-dependent selection). In such studies, relative fitness should preferably be calculated using within-group means, although this approach is taken only occasionally. Related difficulties arise for the standardization of phenotypes. The appropriate scale at which standardization should take place depends on whether groups are considered to be fixed or random. We emphasize that the scale of standardization is a critical decision in empirical studies of selection that should always warrant explicit justification.

Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.

FlashPCA2: principal component analysis of Biobank-scale genotype datasets.

MOTIVATION: Principal component analysis (PCA) is a crucial step in quality control of genomic data and a common approach for understanding population genetic structure. With the advent of large genotyping studies involving hundreds of thousands of individuals, standard approaches are no longer feasible. However, when the full decomposition is not required, substantial computational savings can be made. RESULTS: We present FlashPCA2, a tool that can perform partial PCA on 1 million individuals faster than competing approaches, while requiring substantially less memory. AVAILABILITY AND IMPLEMENTATION: https://github.com/gabraham/flashpca . CONTACT: gad.abraham@unimelb.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic validation of the differential preservation of population history in modern human cranial anatomy.

OBJECTIVES: In modern humans, the significant correlation between neutral genetic loci and cranial anatomy suggests that the cranium preserves a population history signature. However, there is disagreement on whether certain parts of the cranium preserve this signature to a greater degree than other parts. It is also unclear how different quantitative measures of phenotype affect the association of genetic variation and anatomy. Here, we revisit these matters by testing the correlation of genetic distances and various phenotypic distances for ten modern human populations. MATERIALS AND METHODS: Geometric morphometric shape data from the crania of adult individuals (n = 224) are used to calculate phenotypic PST , Procrustes, and Mahalanobis distances. We calculate their correlation to neutral genetic distances, FST , derived from single nucleotide polymorphisms (SNPs). We subset the cranial data into landmark configurations that include the neurocranium, the face, and the temporal bone in order to evaluate whether these cranial regions are differentially correlated to neutral genetic variation. RESULTS: Our results show that PST , Mahalanobis, and Procrustes distances are correlated with FST distances to varying degrees. They indicate that overall cranial shape is significantly correlated with neutral genetic variation. Of the component parts examined, PST distances for both the temporal bone and the face have a stronger association with FST distances than the neurocranium. When controlling for population divergence time, only the whole cranium and the temporal bone have a statistically significant association with FST distances. DISCUSSION: Our results confirm that the cranium, as a whole, and the temporal bone can be used to reconstruct modern human population history.

Pitfalls of the Geographic Population Structure (GPS) Approach Applied to Human Genetic History: A Case Study of Ashkenazi Jews.

In a recent interdisciplinary study, Das et al. have attempted to trace the homeland of Ashkenazi Jews and of their historical language, Yiddish (Das et al. 2016 Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz. Genome Biol Evol. 8:1132-1149). Das et al. applied the geographic population structure (GPS) method to autosomal genotyping data and inferred geographic coordinates of populations supposedly ancestral to Ashkenazi Jews, placing them in Eastern Turkey. They argued that this unexpected genetic result goes against the widely accepted notion of Ashkenazi origin in the Levant, and speculated that Yiddish was originally a Slavic language strongly influenced by Iranian and Turkic languages, and later remodeled completely under Germanic influence. In our view, there are major conceptual problems with both the genetic and linguistic parts of the work. We argue that GPS is a provenancing tool suited to inferring the geographic region where a modern and recently unadmixed genome is most likely to arise, but is hardly suitable for admixed populations and for tracing ancestry up to 1,000 years before present, as its authors have previously claimed. Moreover, all methods of historical linguistics concur that Yiddish is a Germanic language, with no reliable evidence for Slavic, Iranian, or Turkic substrata.