Mutations In Human Population Research Articles

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Impact of Missense Mutations on Spike Protein Stability and Binding Affinity in the Omicron Variant.

The global effort to combat the COVID-19 pandemic faces ongoing uncertainty with the emergence of Variants of Concern featuring numerous mutations on the Spike (S) protein. In particular, the Omicron Variant is distinguished by 32 mutations, including 10 within its receptor-binding domain (RBD). These mutations significantly impact viral infectivity and the efficacy of vaccines and antibodies currently in use for therapeutic purposes. In our study, we employed structure-based computational saturation mutagenesis approaches to predict the effects of Omicron missense mutations on RBD stability and binding affinity, comparing them to the original Wuhan-Hu-1 strain. Our results predict that mutations such as G431W and P507W induce the most substantial destabilizations in the Wuhan-Hu-1-S/Omicron-S RBD. Notably, we postulate that mutations in the Omicron-S exhibit a higher percentage of enhancing binding affinity compared to Wuhan-S. We found that the mutations at residue positions G447, Y449, F456, F486, and S496 led to significant changes in binding affinity. In summary, our findings may shed light on the widespread prevalence of Omicron mutations in human populations. The Omicron mutations that potentially enhance their affinity for human receptors may facilitate increased viral binding and internalization in infected cells, thereby enhancing infectivity. This informs the development of new neutralizing antibodies capable of targeting Omicron's immune-evading mutations, potentially aiding in the ongoing battle against the COVID-19 pandemic.

Novel Humanized G6PD Deficient Mice Reveal Mechanisms of Increased Tolerance to Exercise As Gleaned By Multi-Omics in Models of Critical Speed

INTRODUCTION: Glucose-6-phosphate-dehydrogenase (G6PD) deficiency is the most common enzymatic deficiency in humans, affecting ~500 million people or ~6% of mankind. G6PD is the rate limiting enzyme of the pentose phosphate pathway (PPP), the main antioxidant pathway in mature red blood cells (RBCs). Activation of the PPP generates NADPH, an essential reducing equivalent for antioxidant metabolism that protects against intra- and extra-vascular hemolysis in response to oxidant stress. Impaired G6PD activity makes RBCs susceptible to hemolysis induced by oxidant challenges. Exercise-induced oxidant stress is a well-established concept in the field of physiology. Individuals with G6PD deficiency are recommended to refrain from strenuous exercise, as they are deemed to be at increased risk for hemolytic events in response to exercise-induced oxidant stress. While biochemically sound, this rationale has never been tested mechanistically, owing to ethical and technical limitations. As such, knowledge is limited regarding molecular impacts of exercise activity in G6PD deficient individuals. METHODS: Here we generated novel humanized mouse models carrying either (i) human canonical G6PD (normal activity), or two of the most common G6PD variants (ii) African (V68M - < 10% residual enzymatic activity) or (iii) Mediterranean (S188F - <1% residual enzymatic activity). We leveraged these mouse models to assess critical speed (CS), a functional measurement of the speed-to-duration relationship. To expand the molecular resolution of genetically associated adaptations to exercise, CS tests were combined with measurements of hemodynamics - including cardiac output, imaging (e.g., scanning electron microscopy of RBCs before and after exercise) and multi-omics (mass spectrometry-based metabolomics, lipidomics and proteomics) characterization of plasma, RBCs, and multiple tissue matrices including liver, heart, kidney, lung, spleen, and muscles (gastrocnemius and soleus). RESULTS: First of all, we confirmed a significant decrease in G6PD activity in RBCs from mice carrying the African and Mediterranean variants ( Figure 1). Unexpectedly, G6PD deficient mice showed ~10% faster CS compared to mice carrying the canonical human G6PD (p<0.0001 and p<0.02, Figure 1). While scanning electron microscopy showed differences before and after exercise, with accumulation of reversibly modified morphologies across all groups after the run, no significant differences were observed across groups. No significant changes in hemolysis markers were observed in plasma samples after exercise, neither at the metabolic (e.g., heme catabolites) or proteomics level (e.g., hemoglobin chains, RBC-derived proteins). Within hemodynamics, the hG6PD Med- mice are showing a higher efficiency in their cardiac function through cardiac function (p<0.005 - Figure 1). Pathway analysis of metabolomics data generated from RBC and plasma fractions showed significant decreases in arginine metabolism, increased glycolysis, and elevated Krebs cycle intermediates, in addition to expected differences in PPP. Specifically, in hG6PD A- RBC acylcarnitines accumulate after running. In hG6PD Med-, there are clear left ventricular differences highlighted in energy metabolism ( Figure 1). Multiomics analyses identified five major tissue clusters between heart, muscle, liver, kidney, and spleen, with G6PD function having the most significant metabolic impact on liver and soleus. RBC proteomics results highlighted deceased ferroportin (key iron transporter) in both the deficient groups, with markedly significant changes in the hG6PD Med- (p<0.0001). CONCLUSION: Novel humanized G6PD deficient mice maintain faster CS compared to mice. Omics approaches afford the opportunity to identify metabolic correlates to measurements of performance in relevant animal models, such as the CS model, which offer expanded mechanistic understanding of molecular bases of CS and the associated impact of G6PD deficiency. Our results not only challenge the assumption that exercise-induced oxidant stress predisposes the ~500M individuals with G6PD deficiency worldwide to increased hemolytic propensity, but rather indicate a metabolic advantage of functional polymorphisms to G6PD with respect to exercise performance, an observation that could contribute explaining the prevalence of such mutation in human populations.

Long-Read DNA Sequencing: Recent Advances and Remaining Challenges.

DNA sequencing has revolutionized medicine over recent decades. However, analysis of large structural variation and repetitive DNA, a hallmark of human genomes, has been limited by short-read technology, with read lengths of 100-300 bp. Long-read sequencing (LRS) permits routine sequencing of human DNA fragments tens to hundreds of kilobase pairs in size, using both real-time sequencing by synthesis and nanopore-based direct electronic sequencing. LRS permits analysis of large structural variation and haplotypic phasing in human genomes and has enabled the discovery and characterization of rare pathogenic structural variants and repeat expansions. It has also recently enabled the assembly of a complete, gapless human genome that includes previously intractable regions, such as highly repetitive centromeres and homologous acrocentric short arms. With the addition of protocols for targeted enrichment, direct epigenetic DNA modification detection, and long-range chromatin profiling, LRS promises to launch a new era of understanding of genetic diversity and pathogenic mutations in human populations.

Identifying rare variants inconsistent with identity‐by‐descent in population‐scale whole‐genome sequencing data

Abstract Analyses of genetic variation typically assume that rare variants within a population are inherited from a single common ancestral event identity‐by‐descent (IBD). However, there are genetic and technical processes through which rare variants in population genetic data may deviate from this simple evolutionary model, including recurrent mutations, gene conversions and genotyping error. All these processes can decrease the expected length of shared background haplotype surrounding a rare variant if that variant was inherited from a single event descending from a common ancestor. No method exists to computationally infer rare variants inconsistent with this simple model—denoted here as ‘IBD‐inconsistent’—using unphased population sequencing data. We hypothesized that the difference in shared haplotype background length can distinguish variants consistent and inconsistent with this simple IBD transmission population sequencing data without pedigree information. We implemented a Bayesian hierarchical model and used Gibbs sampling to estimate the posterior probability of IBD state for rare variants, using simulated recurrent mutations to demonstrate that our approach accurately distinguishes rare variants consistent and inconsistent with a simple IBD inheritance model. Applying our method to whole‐genome sequencing data from 3,621 human individuals in the UK10K consortium, we found that IBD‐inconsistent variants correlated with higher local mutation rates and genomic features like replication timing. Using a heuristic to categorize IBD‐inconsistent variants as gene conversions, we found that potential gene conversions had expected properties such as enriched local GC content. By identifying IBD‐inconsistent variants, we can better understand the spectrum of recent mutations in human populations, a source of genetic variation driving evolution and a key factor in understanding recent demographic history.

Presence and transmission of mitochondrial heteroplasmic mutations in human populations of European and African ancestry

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in 6745 maternal pairs of European (EA, n = 4718 pairs) and African (AA, n = 2027 pairs) Americans in whole blood. Mother-offspring pairs displayed the highest concordance rate, followed by sibling-sibling and more distantly-related maternal pairs. The allele fractions of concordant heteroplasmies exhibited high correlation (R2 = 0.8) between paired individuals. Discordant heteroplasmies were more likely to be in coding regions, be nonsynonymous or nonsynonymous-deleterious (p < 0.001). The number of deleterious heteroplasmies was significantly correlated with advancing age (20–44, 45–64, and ≥65 years, p-trend = 0.01). One standard deviation increase in heteroplasmic burden (i.e., the number of heteroplasmies carried by an individual) was associated with 0.17 to 0.26 (p < 1e − 23) standard deviation decrease in mtDNA copy number, independent of age. White blood cell count and differential count jointly explained 0.5% to 1.3% (p ≤ 0.001) variance in heteroplasmic burden. A genome-wide association and meta-analysis identified a region at 11p11.12 (top signal rs779031139, p = 2.0e − 18, minor allele frequency = 0.38) associated with the heteroplasmic burden. However, the 11p11.12 region is adjacent to a nuclear mitochondrial DNA (NUMT) corresponding to a 542 bp area of the D-loop. This region was no longer significant after excluding heteroplasmies within the 542 bp from the heteroplasmic burden. The discovery that blood mtDNA heteroplasmies were both inherited and somatic origins and that an increase in heteroplasmic burden was strongly associated with a decrease in average number of mtDNA copy number in blood are important findings to be considered in association studies of mtDNA with disease traits.

In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints.

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

Overactive Reproductive Axis Due to Fragile X Gene Mutation

Women carrying a pre-mutation or mutation of the Fragile X mental retardation gene (FMR1) comprise the largest portion of premature ovarian failure (POF) cases due to known genetic factors. FMR1 mutation causes Fragile X syndrome, the most common cause of inherited mental impairment. The mutation inhibits the expression of the fragile X mental retardation protein (FMRP), a ubiquitously expressed mRNA binding protein. The specific molecular mechanism(s) leading to premature ovarian failure in Fragile X carriers are not known. Here, we utilize the complete KO mouse model, to mimic the lack of FMRP in Fragile X mutations and analyze the hypothalamic-pituitary-gonadal axis to uncover causes of POF due to FMR1 mutation. Consistent with mutations in human population, KO females experience early cessation of reproductive function and stop having litters at 150 days of age, compared to controls that stop reproducing at 250 days of age. Since POF can be caused by either insufficient pool of primordial follicles or by increased recruitment in each cycle and early depletion, we analyzed ovaries at 3 weeks of age and determined that the FMR1 KO mice had the same number of primordial follicles when compared to the controls, suggesting that POF is not due to a deficit in primordial follicles. However, at 8 weeks of age, FMR1 KO ovaries had higher number of corpora lutea, and KO females had larger litters, indicating that FMR1 KO mice have more follicles recruited in each estrous cycle. FMR1 KO mice have higher FSH, which corresponds to the high FSH in women. Serum estradiol levels and inhibin b expression levels were unaffected by FMR1 mutation suggesting normally functioning negative feedback signals from the ovaries. Analyses of hypothalamic gene expression demonstrated elevated GnRH mRNA in KO mice. To further investigate alterations is hypothalamic protein levels, western blot analyses determined that FMR1 KO mice have higher levels of NMDAR1 and higher levels of GABAA receptor G2 subunit. Dual label immunofluorescence analyses revealed higher number of NMDAR1 and GABAA receptors specifically in GnRH neurons of FMR1 KO mice when compared to control, suggesting that GnRH neurons themselves are affected by FMR1 mutation. Given that both glutamate and GABA can activate GnRH neurons, alterations in the number of these receptors can potentially cause hyperactivity in the HPG axis at the hypothalamic level leading to elevated FSH and the subsequent POF. In summary, our results reveal a potential mechanism of premature ovarian failure in Fragile X mutation carriers.

Impact of regulatory light chain mutation K104E on the ATPase and motor properties of cardiac myosin.

Mutations in the cardiac myosin regulatory light chain (RLC, MYL2 gene) are known to cause inherited cardiomyopathies with variable phenotypes. In this study, we investigated the impact of a mutation in the RLC (K104E) that is associated with hypertrophic cardiomyopathy (HCM). Previously in a mouse model of K104E, older animals were found to develop cardiac hypertrophy, fibrosis, and diastolic dysfunction, suggesting a slow development of HCM. However, variable penetrance of the mutation in human populations suggests that the impact of K104E may be subtle. Therefore, we generated human cardiac myosin subfragment-1 (M2β-S1) and exchanged on either the wild type (WT) or K104E human ventricular RLC in order to assess the impact of the mutation on the mechanochemical properties of cardiac myosin. The maximum actin-activated ATPase activity and actin sliding velocities in the in vitro motility assay were similar in M2β-S1 WT and K104E, as were the detachment kinetic parameters, including the rate of ATP-induced dissociation and the ADP release rate constant. We also examined the mechanical performance of α-cardiac myosin extracted from transgenic (Tg) mice expressing human wild type RLC (Tg WT) or mutant RLC (Tg K104E). We found that α-cardiac myosin from Tg K104E animals demonstrated enhanced actin sliding velocities in the motility assay compared with its Tg WT counterpart. Furthermore, the degree of incorporation of the mutant RLC into α-cardiac myosin in the transgenic animals was significantly reduced compared with wild type. Therefore, we conclude that the impact of the K104E mutation depends on either the length or the isoform of the myosin heavy chain backbone and that the mutation may disrupt RLC interactions with the myosin lever arm domain.

Influence of Dominance and Drift on Lethal Mutations in Human Populations.

We consider disease-causing mutations that are lethal when homozygous. Lethality involves the very strongest form of negative selection, with the selection coefficient against the disease-carrying homozygote having its maximum value of unity. We determine results for the behavior of the frequency of a lethal allele in an effectively infinite population. This includes an estimate of the time it takes to achieve equilibrium, and a description of transient behavior associated with a sudden change in the fitness of the heterozygote. We determine analogous results for a finite population, showing that a lethal disease-causing allele needs to be described by a modified Wright-Fisher model, which deviates from the standard model, where selection coefficients are assumed small compared with 1. We show that a by-product of the dynamics, resulting from the absence of the disease-allele carrying homozygote in adults, is the general constraint that the frequency of the disease-causing allele cannot exceed . The results presented in this work should prove useful to a number of areas including analysis of lethal/near lethal mutations in Mendelian disorders and, in particular, for exploring how mutation-selection-drift balance explains the current spectrum of mutation frequencies in humans. While the number of empirical examples of overdominance in lethal genetic disorders is not large, relatively high observed heterozygote frequencies may be a hint of transient heterozygous advantage in nature. For lethal disorders with anomalous frequencies, such as cystic fibrosis and Tay-Sachs, our analysis lends further support to the role that transitory episodes of weak overdominance may play in the evolution of lethal mutations.

High-Throughput Mutational Screen Identifies Phenotypically Relevant Categories of Mutations in Fumarate Hydratase

Fumarate Hydratase (FH) is an enzyme of the citric acid (TCA) cycle that is responsible for reversibly catalysing the conversion between fumarate and malate. Deficiency in FH is linked to numerous syndromes in humans, but notably the buildup of the oncometabolite fumarate caused by mutational inactivation is a major cause of clear cell renal carcinoma. We sought to explore the mutational landscape of FH in silico, to predict the functional effects of many detected mutations, and categorise detected but un-characterised mutations in human populations. Through the use of homology modelling and mutational energy predicting tools such as Rosetta and Foldx we can accurately detect mutations and mutational hotspots with high disruptive capability. Furthermore, through performing molecular dynamics simulations we show that hinge regions of the protein can be stabilized or destabilized by mutations, with consequences on the binding affinity of the enzyme for its substrates. We can additionally categorise a large majority of mutations and potential mutations into functional groups, and predict which detected mutations in the human population are likely to be loss-of-function, and therefore predispose patients to clear cell renal carcinoma. We additionally link mutations from various functional groups to metabolomics data, and show that mutations from separate categories (destabilizing, binding-site affecting, and hinge-altering) alter cellular metabolic activity differently.

Impact of Regulatory Light Chain Mutation (K104E) on the Atpase and Motor Properties of Human Cardiac Myosin

Mutations in the cardiac myosin heavy chain (MYH7) as well as the essential (MYL3) and regulatory (MYL2) light chains are known to lead to cardiomyopathies with variable phenotypes, including hypertrophic (HCM) and dilated (DCM) cardiomyopathy. We investigated the impact of the HCM-associated regulatory light chain (RLC) mutation (K104E) on myosin ATPase kinetics and in vitro motility. We expressed and purified human cardiac myosin subfragment 1 (M2B-S1) and exchanged on the human ventricular RLC, with the mutant or WT RLC. A mouse model of K104E was found to cause cardiac hypertrophy, fibrosis, and diastolic dysfunction in older animals suggesting a slow development of HCM, while variable penetrance of the mutation in human populations suggest the impact of K104E may be subtle. We found that maximum actin-activated ATPase activity and the actin-dependence of ATPase were similar in K104E and WT. Actin gliding velocities in the in vitro motility assay were slightly increased in K104E. We found that the ADP release rate constant in the presence of actin also was slightly increased in K104E compared to WT. Our results suggest that HCM-associated RLC mutations may only have a minor impact on unloaded ATPase kinetics and in vitro actin gliding, which may suggest other factors could be altered by the mutation such as mechanical load and myosin regulation. Future experiments will address the impact of the mutation in mouse cardiac vs. human cardiac myosin as well as the impact of the mutation on load-dependent motility and the off state (super-relaxed state) of cardiac myosin.

What can a comparative genomics approach tell us about the pathogenicity of mtDNA mutations in human populations?

Mitochondrial disorders are heterogeneous, showing variable presentation and penetrance. Over the last three decades, our ability to recognize mitochondrial patients and diagnose these mutations, linking genotype to phenotype, has greatly improved. However, it has become increasingly clear that these strides in diagnostics have not benefited all population groups. Recent studies have demonstrated that patients from genetically understudied populations, in particular those of black African heritage, are less likely to receive a diagnosis of mtDNA disease. It has been suggested that haplogroup context might influence the presentation and penetrance of mtDNA disease; thus, the spectrum of mutations that are associated with disease in different populations. However, to date there is only one well‐established example of such an effect: the increased penetrance of two Leber's hereditary optic neuropathy mutations on a haplogroup J background. This paper conducted the most extensive investigation to date into the importance of haplogroup context on the pathogenicity of mtDNA mutations. We searched for proven human point mutations across 726 multiple sequence alignments derived from 33 non‐human species absent of disease. A total of 58 pathogenic point mutations arise in the sequences of these species. We assessed the sequence context and found evidence of population variants that could modulate the phenotypic expression of these point mutations masking the pathogenic effects seen in humans. This supports the theory that sequence context is influential in the presentation of mtDNA disease and has implications for diagnostic practices. We have shown that our current understanding of the pathogenicity of mtDNA point mutations, primarily built on studies of individuals with haplogroups HVUKTJ, will not present a complete picture. This will have the effect of creating a diagnostic inequality, whereby individuals who do not belong to these lineages are less likely to receive a genetic diagnosis.

“Genetic Load”

The term “genetic load” first emerged in a paper written in 1950 by the geneticist H. Muller. It is a mathematical model based on biological, social, political and ethical arguments describing the dramatic accumulation of disadvantageous mutations in human populations that will occur in modern societies if eugenic measures are not taken. The model describes how the combined actions of medical and social progress will supposedly impede natural selection and make genes of inferior quality likely to spread across populations – a process which in fine loads their progress. Genetic load is based on optimal fitness and emerges from a “typological view” of evolution. This model of evolution had previously, however, been invalidated by Robert Wright and Theodosius Dobzhansky who, as early as 1946, showed that polymorphism was the rule in natural populations. The blooming and persistence of the concept of genetic load, after its theoretical basis had already expired, are a historical puzzle. This persistence reveals the intricacy of science and policy-making in eugenic matters. The Canguilhemian concept of ‘scientific ideology’ (1988) is used along with the concept of ‘immutable mobile’ (Latour 1986) and compared with the concept of ‘co-production’ (Jasanoff 1998), to provide complementary perspectives on this complex phenomenon.

The population genetics of human disease: The case of recessive, lethal mutations.

Do the frequencies of disease mutations in human populations reflect a simple balance between mutation and purifying selection? What other factors shape the prevalence of disease mutations? To begin to answer these questions, we focused on one of the simplest cases: recessive mutations that alone cause lethal diseases or complete sterility. To this end, we generated a hand-curated set of 417 Mendelian mutations in 32 genes reported to cause a recessive, lethal Mendelian disease. We then considered analytic models of mutation-selection balance in infinite and finite populations of constant sizes and simulations of purifying selection in a more realistic demographic setting, and tested how well these models fit allele frequencies estimated from 33,370 individuals of European ancestry. In doing so, we distinguished between CpG transitions, which occur at a substantially elevated rate, and three other mutation types. Intriguingly, the observed frequency for CpG transitions is slightly higher than expectation but close, whereas the frequencies observed for the three other mutation types are an order of magnitude higher than expected, with a bigger deviation from expectation seen for less mutable types. This discrepancy is even larger when subtle fitness effects in heterozygotes or lethal compound heterozygotes are taken into account. In principle, higher than expected frequencies of disease mutations could be due to widespread errors in reporting causal variants, compensation by other mutations, or balancing selection. It is unclear why these factors would have a greater impact on disease mutations that occur at lower rates, however. We argue instead that the unexpectedly high frequency of disease mutations and the relationship to the mutation rate likely reflect an ascertainment bias: of all the mutations that cause recessive lethal diseases, those that by chance have reached higher frequencies are more likely to have been identified and thus to have been included in this study. Beyond the specific application, this study highlights the parameters likely to be important in shaping the frequencies of Mendelian disease alleles.

