  Natural selection at one site shapes patterns of genetic variation at linked
sites. Quantifying the effects of 'linked selection' on levels of genetic
diversity is key to making reliable inference about demography, building a null
model in scans for targets of adaptation, and learning about the dynamics of
natural selection. Here, we introduce the first method that jointly infers
parameters of distinct modes of linked selection, notably background selection
and selective sweeps, from genome-wide diversity data, functional annotations
and genetic maps. The central idea is to calculate the probability that a
neutral site is polymorphic given local annotations, substitution patterns, and
recombination rates. Information is then combined across sites and samples
using composite likelihood in order to estimate genome-wide parameters of
distinct modes of selection. In addition to parameter estimation, this approach
yields a map of the expected neutral diversity levels along the genome. To
illustrate the utility of our approach, we apply it to genome-wide resequencing
data from 125 lines in Drosophila melanogaster and reliably predict diversity
levels at the 1Mb scale. Our results corroborate estimates of a high fraction
of beneficial substitutions in proteins and untranslated regions (UTR). They
allow us to distinguish between the contribution of sweeps and other modes of
selection around amino acid substitutions and to uncover evidence for pervasive
sweeps in untranslated regions (UTRs). Our inference further suggests a
substantial effect of linked selection from non-classic sweeps. More generally,
we demonstrate that linked selection has had a larger effect in reducing
diversity levels and increasing their variance in D. melanogaster than
previously appreciated.
