Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.     

Asymptotic variances of QTL estimators with selective DNA pooling

Investigation on QTL-marker linkage usually requires a great number of observed recombinations, inferred from combined analysis of phenotypes and genotypes. To avoid costly individual genotyping, inferences on QTL position and effects can instead make use of marker allele frequencies. DNA pooling of selected samples makes allele frequency estimation feasible for studies involving large sample sizes. Linkage studies in outbred populations have traditionally exploited half-sib family designs; within the animal production context, half-sibships provide large families that are highly suitable for DNA pooling. Estimators for QTL position and effect have been proposed that make use of information from flanking markers. We present formulas derived by the delta method for the asymptotic variance of these estimators.     

The Accuracy,Feasibility and Challenges of Sequencing Short Tandem Repeats Using Next-Generation Sequencing Platforms

To date we have little knowledge of how accurate next-generation sequencing (NGS) technologies are in sequencing repetitive sequences beyond known limitations to accurately sequence homopolymers. Only a handful of previous reports have evaluated the potential of NGS for sequencing short tandem repeats (microsatellites) and no empirical study has compared and evaluated the performance of more than one NGS platform with the same dataset. Here we examined yeast microsatellite variants from both long-read (454-sequencing) and short-read (Illumina) NGS platforms and compared these to data derived through Sanger sequencing. In addition, we investigated any locus-specific biases and differences that might have resulted from variability in microsatellite repeat number, repeat motif or type of mutation. Out of 112 insertion/deletion variants identified among 45 microsatellite amplicons in our study, we found 87.5% agreement between the 454-platform and Sanger sequencing in frequency of variant detection after Benjamini-Hochberg correction for multiple tests. For a subset of 21 microsatellite amplicons derived from Illumina sequencing, the results of short-read platform were highly consistent with the other two platforms, with 100% agreement with 454-sequencing and 93.6% agreement with the Sanger method after Benjamini-Hochberg correction. We found that the microsatellite attributes copy number, repeat motif and type of mutation did not have a significant effect on differences seen between the sequencing platforms. We show that both long-read and short-read NGS platforms can be used to sequence short tandem repeats accurately, which makes it feasible to consider the use of these platforms in high-throughput genotyping. It appears the major requirement for achieving both high accuracy and rare variant detection in microsatellite genotyping is sufficient read depth coverage. This might be a challenge because each platform generates a consistent pattern of non-uniform sequence coverage, which, as our study suggests, may affect some types of tandem repeats more than others.     

Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.     

The Next Generation of Molecular Markers From Massively Parallel Sequencing of Pooled DNA Samples

