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Biology subjects

Mawass, W.

Publications and source records attributed to Mawass, W..

5 recordsLinked to original sources

Rare variants drive high variance in human ancestral fitness at mutation-selection-drift balance

It is an open question whether variation in the genetic load of unconditionally deleterious mutations contributes substantially to the variability in human disease. Here, we solve for mutation-selection-drift balance and predict variation in genetic load given a realistic human genome-wide deleterious mutation rate, U, and a distribution of fitness effects (DFE). Empirical estimates of U come from sequence constraint, which fails to count slightly deleterious mutations that nevertheless fix. We use the inferred DFE to correct for this and conclude that total human U>3.8. Two humans typically differ in ancestral fitness by 17-33% given uncertainty in U, or by 6-49% when we consider a broad range of alternative DFEs. Results are similar for other species with larger mean selection coefficients, such as other mammals. Most variation in load comes from rare variants with frequencies below 1%, with a substantial fraction coming from ultra-rare variants below 0.01%. This could help explain why some of the heritability observed in pedigree studies is missing from genome-wide association studies. Accounting for rare and ultra-rare variants, e.g., via variant-effect prediction of unique mutations from whole-genome sequencing rather than via polygenic risk scores, could help identify individuals at high risk of disease. SignificanceMany human mutations mildly disrupt molecular function, e.g., by destabilizing proteins. Having too many of these mutations would have reduced fitness in ancestral human environments and might contribute to disease today. Here, we mathematically derive how much variation in fitness such mutations cause, using estimated human parameter values. Rare variants with larger fitness effects contribute the most. Identifying individuals with high disease risk likely requires methods capable of scoring rare variants.

evolutionary biology↗

Quantifying the susceptibility of polygenic scores to ancestry stratification

Polygenic scores aim to predict phenotypes from genetic data, yet they remain vulnerable to spurious correlations arising from environmental variation that covaries with population structure. While standard methods like Principal Component Analysis (PCA) and Linear Mixed Models (LMMs) mitigate this, quantifying the residual risk for specific applications remains challenging. Here, we develop a theoretical framework that quantifies the proportion of genetic variance in a GWAS panel explained by an external ancestry gradient (H), providing a direct measure of stratification susceptibility. We show that this baseline risk is amplified by the ascertainment process itself, which creates a directional bias ({Phi}) that is particularly strong for variants with intermediate probabilities of ascertainment. Applying this framework to the UK Biobank, we find that while uncorrected susceptibility is drastically higher in diverse cohorts, PCA correction effectively flattens this disparity. We observe that the residual susceptibility (H') in corrected diverse panels is often comparable to, or marginally lower than, that found in restricted homogeneous subsets, suggesting that sample diversity need not compromise stratification control. However, for both study designs, residual structure often remains just above or indistinguishable from the theoretical limit of detection. Because even undetectable levels of structure can accumulate to produce significant bias in highly polygenic scores, we introduce a diagnostic to calculate the critical magnitude of environmental confounding required to explain an observed signal. Using this diagnostic, we find that both the well-known divergence in height scores between Sardinia and mainland Europe and novel signals of divergence in systolic blood pressure scores within the British Isles appear relatively robust to residual stratification, albeit for different reasons. While the Sardinia signal would require moderate-to-strong environmental confounding to align with a vanishingly small residual ancestry axis, the systolic blood pressure signals would require implausibly large environmental effects to be explained as artifacts.

genetics↗

Contrasting evolutionary outcomes in a human life history trait which is heritable and under consistent unbiased directional selection

Microevolution is well documented in natural populations, yet its persistence as an adaptive process remains debated. Despite widespread directional selection on heritable traits, including life-history traits, evolutionary stasis often prevails, with no detectable microevolutionary response. However, evidence of microevolution in some populations raises a key question: do populations under similar ecological conditions and selective pressures exhibit parallel evolution in the same traits? To address this, we examined age at first reproduction (AFR) in three contemporary human populations considered semi-independent replicates, sharing a genetic, demographic, and historical background, with pedigree data available for [~]7 generations. Across all populations, we found strong directional selection favoring earlier AFR, yet quantitative genetic analyses revealed consistently low heritability (h2 {approx} 0.11). Only in the Charlevoix population did AFR show a negative genetic correlation with relative fitness, where more than 50% of the standardized phenotypic selection gradient was explained by the genetic selection gradient. Using the Breeders Equation and Robertsons Secondary Theorem of Selection, we predicted an evolutionary response to selection for AFR, which emerged only in Charlevoix. However, neither phenotypic nor breeding values of AFR showed temporal trends, indicating evolutionary stasis. These findings demonstrate that even under consistent directional selection and moderate additive genetic variation, microevolutionary responses may vary across replicate populations. Our results underscore the prevalence of evolutionary stasis, challenging assumptions about the inevitability of microevolution in response to natural selection.

evolutionary biology↗

Extinction vortices are driven more by a shortage of beneficial mutations than by deleterious mutation accumulation

Habitat loss contributes to extinction risk in multiple ways. Genetically, small populations can face an "extinction vortex" -- a positive feedback loop between declining fitness and declining population size. Two distinct genetic mechanisms can drive a long-term extinction vortex: i) ineffective selection in small populations allows deleterious mutations to fix, driving "mutational meltdown", and ii) smaller populations generate fewer beneficial mutations essential for long-term adaptation, a mechanism we term "mutational drought". To determine their relative importance, we ask whether, for a population near its critical size for persistence, changes in population size have a larger effect on the beneficial vs. deleterious component of fitness flux. In stable environments, we find that mutational drought is nearly as significant as mutational meltdown. Drought is more important than meltdown when populations must also adapt to a changing environment, unless the beneficial mutation rate is extremely high. Linkage disequilibria from background selection under realistically high deleterious mutation rates modestly increase the importance of mutational drought. Long-term conservation efforts should consider adaptive potential, not just deleterious load.

evolutionary biology↗

Assessing the impact of pedigree quality on the validity of quantitative genetic parameterestimates

Investigating the evolutionary dynamics of complex traits in nature requires the accurate assessment of their genetic architecture. Using a quantitative genetic (QG) modeling approach (e.g., animal model), relatedness information from a pedigree combined with phenotypic measurements can be used to infer the amount of additive genetic variance in traits. However, pedigree information from natural systems is not perfect and might contain errors or be of low quality. Published sensitivity analyses revealed a limited impact of expected error rates on parameter estimates. However, natural systems will differ in many respects (e.g., mating system, data availability, pedigree structure), thus it can be inappropriate to generalize outcomes from one system to another. French-Canadian (FC) genealogies are extensive and deep-rooted (up to 9 generations in this study) making them ideal to study how the quality and properties (e.g., errors, completeness) of pedigrees affect QG estimates. We conducted simulation analyses to infer the reliability of QG estimates using FC pedigrees and how it is impacted by genealogical errors and variation in pedigree structure. Broadly, results show that pedigree size and depth are important determinants of precision but not of accuracy. While the mean genealogical entropy (based on missing links) seems to be a good indicator of accuracy. Including a shared familial component into the simulations led to on average a 46% overestimation of the additive genetic variance. This has crucial implications for evolutionary studies aiming to estimate QG parameters given that many traits of interest, such as life history, exhibit important non-genetic sources of variation.

evolutionary biology↗