SUMMARY: Here, we present an approach, called SVUPP, which improves genotyping of structural variant (SV) by incorporating read phasing information into genotype likelihoods. Through comprehensive benchmarking, we show that SVUPP achieved higher accuracy than cuteSV2, Sniffles2 and kanpig with both long and ultra long Oxford Nanopore Technologies (ONT) data as well as Pacific Biosciences (PacBio) HiFi data for genotyping SVs without close neighbor SVs. SVUPP can be applied together with SV callers such as cuteSV2 and take the per-read phasing information from reference panel based phasing method such as QUILT2 or from reference-free phasing method such as WhatsHap. AVAILABILITY AND IMPLEMENTATION: SVUPP is written in Nextflow with modular design and is freely available here https://github.com/Zilong-Li/SVUPP.

MOTIVATION: Principal component analysis (PCA) is a commonly used tool in genetics to capture and visualize population structure. Due to technological advances in sequencing, such as the widely used non-invasive prenatal test, massive datasets of ultra-low coverage sequencing are being generated. These datasets are characterized by having a large amount of missing genotype information. RESULTS: We present EMU, a method for inferring population structure in the presence of rampant non-random missingness. We show through simulations that several commonly used PCA methods cannot handle missing data arisen from various sources, which leads to biased results as individuals are projected into the PC space based on their amount of missingness. In terms of accuracy, EMU outperforms an existing method that also accommodates missingness while being competitively fast. We further tested EMU on around 100K individuals of the Phase 1 dataset of the Chinese Millionome Project, that were shallowly sequenced to around 0.08×. From this data we are able to capture the population structure of the Han Chinese and to reproduce previous analysis in a matter of CPU hours instead of CPU years. EMU's capability to accurately infer population structure in the presence of missingness will be of increasing importance with the rising number of large-scale genetic datasets. AVAILABILITY AND IMPLEMENTATION: EMU is written in Python and is freely available at https://github.com/rosemeis/emu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. MATERIALS AND METHODS: We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure. RESULTS: Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. CONCLUSION: We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.

MOTIVATION: The presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested for general use while few are aimed at low-depth nuclear data, a common feature in aDNA datasets. RESULTS: We develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data from male individuals. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5× on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e. when the contaminant and the target come from closely related populations or with increased error rates. With a running time below 5 min, our method is applicable to large scale aDNA genomic studies. AVAILABILITY AND IMPLEMENTATION: The method is implemented in C++ and R and is available in github.com/sapfo/contaminationX and popgen.dk/angsd.

MOTIVATION: Estimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases. RESULTS: Here we present fastNGSadmix, a tool to fast and reliably estimate admixture proportions and perform PCA from next generation sequencing data of a single individual. The analyses are based on genotype likelihoods of the input sample and a set of predefined reference populations. The method has high accuracy, even at low sequencing depth and corrects for the biases introduced by small reference populations. AVAILABILITY AND IMPLEMENTATION: The admixture estimation method is implemented in C ++ and the PCA method is implemented in R. The code is freely available at http://www.popgen.dk/software/index.php/FastNGSadmix. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The amount of IBD in an individual depends on the relatedness of the individual's parents. However, it can also provide information regarding mating system, past history and effective size of the population from which the individual has been sampled. RESULTS: Here, we present a new method for estimating inbreeding IBD tracts from low coverage NGS data. Contrary to other methods that use genotype data, the one presented here uses genotype likelihoods to take the uncertainty of the data into account. We benchmark it under a wide range of biologically relevant conditions and show that the new method provides a marked increase in accuracy even at low coverage. AVAILABILITY AND IMPLEMENTATION: The methods presented in this work were implemented in C/C ++ and are freely available for non-commercial use from https://github.com/fgvieira/ngsF-HMM CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Pairwise relatedness plays an important role in a range of genetic research fields. However, currently only few estimators exist for individuals that are admixed, i.e. have ancestry from more than one population, and these estimators fail in some situations. RESULTS: We present a new software tool, RelateAdmix, for obtaining maximum likelihood estimates of pairwise relatedness from genetic data between admixed individuals. We show using simulated data that it gives rise to better estimates than three state-of-the-art software tools, REAP, KING and Plink, while still being fast enough to be applicable to large datasets. AVAILABILITY AND IMPLEMENTATION: The software tool, implemented in C and R, is freely available from www.popgen.dk/software.

BACKGROUND: High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. RESULTS: We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. CONCLUSIONS: The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd . The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

BACKGROUND: A number of different statistics are used for detecting natural selection using DNA sequencing data, including statistics that are summaries of the frequency spectrum, such as Tajima's D. These statistics are now often being applied in the analysis of Next Generation Sequencing (NGS) data. However, estimates of frequency spectra from NGS data are strongly affected by low sequencing coverage; the inherent technology dependent variation in sequencing depth causes systematic differences in the value of the statistic among genomic regions. RESULTS: We have developed an approach that accommodates the uncertainty of the data when calculating site frequency based neutrality test statistics. A salient feature of this approach is that it implicitly solves the problems of varying sequencing depth, missing data and avoids the need to infer variable sites for the analysis and thereby avoids ascertainment problems introduced by a SNP discovery process. CONCLUSION: Using an empirical Bayes approach for fast computations, we show that this method produces results for low-coverage NGS data comparable to those achieved when the genotypes are known without uncertainty. We also validate the method in an analysis of data from the 1000 genomes project. The method is implemented in a fast framework which enables researchers to perform these neutrality tests on a genome-wide scale.

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.