E. A. Thompson

The paper shows that the detectable identity-by-descent rate is asymptotically normal and generalizes this result to complex demographic scenarios and multi-way segments.

This paper introduces an efficient algorithm for simulating identity-by-descent segments, achieving linear scaling in sample size and enabling feasible statistical analyses of large datasets.

The paper analyzes the ancestry of genomes in diploid dioecious organisms, showing how genetic ancestors increase linearly over generations and remain geographically local in finite populations.

This study compares heritability estimators under different LD structures and relatedness, finding no significant performance differences between fixed and random SNP-effect methods.

The paper identifies conditions under which complex traits can be modeled linearly despite biological nonlinearity, and classifies types of nonlinearity from a systems perspective.