An explosion of high-throughput DNA sequencing in the past decade has led to a surge of
interest in population-scale inference with whole-genome data. Recent work in population …

Population genetics relies heavily on simulated data for validation, inference and intuition.
In particular, since the evolutionary ‘ground truth’ for real data is always limited, simulated …

Background The genetic bases of many complex phenotypes are still largely unknown, mostly
due to the polygenic nature of the traits and the small effect of each associated mutation. …

Variation at the FADS1/FADS2 gene cluster is functionally associated with differences in lipid
metabolism and is often hypothesized to reflect adaptation to an agricultural diet. Here, we …

Understanding natural selection and other forms of non-neutrality is a major focus for the
use of machine learning in population genetics. Existing methods rely on computationally …

In this work we develop a novel algorithm for reconstructing the genomes of ancestral
individuals, given genotype or sequence data from contemporary individuals and an extended …

Cultural and socioeconomic differences stratify human societies and shape their genetic
structure beyond the sole effect of geography. Despite mating being limited by sociocultural …

"Deep Learning for Population Genetic Inference" by Sara Mathieson and Yun S. Song …
Sara Mathieson, Haverford College…

Segments of DNA that are inherited from a common ancestor are referred to as identical-by-descent
(IBD). Because these segments are inherited, they not only allow us to study …

We encode population genetic data x as follows. Let xS be the binary n× d matrix with 0 and
1 as the common and rare nucleotide variant, respectively, where n is the number of …