Inferring demography from runs of homozygosity in whole-genome sequence, with correction for sequence errors

Molecular Biology and Evolution
Iona M MacleodMike Goddard


Whole-genome sequence is potentially the richest source of genetic data for inferring ancestral demography. However, full sequence also presents significant challenges to fully utilize such large data sets and to ensure that sequencing errors do not introduce bias into the inferred demography. Using whole-genome sequence data from two Holstein cattle, we demonstrate a new method to correct for bias caused by hidden errors and then infer stepwise changes in ancestral demography up to present. There was a strong upward bias in estimates of recent effective population size (Ne) if the correction method was not applied to the data, both for our method and the Li and Durbin (Inference of human population history from individual whole-genome sequences. Nature 475:493-496) pairwise sequentially Markovian coalescent method. To infer demography, we use an analytical predictor of multiloci linkage disequilibrium (LD) based on a simple coalescent model that allows for changes in Ne. The LD statistic summarizes the distribution of runs of homozygosity for any given demography. We infer a best fit demography as one that predicts a match with the observed distribution of runs of homozygosity in the corrected sequence data. We use multiloci L...Continue Reading


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