Biology subjects

Albrechtsen, A.

Publications and source records attributed to Albrechtsen, A..

3 recordsLinked to original sources

bioRxiv2018

Inferring Population Structure and Admixture Proportions in Low Depth Next-Generation Sequencing Data

We here present two methods for inferring population structure and admixture proportions in low depth next generation sequencing data. Inference of population structure is essential in both population genetics and association studies and is often performed using principal component analysis or clustering-based approaches. Next-generation sequencing methods provide large amounts of genetic data but are associated with statistical uncertainty for especially low depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through principal component analysis in an iterative approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.

genetics↗

bioRxiv2018

Allele frequency-free inference of close familial relationships from genotypes or low depth sequencing data

Knowledge of how individuals are related is important in many areas of research and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data is limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants, and accurate genotype data. But in studies of non-model organisms or ancient human samples, such data is not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied to both genotype data and to low-depth sequencing data where genotypes cannot be accurately called. We evaluate it using data from SNP arrays and low-depth sequencing from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment, which is important for application to a diverse range of populations and species.

genetics↗

bioRxiv2017

Improved D-Statistic For Low-Coverage Data

The detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups.\n\nWhen working with high throughput sequencing data calling genotypes accurately is not always possible, therefore the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes.\n\nWe provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate.\n\nWe prove that the D-statistic is approximated by a standard normal. Furthermore we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low/medium sequencing depth (1-10X) and performances are as good as with perfectly called genotypes at a sequencing depth of 2X. We show the reliability of error correction on scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.

bioinformatics↗