Next generation sequencing (NGS) is about to revolutionize genetic analysis. Currently NGS techniques are mainly used to sequence individual genomes. Due to the high sequence coverage required, the costs for population-scale analyses are still too high to allow an extension to nonmodel organisms. Here, we show that NGS of pools of individuals is often more effective in SNP discovery and provides more accurate allele frequency estimates, even when taking sequencing errors into account. We modify the population genetic estimators Tajima''s π and Watterson''s θ to obtain unbiased estimates from NGS pooling data. Given the same sequencing effort, the resulting estimators often show a better performance than those obtained from individual sequencing. Although our analysis also shows that NGS of pools of individuals will not be preferable under all circumstances, it provides a cost-effective approach to estimate allele frequencies on a genome-wide scale.NEXT generation sequencing (NGS) is about to revolutionize biology. Through a massive parallelization, NGS provides an enormous number of reads, which permits sequencing of entire genomes at a fraction of the costs for Sanger sequencing. Hence, for the first time it has become feasible to obtain the complete genomic sequence for a large number of individuals. For several organisms, including humans, Drosophila melanogaster, and Arabidopsis thaliana, large resequencing projects are well on their way. Nevertheless, despite the enormous cost reduction, genome sequencing on a population scale is still out of reach for the budget of most laboratories. The extraction of as much statistical information as possible at cost as low as possible has therefore already attracted considerable interest. See, for instance, Jiang et al. (2009) for the modeling of sequencing errors and Erlich et al. (2009) for the efficient tagging of sequences.Current genome-wide resequencing projects collect the sequences individual by individual. To obtain full coverage of the entire genome and to have high confidence that all heterozygous sites were discovered, it is required that genomes are sequenced at a sufficiently high coverage. As many of the reads provide only redundant information, cost could be reduced by a more effective sampling strategy.In this report, we explore the potential of DNA pooling to provide a more cost-effective approach for SNP discovery and genome-wide population genetics. Sequencing a large pool of individuals simultaneously keeps the number of redundant DNA reads low and provides thus an economic alternative to the sequencing of individual genomes. On the other hand, more care has to be taken to establish an appropriate control of sequencing errors. Obviously haplotype information is not available from pooling experiments, but this will often be outweighed by the increased accuracy in population genetic inference.Focusing on biallelic loci, our analysis shows that with sufficiently large pool sizes, pooling usually outperforms the separate sequencing of individuals, both for estimating allele frequencies and for inference of population genetic parameters. When sequencing errors are not too common, pooling seems also to be a good choice for SNP detection experiments. To avoid the additional challenges encountered with individual sequencing of diploid individuals, we compare pooling with individual sequencing of haploid individuals. See Lynch (2008, 2009) for a discussion of next generation sequencing of diploid individuals. Our results for the pooling experiments should be also applicable to a diploid setting, as we are just merging pools of size 2 to a larger pool in this case, leading to a pool size of n = 2n_d for n_d diploid individuals. In the methods section, we derive several mathematical expressions that permit us to compare pooling with separate sequencing of individuals. These formulas are then applied in the results section to illustrate the differences in accuracy between the approaches. A reader who is interested only in the actual differences under several scenarios might therefore want to move directly to the results section.     

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.     

Targeted single molecule mutation detection with massively parallel sequencing

Next-generation sequencing (NGS) technologies have transformed genomic research and have the potential to revolutionize clinical medicine. However, the background error rates of sequencing instruments and limitations in targeted read coverage have precluded the detection of rare DNA sequence variants by NGS. Here we describe a method, termed CypherSeq, which combines double-stranded barcoding error correction and rolling circle amplification (RCA)-based target enrichment to vastly improve NGS-based rare variant detection. The CypherSeq methodology involves the ligation of sample DNA into circular vectors, which contain double-stranded barcodes for computational error correction and adapters for library preparation and sequencing. CypherSeq is capable of detecting rare mutations genome-wide as well as those within specific target genes via RCA-based enrichment. We demonstrate that CypherSeq is capable of correcting errors incurred during library preparation and sequencing to reproducibly detect mutations down to a frequency of 2.4 × 10⁻⁷ per base pair, and report the frequency and spectra of spontaneous and ethyl methanesulfonate-induced mutations across the Saccharomyces cerevisiae genome.     

Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes

Next Generation Sequencing Technology has revolutionized our ability to study the contribution of rare genetic variation to heritable traits. However, existing single-marker association tests are underpowered for detecting rare risk variants. A more powerful approach involves pooling methods that combine multiple rare variants from the same gene into a single test statistic. Proposed pooling methods can be limited because they generally assume high-quality genotypes derived from deep-coverage sequencing, which may not be available. In this paper, we consider an intuitive and computationally efficient pooling statistic, the cumulative minor-allele test (CMAT). We assess the performance of the CMAT and other pooling methods on datasets simulated with population genetic models to contain realistic levels of neutral variation. We consider study designs ranging from exon-only to whole-gene analyses that contain noncoding variants. For all study designs, the CMAT achieves power comparable to that of previously proposed methods. We then extend the CMAT to probabilistic genotypes and describe application to low-coverage sequencing and imputation data. We show that augmenting sequence data with imputed samples is a practical method for increasing the power of rare-variant studies. We also provide a method of controlling for confounding variables such as population stratification. Finally, we demonstrate that our method makes it possible to use external imputation templates to analyze rare variants imputed into existing GWAS datasets. As proof of principle, we performed a CMAT analysis of more than 8 million SNPs that we imputed into the GAIN psoriasis dataset by using haplotypes from the 1000 Genomes Project.     

Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms

Genotype imputation is an indispensable step in human genetic studies. Large reference panels with deeply sequenced genomes now allow interrogating variants with minor allele frequency < 1% without sequencing. Although it is critical to consider limits of this approach, imputation methods for rare variants have only done so empirically; the theoretical basis of their imputation accuracy has not been explored. To provide theoretical consideration of imputation accuracy under the current imputation framework, we develop a coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals. We show that broadly used imputation algorithms include model misspecifications about this joint genealogy that limit the ability to correctly impute rare variants. We develop closed-form solutions for the probability distribution of this joint genealogy and quantify the inevitable error rate resulting from the model misspecification across a range of allele frequencies and reference sample sizes. We show that the probability of a falsely imputed minor allele decreases with reference sample size, but the proportion of falsely imputed minor alleles mostly depends on the allele count in the reference sample. We summarize the impact of this error on genotype imputation on association tests by calculating the r² between imputed and true genotype and show that even when modeling other sources of error, the impact of the model misspecification has a significant impact on the r² of rare variants. To evaluate these predictions in practice, we compare the imputation of the same dataset across imputation panels of different sizes. Although this empirical imputation accuracy is substantially lower than our theoretical prediction, modeling misspecification seems to further decrease imputation accuracy for variants with low allele counts in the reference. These results provide a framework for developing new imputation algorithms and for interpreting rare variant association analyses.     

Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping

Molecular markers produced by next‐generation sequencing (NGS) technologies are revolutionizing genetic research. However, the costs of analysing large numbers of individual genomes remain prohibitive for most population genetics studies. Here, we present results based on mathematical derivations showing that, under many realistic experimental designs, NGS of DNA pools from diploid individuals allows to estimate the allele frequencies at single nucleotide polymorphisms (SNPs) with at least the same accuracy as individual‐based analyses, for considerably lower library construction and sequencing efforts. These findings remain true when taking into account the possibility of substantially unequal contributions of each individual to the final pool of sequence reads. We propose the intuitive notion of effective pool size to account for unequal pooling and derive a Bayesian hierarchical model to estimate this parameter directly from the data. We provide a user‐friendly application assessing the accuracy of allele frequency estimation from both pool‐ and individual‐based NGS population data under various sampling, sequencing depth and experimental error designs. We illustrate our findings with theoretical examples and real data sets corresponding to SNP loci obtained using restriction site–associated DNA (RAD) sequencing in pool‐ and individual‐based experiments carried out on the same population of the pine processionary moth (Thaumetopoea pityocampa). NGS of DNA pools might not be optimal for all types of studies but provides a cost‐effective approach for estimating allele frequencies for very large numbers of SNPs. It thus allows comparison of genome‐wide patterns of genetic variation for large numbers of individuals in multiple populations.     

Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals. We analysed the genotype discordance between HapMap and 1KGP to assess consistency in the SNPs from the two references. In 1KGP, 90.58% of 36,817,799 SNPs detected were not measured in HapMap. More SNPs with minor allele frequencies less than 0.01 were found in 1KGP than HapMap. The two references have low discordance (generally smaller than 0.02) in genotypes of common SNPs, with most discordance from heterozygous SNPs. Our study demonstrated that SNP array-based GWAS findings were reliable and useful, although only a small portion of genetic variances were explained. NGS can detect not only common but also rare variants, supporting the expectation that NGS-based GWAS will be able to incorporate a much larger portion of genetic variance than SNP arrays-based GWAS.     

