David Balding

Abstract Current methods for inferring historical population sizes from DNA sequences often impose a heavy computational burden, or relieve that burden by imposing a fixed parametric form. In addition, they can be marred by sequencing errors or uncertainty about recombination rates, and the quality of inference is often poor in the recent past. We propose "InferNo" for flexible, nonparametric inference of effective population sizes.

1000 Genomes population analysis (26 populations, n = 2504) The single-population values, measuring the divergence of each of the 26 populations from the inferred global ancestral population (Table 3), are lowest for the AFR populations (0.01—0.05) and highest for PUR and the EAS populations (0.30—0.31). Greater divergence of non-AFR populations may reflect an out-of-Africa bottleneck.

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel.