Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations.
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ABSTRACT:
SUBMITTER: Huang X
PROVIDER: S-EPMC8476148 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPopulations of the Arabian Peninsula have a complex genetic structure that reflects waves of migrations including the earliest human migrations from Africa and eastern Asia, migrations along ancient civilization trading routes and colonization history of recent centuries.ResultsHere, we present a study of genome-wide admixture in this region, using 156 genotyped individuals from Qatar, a country located at the crossroads of these migration patterns. Since haplotypes of these individuals could have originated from many different populations across the world, we have developed a machine learning method "SupportMix" to infer loci-specific genomic ancestry when simultaneously analyzing many possible ancestral populations. Simulations show that SupportMix is not only more accurate than other popular admixture discovery tools but is the first admixture inference method that can efficiently scale for simultaneous analysis of 50-100 putative ancestral populations while being independent of prior demographic information.ConclusionsBy simultaneously using the 55 world populations from the Human Genome Diversity Panel, SupportMix was able to extract the fine-scale ancestry of the Qatar population, providing many new observations concerning the ancestry of the region. For example, as well as recapitulating the three major sub-populations in Qatar, composed of mainly Arabic, Persian, and African ancestry, SupportMix additionally identifies the specific ancestry of the Persian group to populations sampled in Greater Persia rather than from China and the ancestry of the African group to sub-Saharan origin and not Southern African Bantu origin as previously thought.
| S-EPMC3512499 | biostudies-literature
Project description:BackgroundFitness epistasis, the interaction effect of genes at different loci on fitness, makes an important contribution to adaptive evolution. Although fitness interaction evidence has been observed in model organisms, it is more difficult to detect and remains poorly understood in human populations as a result of limited statistical power and experimental constraints. Fitness epistasis is inferred from non-independence between unlinked loci. We previously observed ancestral block correlation between chromosomes 4 and 6 in African Americans. The same approach fails when examining ancestral blocks on the same chromosome due to the strong confounding effect observed in a recently admixed population.ResultsWe developed a novel approach to eliminate the bias caused by admixture linkage disequilibrium when searching for fitness epistasis on the same chromosome. We applied this approach in 16,252 unrelated African Americans and identified significant ancestral correlations in two pairs of genomic regions (P-value<?8.11?×?10-?7) on chromosomes 1 and 10. The ancestral correlations were not explained by population admixture. Historical African-European crossover events are reduced between pairs of epistatic regions. We observed multiple pairs of co-expressed genes shared by the two regions on each chromosome, including ADAR being co-expressed with IFI44 in almost all tissues and DARC being co-expressed with VCAM1, S1PR1 and ELTD1 in multiple tissues in the Genotype-Tissue Expression (GTEx) data. Moreover, the co-expressed gene pairs are associated with the same diseases/traits in the GWAS Catalog, such as white blood cell count, blood pressure, lung function, inflammatory bowel disease and educational attainment.ConclusionsOur analyses revealed two instances of fitness epistasis on chromosomes 1 and 10, and the findings suggest a potential approach to improving our understanding of adaptive evolution.
| S-EPMC7353720 | biostudies-literature
Project description:We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.
| S-EPMC8728706 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell's method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.
| S-EPMC6343668 | biostudies-literature
Project description:The properties of the distribution of deleterious mutational effects on fitness (DDME) are of fundamental importance for evolutionary genetics. Since it is extremely difficult to determine the nature of this distribution, several methods using various assumptions about the DDME have been developed, for the purpose of parameter estimation. We apply a newly developed method to DNA sequence polymorphism data from two Drosophila species and compare estimates of the parameters of the distribution of the heterozygous fitness effects of amino acid mutations for several different distribution functions. The results exclude normal and gamma distributions, since these predict too few effectively lethal mutations and power-law distributions as a result of predicting too many lethals. Only the lognormal distribution appears to fit both the diversity data and the frequency of lethals. This DDME arises naturally in complex systems when independent factors contribute multiplicatively to an increase in fitness-reducing damage. Several important parameters, such as the fraction of effectively neutral non-synonymous mutations and the harmonic mean of non-neutral selection coefficients, are robust to the form of the DDME. Our results suggest that the majority of non-synonymous mutations in Drosophila are under effective purifying selection.
| S-EPMC1686194 | biostudies-literature
Project description:An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome (correlation is not causation). If genetic variants associated with the risk factor are also associated with the outcome, then this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We review the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which includes variants from multiple regions. We focus on answering two questions: When can Mendelian randomization be used to make reliable causal inferences, and when can it be used to make relevant causal inferences?