Base-Biased Evolution of Disease-Associated Mutations in the Human Genome.

Understanding the evolution of disease-associated mutations is fundamental to analyze pathogenetics of diseases. Mutation, recombination (by GC-biased gene conversion, gBGC), and selection have been known to shape the evolution of disease-associated mutations, but how these evolutionary forces work together is still an open question. In this study, we analyzed several human large-scale datasets (1000 Genomes, ESP6500, ExAC and ClinVar), and found that base-biased mutagenesis generates more GC→AT than AT→GC mutations, while gBGC promotes the fixation of AT→GC mutations to balance the impact of base-biased mutation on genome. Due to this effect of gBGC, purifying selection removes more deleterious AT→GC mutations than GC→AT from population, but many high-frequency (fixed and nearly fixed) deleterious AT→GC mutations are remained possibly due to high genetic load. As a special subset, disease-associated mutations follow this evolutionary rule, in which disease-associated GC→AT mutations are more enriched in rare mutations compared with AT→GC, while disease-associated AT→GC are more enriched in mutations with high frequency. Thus, we presented a base-biased evolutionary framework that explains the base-biased generation and accumulation of disease-associated mutations in human populations.

Why individual thermo sensation and pain perception varies? Clue of disruptive mutations in TRPVs from 2504 human genome data

ABSTRACTEvery individual varies in character and so do their sensory functions and perceptions. The molecular mechanism and the molecular candidates involved in these processes are assumed to be similar if not same. So far several molecular factors have been identified which are fairly conserved across the phylogenetic tree and are involved in these complex sensory functions. Among all, members belonging to Transient Receptor Potential (TRP) channels have been widely characterized for their involvement in thermo-sensation. These include TRPV1 to TRPV4 channels which reveal complex thermo-gating behavior in response to changes in temperature. The molecular evolution of these channels is highly correlative with the thermal response of different species. However, recent 2504 human genome data suggest that these thermo-sensitive TRPV channels are highly variable and carry possible deleterious mutations in human population. These unexpected findings may explain the individual differences in terms of complex sensory functions.

Recombination affects accumulation of damaging and disease-associated mutations in human populations.

Many decades of theory have demonstrated that, in non-recombining systems, slightly deleterious mutations accumulate non-reversibly, potentially driving the extinction of many asexual species. Non-recombining chromosomes in sexual organisms are thought to have degenerated in a similar fashion; however, it is not clear the extent to which damaging mutations accumulate along chromosomes with highly variable rates of crossing over. Using high-coverage sequencing data from over 1,400 individuals in the 1000 Genomes and CARTaGENE projects, we show that recombination rate modulates the distribution of putatively deleterious variants across the entire human genome. Exons in regions of low recombination are significantly enriched for deleterious and disease-associated variants, a signature varying in strength across worldwide human populations with different demographic histories. Regions with low recombination rates are enriched for highly conserved genes with essential cellular functions and show an excess of mutations with demonstrated effects on health, a phenomenon likely affecting disease susceptibility in humans.

Distinguishing neutral from deleterious mutations in growing populations

Distinguishing neutral from deleterious mutations in growing populations

Molecular Phylogeography of a Human Autosomal Skin Color Locus Under Natural Selection

Divergent natural selection caused by differences in solar exposure has resulted in distinctive variations in skin color between human populations. The derived light skin color allele of the SLC24A5 gene, A111T, predominates in populations of Western Eurasian ancestry. To gain insight into when and where this mutation arose, we defined common haplotypes in the genomic region around SLC24A5 across diverse human populations and deduced phylogenetic relationships between them. Virtually all chromosomes carrying the A111T allele share a single 78-kb haplotype that we call C11, indicating that all instances of this mutation in human populations share a common origin. The C11 haplotype was most likely created by a crossover between two haplotypes, followed by the A111T mutation. The two parental precursor haplotypes are found from East Asia to the Americas but are nearly absent in Africa. The distributions of C11 and its parental haplotypes make it most likely that these two last steps occurred between the Middle East and the Indian subcontinent, with the A111T mutation occurring after the split between the ancestors of Europeans and East Asians.