Andrew Whalen

This study evaluates the benefits of using whole-genome sequence data for genomic prediction in pigs, finding small improvements over marker arrays in some scenarios.

The paper demonstrates accurate genetic imputation in maize, sugar beet, and wheat without pedigrees, outperforming Beagle 5.1 in most scenarios.

This study estimates recombination rate variation in pigs using multiocus iterative peeling across 150,000 pigs, identifies heritability, and finds three genomic regions associated with recombination.

AlphaImpute2 improves imputation accuracy and speed for large livestock populations, outperforming existing methods in both metrics.

This paper improves long-range phasing and haplotype library imputation algorithms to handle large and heterogeneous datasets, achieving high accuracy with reduced computational time.

AlphaFamImpute improves genotype imputation in full-sib families using low-coverage GBS data by leveraging shared haplotype segments between siblings.

This study evaluates sequencing strategies for whole-genome imputation using hybrid peeling, finding that 2% of the population sequenced at 2x coverage provides high accuracy.

This study demonstrates high accuracy of whole-genome sequence imputation in large livestock populations using hybrid peeling, with median individual-wise correlation of 0.97.

This paper develops a method to infer genotypes from high-throughput phenotypes to enable genomic selection in plant breeding programs, showing increased genetic gain in simulations.

This study shows that family-specific low-density genotype arrays improve pedigree-based imputation accuracy, especially at very low marker densities.

AlphaFamSeq is a family-based phasing algorithm that minimizes errors and maximizes allele phasing, achieving high accuracy in simulated data.

This paper evaluates using genotype-by-sequencing data for parentage assignment, showing high-coverage data performs similarly to array data and low-coverage data can be effective with sufficient markers.

This paper introduces hybrid peeling, a method for efficiently calling, phasing, and imputing sequence data in pedigrees, showing improved accuracy and coverage.

This study compares hidden Markov model-based imputation methods in animal breeding, finding that pre-phasing with Eagle2 or HAPI-UR and haploid imputation with minimac3 or IMPUTE2 is faster and more accurate than diploid imputation.