| S-EPMC6481551 | biostudies-literature
Project description:BackgroundMarine fish populations are often characterized by high levels of gene flow and correspondingly low genetic divergence. This presents a challenge to define management units. Goldsinny wrasse (Ctenolabrus rupestris) is a heavily exploited species due to its importance as a cleaner-fish in commercial salmonid aquaculture. However, at the present, the population genetic structure of this species is still largely unresolved. Here, full-genome sequencing was used to produce the first genomic reference for this species, to study population-genomic divergence among four geographically distinct populations, and, to identify informative SNP markers for future studies.ResultsAfter construction of a de novo assembly, the genome was estimated to be highly polymorphic and of ~600Mbp in size. 33,235 SNPs were thereafter selected to assess genomic diversity and differentiation among four populations collected from Scandinavia, Scotland, and Spain. Global FST among these populations was 0.015-0.092. Approximately 4% of the investigated loci were identified as putative global outliers, and ~ 1% within Scandinavia. SNPs showing large divergence (FST > 0.15) were picked as candidate diagnostic markers for population assignment. One hundred seventy-three of the most diagnostic SNPs between the two Scandinavian populations were validated by genotyping 47 individuals from each end of the species' Scandinavian distribution range. Sixty-nine of these SNPs were significantly (p < 0.05) differentiated (mean FST_173_loci = 0.065, FST_69_loci = 0.140). Using these validated SNPs, individuals were assigned with high probability (≥ 94%) to their populations of origin.ConclusionsGoldsinny wrasse displays a highly polymorphic genome, and substantial population genomic structure. Diversifying selection likely affects population structuring globally and within Scandinavia. The diagnostic loci identified now provide a promising and cost-efficient tool to investigate goldsinny wrasse populations further.
| S-EPMC7547435 | biostudies-literature
Project description:Under the traditional mutation load model based on multiplicative fitness effects, the load in a population is 1-e-U , where U is the genomic deleterious mutation rate. Because this load becomes high under large U, synergistic epistasis has been proposed as one possible means of reducing the load. However, experiments on model organisms attempting to detect synergistic epistasis have often focused on a quadratic fitness model, with the resulting general conclusion being that epistasis is neither common nor strong. Here, I present a model of additive fitness effects and show that, unlike multiplicative effects, the equilibrium frequency of an allele under additivity is dependent on the average absolute fitness of the population. The additive model then results in a load of U/(U +1), which is much lower than 1-e-U for large U. Numerical iterations demonstrate that this analytic derivation holds as a good approximation under biologically relevant values of selection coefficients and U. Additionally, regressions onto Drosophila mutation accumulation data suggest that the common method of inferring epistasis by detecting large quadratic terms from regressions is not always necessary, as the additive model fits the data well and results in synergistic epistasis. Furthermore, the additive model gives a much larger reduction in load than the quadratic model when predicted from the same data, indicating that it is important to consider this additive model in addition to the quadratic model when inferring epistasis from mutation accumulation data.
| S-EPMC4957254 | biostudies-literature
Project description:Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes of a collection of cells with known genealogical structure contains information on phenotypic dynamics. Here, we use a model of phenotypic dynamics on a genealogical tree to define an inference method that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal--a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available.
| S-EPMC4426465 | biostudies-literature
Project description:Spontaneous mutations are the source of new genetic variation and are thus central to the evolutionary process. In molecular evolution and quantitative genetics, the nature of genetic variation depends critically on the distribution of effects of mutations on fitness and other quantitative traits. Spontaneous mutation accumulation (MA) experiments have been the principal approach for investigating the overall rate of occurrence and cumulative effect of mutations but have not allowed the phenotypic effects of individual mutations to be studied directly. Here, we crossed MA lines of the green alga Chlamydomonas reinhardtii with its unmutated ancestral strain to create haploid recombinant lines, each carrying an average of 50% of the accumulated mutations in a large number of combinations. With the aid of the genome sequences of the MA lines, we inferred the genotypes of the mutations, assayed their growth rate as a measure of fitness, and inferred the distribution of fitness effects (DFE) using a Bayesian mixture model. We infer that the DFE is highly leptokurtic (L-shaped). Of mutations with absolute fitness effects exceeding 1%, about one-sixth increase fitness in the laboratory environment. The inferred distribution of effects for deleterious mutations is consistent with a strong role for nearly neutral evolution. Specifically, such a distribution predicts that nucleotide variation and genetic variation for quantitative traits will be insensitive to change in the effective population size.
| S-EPMC6615632 | biostudies-literature