Accounting for genotype uncertainty in the estimation of allele frequencies in autopolyploids

Despite the increasing opportunity to collect large‐scale data sets for population genomic analyses, the use of high‐throughput sequencing to study populations of polyploids has seen little application. This is due in large part to problems associated with determining allele copy number in the genotypes of polyploid individuals (allelic dosage uncertainty–ADU), which complicates the calculation of important quantities such as allele frequencies. Here, we describe a statistical model to estimate biallelic SNP frequencies in a population of autopolyploids using high‐throughput sequencing data in the form of read counts. We bridge the gap from data collection (using restriction enzyme based techniques [e.g. GBS, RADseq]) to allele frequency estimation in a unified inferential framework using a hierarchical Bayesian model to sum over genotype uncertainty. Simulated data sets were generated under various conditions for tetraploid, hexaploid and octoploid populations to evaluate the model's performance and to help guide the collection of empirical data. We also provide an implementation of our model in the R package polyfreqs and demonstrate its use with two example analyses that investigate (i) levels of expected and observed heterozygosity and (ii) model adequacy. Our simulations show that the number of individuals sampled from a population has a greater impact on estimation error than sequencing coverage. The example analyses also show that our model and software can be used to make inferences beyond the estimation of allele frequencies for autopolyploids by providing assessments of model adequacy and estimates of heterozygosity.     

Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.     

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.     

A powerful test for multiple rare variants association studies that incorporates sequencing qualities

Next-generation sequencing data will soon become routinely available for association studies between complex traits and rare variants. Sequencing data, however, are characterized by the presence of sequencing errors at each individual genotype. This makes it especially challenging to perform association studies of rare variants, which, due to their low minor allele frequencies, can be easily perturbed by genotype errors. In this article, we develop the quality-weighted multivariate score association test (qMSAT), a new procedure that allows powerful association tests between complex traits and multiple rare variants under the presence of sequencing errors. Simulation results based on quality scores from real data show that the qMSAT often dominates over current methods, that do not utilize quality information. In particular, the qMSAT can dramatically increase power over existing methods under moderate sample sizes and relatively low coverage. Moreover, in an obesity data study, we identified using the qMSAT two functional regions (MGLL promoter and MGLL 3'-untranslated region) where rare variants are associated with extreme obesity. Due to the high cost of sequencing data, the qMSAT is especially valuable for large-scale studies involving rare variants, as it can potentially increase power without additional experimental cost. qMSAT is freely available at http://qmsat.sourceforge.net/.     

Frequency of Rare Allelic Variation in Candidate Genes among Individuals with Low and High Urinary Calcium Excretion

Our study investigated the association of rare allelic variants with extremes of 24-hour urinary calcium excretion because higher urinary calcium excretion is a dominant risk factor for calcium-based kidney stone formation. We resequenced 40 candidate genes potentially related to urinary calcium excretion in individuals from the Nurses'' Health Studies I & II and the Health Professionals Follow-up Study. A total of 960 participants were selected based on availability of 24-hour urine collection data and level of urinary calcium excretion (low vs. high). We utilized DNA sample pooling, droplet-based target gene enrichment, multiplexing, and high-throughput sequencing. Approximately 64% of samples (n = 615) showed both successful target enrichment and sequencing data with >20-fold deep coverage. A total of 259 novel allelic variants were identified. None of the rare gene variants (allele frequencies <2%) were found with increased frequency in the low vs. high urinary calcium groups; most of these variants were only observed in single individuals. Unadjusted analysis of variants with allele frequencies ≥2% suggested an association of the Claudin14 SNP rs113831133 with lower urinary calcium excretion (6/520 versus 29/710 haplotypes, P value = 0.003). Our data, together with previous human and animal studies, suggest a possible role for Claudin14 in urinary calcium excretion. Genetic validation studies in larger sample sets will be necessary to confirm our findings for rs113831133. In the tested set of candidate genes, rare allelic variants do not appear to contribute significantly to differences in urinary calcium excretion between individuals.     

Inferring Clonal Composition from Multiple Sections of a Breast Cancer

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor''s clonal composition; however, deconvolving NGS data to infer a tumor''s clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.     

MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue-residue contacts

Background

Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.

Results

We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.

Conclusions

Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.     

The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process

With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.