Biology subjects

Ros-Freixedes, R.

Publications and source records attributed to Ros-Freixedes, R..

5 recordsLinked to original sources

bioRxiv2018

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

BackgroundInherent sources of error and bias that affect the quality of the sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing there is a need to understand the impact of these errors and bias on resulting genotype calls.\n\nResultsWe used a dataset of 26 pigs sequenced both at 2x with multiplexing and at 30x without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, a default and desired step for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.\n\nConclusionsWe propose a simple pipeline to correct this bias and we recommend that users of low-coverage sequencing be wary of unexpected biases produced by tools designed for high-coverage sequencing.

bioinformatics↗

bioRxiv2018

Sequence variability, constraint and selection in the CD163 gene in pigs

BackgroundIn this paper, we investigate sequence variability, evolutionary constraint, and selection on the CD163 gene in pigs. The pig CD163 gene is required for infection by porcine reproductive and respiratory syndrome virus (PRRSV), a serious pathogen with major impact on pig production.\n\nResultsWe used targeted pooled sequencing of the exons of CD163 to detect sequence variants in 35,000 pigs of diverse genetic backgrounds and search for potential knock-out variants. We then used whole genome sequence data from three pig lines to calculate a variant intolerance score, which measures the tolerance of genes to protein coding variation, a selection test on protein coding variation over evolutionary time, and haplotype diversity statistics to detect recent selective sweeps during breeding.\n\nConclusionsWe performed a deep survey of sequence variation in the CD163 gene in domestic pigs. We found no potential knock-out variants. CD163 was moderately intolerant to variation, and showed evidence of positive selection in the lineage leading up to the pig, but no evidence of selective sweeps during breeding.

genomics↗

bioRxiv2017

Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees

In this paper we extend multi-locus iterative peeling to be a computationally efficient method for calling, phasing, and imputing sequence data of any coverage in small or large pedigrees. Our method, called hybrid peeling, uses multi-locus iterative peeling to estimate shared chromosome segments between parents and their offspring, and then uses single-locus iterative peeling to aggregate genomic information across multiple generations. Using a synthetic dataset, we first analysed the performance of hybrid peeling for calling and phasing alleles in disconnected families, families which contained only a focal individual and its parents and grandparents. Second, we analysed the performance of hybrid peeling for calling and phasing alleles in the context of the full pedigree. Third, we analysed the performance of hybrid peeling for imputing whole genome sequence data to the remaining individuals in the population. We found that hybrid peeling substantially increase the number of genotypes that were called and phased by leveraging sequence information on related individuals. The calling rate and accuracy increased when the full pedigree was used compared to a reduced pedigree of just parents and grandparents. Finally, hybrid peeling accurately imputed whole genome sequence information to non-sequenced individuals. We believe that this algorithm will enable the generation of low cost and high accuracy whole genome sequence data in many pedigreed populations. We are making this algorithm available as a standalone program called AlphaPeel.

genetics↗

bioRxiv2017

Assessment of the performance of different hidden Markov models for imputation in animal breeding

In this paper we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. However, recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations. To test this, we evaluate here the accuracy and computational cost of several methods in a series of simulated populations and a real animal population. We first tested single-step (diploid) imputation, which performs both phasing and imputation. Then we tested pre-phasing followed by haploid imputation. We tested four diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and minimac3). We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, we found that pre-phasing with Eagle2 or HAPI-UR and imputing with minimac3 or IMPUTE2 gave the highest accuracies in both simulated and real data.

genetics↗

bioRxiv2017

A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals

BackgroundThis paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes.\n\nResultsOur method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional to the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on the haplotypes population frequency. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach to filter rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted.\n\nConclusionsWe present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach of filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than methods reported previously for reducing sequencing redundancy.

genomics↗