Biomedical subjects

Kirk E Lohmueller

Publications and source records attributed to Kirk E Lohmueller.

8 recordsLinked to original sources

PubMed2026

The distribution of fitness effects of nonsynonymous mutations varies phylogenetically across animals.

The distribution of fitness effects (DFE) describes the selection coefficients of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of differences in the DFE for non-synonymous mutations have not been systematically investigated across species with divergent phylogenetic histories and ecologies. Here, we inferred the DFE in natural populations of 11 animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We found that mammals have a higher proportion of strongly deleterious mutations (defined as s≤-0.01; 22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Further, the DFE co-varies with phylogeny, such that the mean mutation effects are more similar in closely related species (Pagel's λ = 0.84, P = 0.01). Next, we investigated whether various summary statistics of the DFE were related to variation in life-history traits across these organisms. We found some support for genome size, body mass, and long-term effective population size being correlated with the DFE. Overall, our findings are consistent with predictions derived independently from the Fisher's Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. FGM predicts that mutations are more deleterious in complex organisms, while strongly deleterious mutations occur more frequently in smaller populations. Our study demonstrates strong phylogenetic signal in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of epistasis, long-term population size and organismal complexity could be underlying variation in the DFE across animals.

Journal Article↗

PubMed2026

Pervasive cryptic selection in the human noncoding genome.

The prevailing dogma in evolutionary genetics holds that mutations within sequences that are conserved across a phylogeny are deleterious in those species, and mutations outside are neutrally evolving. Indeed, such comparative genomic approaches have estimated that mutations in approximately 5% of the human genome experience negative selection. However, sites that have biological function in certain lineages but not in others, i.e. functional turnover, may violate this assumption since these sites may be invisible to comparative genomic approaches. Thus, the extent of such cryptic, or hidden, negative selection remains elusive. Here, we developed a statistical test to detect cryptic selection in human polymorphism data. Applying our approach to simulated data shows that cryptic selection shapes the site frequency spectrum (SFS) and the statistical detection power depends on the proportion of mutations experiencing cryptic selection, the amount of sequence tested, and the sample size. We applied our method to polymorphism data from the 1000 Genomes Project, comparing variants in putatively functional noncoding regions to those in putatively neutral regions. We detected pervasive signals of cryptic selection in putatively functional regions, even after filtering out the top 70% of conserved sites. Using simulations with varying levels of cryptic selection, we estimated the extent of genome-wide constraint in the human genome. Our approximation suggests that mutations in at least 7% of the human genome are under negative selection, which is greater than the estimates from conservation-based methods, and that many of these mutations have escaped detection by comparative genomic methods. In sum, our results highlight the evolutionary dynamic nature of the noncoding genome and suggest the need to account for functional turnover when identifying putatively neutral variants for evolutionary analyses.

Journal Article↗

PubMed2025

Accessible, realistic genome simulation with selection using stdpopsim.

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

Journal Article↗

PubMed2025

The distribution of fitness effects varies phylogenetically across animals.

The distribution of fitness effects (DFE) describes the selection coefficients () of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of DFE variation have not been systematically investigated across species with divergent phylogenetic histories and ecological functions. Here, we inferred the DFE in natural populations of eleven animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We find that the DFE co-varies with phylogeny, where the expected mutation effects are more similar in closely related species (). Additionally, mammals have a higher proportion of strongly deleterious mutations (22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Population size is significantly negatively correlated with the expected impact of new deleterious mutations (), and the proportion of new beneficial mutations (). These findings align with Fisher's Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. Consistent with the FGM's predictions, we observe that mutations are more deleterious in complex organisms, while beneficial mutations occur more frequently in smaller populations to compensate for the drift load. Our study demonstrates strong phylogenetic constraints in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of global epistasis, long-term population size and organismal complexity drive variation in the DFE across animals.

Fisher’s geometric model↗

PubMed2024

Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids.

The domestication of wild canids led to dogs no longer living in the wild but instead residing alongside humans. Extreme changes in behavior and diet associated with domestication may have led to the relaxation of the selective pressure on traits that may be less important in the domesticated context. Thus, here we hypothesize that strongly deleterious mutations may have become less deleterious in domesticated populations. We test this hypothesis by estimating the distribution of fitness effects (DFE) for new amino acid changing mutations using whole-genome sequence data from 24 gray wolves and 61 breed dogs. We find that the DFE is strikingly similar across canids, with 26-28% of new amino acid changing mutations being neutral/nearly neutral (|s| < 1e-5), and 41-48% under strong purifying selection (|s| > 1e-2). Our results are robust to different model assumptions suggesting that the DFE is stable across short evolutionary timescales, even in the face of putative drastic changes in the selective pressure caused by artificial selection during domestication and breed formation. On par with previous works describing DFE evolution, our data indicate that the DFE of amino acid changing mutations depends more strongly on genome structure and organismal characteristics, and less so on shifting selective pressures or environmental factors. Given the constant DFE and previous data showing that genetic variants that differentiate wolf and dog populations are enriched in regulatory elements, we speculate that domestication may have had a larger impact on regulatory variation than on amino acid changing mutations.

Journal Article↗

PubMed2005

Variants associated with common disease are not unusually differentiated in frequency across populations.

Genetic variants that contribute to risk of common disease may differ in frequency across populations more than random variants in the genome do, perhaps because they have been exposed to population-specific natural selection. To assess this hypothesis empirically, we analyzed data from two groups of single-nucleotide polymorphisms (SNPs) that have shown reproducible (n = 9) or reported (n = 39) associations with common diseases. We compared the frequency differentiation (between Europeans and Africans) of the disease-associated SNPs with that of random SNPs in the genome. These common-disease-associated SNPs are not significantly more differentiated across populations than random SNPs. Thus, for the data examined here, ethnicity will not be a good predictor of genotype at many common-disease-associated SNPs, just as it is rarely a good predictor of genotype at random SNPs in the genome.

Black People↗

PubMed2004

Methods for high-density admixture mapping of disease genes.

Admixture mapping (also known as "mapping by admixture linkage disequilibrium," or MALD) has been proposed as an efficient approach to localizing disease-causing variants that differ in frequency (because of either drift or selection) between two historically separated populations. Near a disease gene, patient populations descended from the recent mixing of two or more ethnic groups should have an increased probability of inheriting the alleles derived from the ethnic group that carries more disease-susceptibility alleles. The central attraction of admixture mapping is that, since gene flow has occurred recently in modern populations (e.g., in African and Hispanic Americans in the past 20 generations), it is expected that admixture-generated linkage disequilibrium should extend for many centimorgans. High-resolution marker sets are now becoming available to test this approach, but progress will require (a). computational methods to infer ancestral origin at each point in the genome and (b). empirical characterization of the general properties of linkage disequilibrium due to admixture. Here we describe statistical methods to estimate the ancestral origin of a locus on the basis of the composite genotypes of linked markers, and we show that this approach accurately estimates states of ancestral origin along the genome. We apply this approach to show that strong admixture linkage disequilibrium extends, on average, for 17 cM in African Americans. Finally, we present power calculations under varying models of disease risk, sample size, and proportions of ancestry. Studying approximately 2500 markers in approximately 2500 patients should provide power to detect many regions contributing to common disease. A particularly important result is that the power of an admixture mapping study to detect a locus will be nearly the same for a wide range of mixture scenarios: the mixture proportion should be 10%-90% from both ancestral populations.

Alleles↗

PubMed2003

Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.

Alleles↗