Biology subjects

Mileham, A. J.

Publications and source records attributed to Mileham, A. J..

3 recordsLinked to original sources

bioRxiv2019

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

BackgroundFor assembling large whole-genome sequence datasets to be used routinely in research and breeding, the sequencing strategy should be adapted to the methods that will later be used for variant discovery and imputation. In this study we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method well-suited for large livestock populations.\n\nMethodsWe simulated marker array and whole-genome sequence data for fifteen populations with simulated or real pedigrees that had different structures. In these populations we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population we considered four levels of investment in sequencing that were proportional to the size of the population.\n\nResultsImputation accuracy largely depended on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence. Additionally, it was critical to balance high imputation accuracy in early generations as well as in late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of around 2x rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2x provided high imputation accuracy. The gain in imputation accuracy from additional investment diminished with larger populations and larger levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones.\n\nConclusionsSuitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing around 2% of the population at a uniform coverage around 2x, distributed preferably from the third generation of the pedigree onwards. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

genomics↗

bioRxiv2019

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

BackgroundWe demonstrate high accuracy of whole-genome sequence imputation in large livestock populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage.\n\nMethodsWe used data from four pig populations of different sizes (18,349 to 107,815 individuals) that were broadly genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most at 1x or 2x and a small fraction at 30x; average coverage per individual: 4x). We imputed whole-genome sequence with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of a total of 284 individuals that had been sequenced at high coverage, using a leave-one-out design. We complemented these results with simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.\n\nResultsImputation accuracy was high for the majority of individuals in all four populations (median individual-wise correlation was 0.97). Individuals in the earliest generations of each population had lower accuracy than the rest, likely due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status of the individual, the availability of marker array data for immediate ancestors, and the degree of connectedness of an individual to the rest of the population, but sequencing coverage had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. These results were validated with the empirical observations.\n\nConclusionsThe coupling of an appropriate sequencing strategy and imputation method, such as described and validated here, is a powerful strategy for generating whole-genome sequence data in large pedigreed populations with high accuracy. This is a critical step for the successful implementation of whole-genome sequence data for genomic predictions and fine-mapping of causal variants.

genomics↗

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↗