Biology subjects

Whalen, A.

Publications and source records attributed to Whalen, A..

4 recordsLinked to original sources

bioRxiv2018

Social learning strategies regulate the wisdom and madness of interactive online crowds

Why groups of individuals sometimes exhibit collective wisdom and other times maladaptive herding is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-ftting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated wisdom of the crowd effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

animal behavior and cognition↗

bioRxiv2018

Parentage assignment with low density array data and low coverage sequence data

In this paper we evaluate using genotype-by-sequencing (GBS) data to perform parentage assignment in lieu of traditional array data. The use of GBS data raises two issues: First, for low-coverage GBS data, it may not be possible to call the genotype at many loci, a critical first step for detecting opposing homozygous markers. Second, the amount of sequencing coverage may vary across individuals, making it challenging to directly compare the likelihood scores between putative parents. To address these issues we extend the probabilistic framework of Huisman (2017) and evaluate putative parents by comparing their (potentially noisy) genotypes to a series of proposal distributions. These distributions describe the expected genotype probabilities for the relatives of an individual. We assign putative parents as a parent if they are classified as a parent (as opposed to e.g., an unrelated individual), and if the assignment score passes a threshold. We evaluated this method on simulated data and found that (1) high-coverage GBS data performs similarly to array data and requires only a small number of markers to correctly assign parents and (2) low-coverage GBS data (as low as 0.1x) can also be used, provided that it is obtained across a large number of markers. When analysing the low-coverage GBS data, we also found a high number of false positives if the true parent is not contained within the list of candidate parents, but that this false positive rate can be greatly reduced by hand tuning the assignment threshold. We provide this parentage assignment method as a standalone program called AlphaAssign.

genetics↗

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↗