Population Growth Inflates the Per-Individual Number of Deleterious Mutations and Reduces Their Mean Effect

This study addresses the question of how purifying selection operates during recent rapid population growth such as has been experienced by human populations. This is not a straightforward problem because the human population is not at equilibrium: population genetics predicts that, on the one hand, the efficacy of natural selection increases as population size increases, eliminating ever more weakly deleterious variants; on the other hand, a larger number of deleterious mutations will be introduced into the population and will be more likely to increase in their number of copies as the population grows. To understand how patterns of human genetic variation have been shaped by the interaction of natural selection and population growth, we examined the trajectories of mutations with varying selection coefficients, using computer simulations. We observed that while population growth dramatically increases the number of deleterious segregating sites in the population, it only mildly increases the number carried by each individual. Our simulations also show an increased efficacy of natural selection, reflected in a higher fraction of deleterious mutations eliminated at each generation and a more efficient elimination of the most deleterious ones. As a consequence, while each individual carries a larger number of deleterious alleles than expected in the absence of growth, the average selection coefficient of each segregating allele is less deleterious. Combined, our results suggest that the genetic risk of complex diseases in growing populations might be distributed across a larger number of more weakly deleterious rare variants.     

Increased variation in mtDNA in patients with familial sensorineural hearing impairment

Analyses of mitochondrial DNA (mtDNA) sequences have revealed non-neutral patterns, suggesting that many amino acid mutations in animal mtDNA may be mildly deleterious, but this has not been verified in human clinical series. Since sensorineural hearing impairment (SNHI) is a common manifestation in many of the syndromes caused by mutations in mtDNA, this may be regarded as the phenotype of choice in attempts to detect mutations that may have a mildly deleterious effect on mitochondrial function. We selected 32 subjects from among 117 unrelated SNHI patients with SNHI in maternal relatives by means of family history, determined the entire coding region sequence of mtDNA and compared the sequence variation with that in 32 haplogroup-matched controls taken at random from 192 Finnish sequences. The 32 control sequences differed from the remaining 160 sequences by 36±9 substitutions (mean ± SD), while the difference for the 32 patients was 58±4 substitutions (P=0.005 for difference; Wilcoxon signed rank test). Differences were also found in the number of new haplotypes and new non-synonymous mutations or mutations in tRNA or rRNA genes. A total of 12 rare mtDNA variants were detected in the patients, and only 3 of these were considered to be neutral in effect. It is proposed that increased sequence variation in mtDNA may be a genetic risk factor for SNHI, and the increased frequency of rare haplotypes in these patients points to the presence of mildly deleterious mutations in mtDNA.     

Population Analysis of Large Copy Number Variants and Hotspots of Human Genetic Disease

Copy number variants (CNVs) contribute to human genetic and phenotypic diversity. However, the distribution of larger CNVs in the general population remains largely unexplored. We identify large variants in ~2500 individuals by using Illumina SNP data, with an emphasis on “hotspots” prone to recurrent mutations. We find variants larger than 500 kb in 5%–10% of individuals and variants greater than 1 Mb in 1%–2%. In contrast to previous studies, we find limited evidence for stratification of CNVs in geographically distinct human populations. Importantly, our sample size permits a robust distinction between truly rare and polymorphic but low-frequency copy number variation. We find that a significant fraction of individual CNVs larger than 100 kb are rare and that both gene density and size are strongly anticorrelated with allele frequency. Thus, although large CNVs commonly exist in normal individuals, which suggests that size alone can not be used as a predictor of pathogenicity, such variation is generally deleterious. Considering these observations, we combine our data with published CNVs from more than 12,000 individuals contrasting control and neurological disease collections. This analysis identifies known disease loci and highlights additional CNVs (e.g., 3q29, 16p12, and 15q25.2) for further investigation. This study provides one of the first analyses of large, rare (0.1%–1%) CNVs in the general population, with insights relevant to future analyses of genetic disease.     

Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10⁻⁸) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10⁻¹¹⁷). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10⁻⁴), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.     

A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.     

Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel

Several large ongoing initiatives that profit from next-generation sequencing technologies have driven--and in coming years will continue to drive--the emergence of long catalogs of missense single-nucleotide variants (SNVs) in the human genome. As a consequence, researchers have developed various methods and their related computational tools to classify these missense SNVs as probably deleterious or probably neutral polymorphisms. The outputs produced by each of these computational tools are of different natures and thus difficult to compare and integrate. Taking advantage of the possible complementarity between different tools might allow more accurate classifications. Here we propose an effective approach to integrating the output of some of these tools into a unified classification; this approach is based on a weighted average of the normalized scores of the individual methods (WAS). (In this paper, the approach is illustrated for the integration of five tools.) We show that this WAS outperforms each individual method in the task of classifying missense SNVs as deleterious or neutral. Furthermore, we demonstrate that this WAS can be used not only for classification purposes (deleterious versus neutral mutation) but also as an indicator of the impact of the mutation on the functionality of the mutant protein. In other words, it may be used as a deleteriousness score of missense SNVs. Therefore, we recommend the use of this WAS as a consensus deleteriousness score of missense mutations (Condel).     

Muller's ratchet and the pattern of variation at a neutral locus

The levels and patterns of variation at a neutral locus are analyzed in a haploid asexual population undergoing accumulation of deleterious mutations due to Muller's ratchet. We find that the movement of Muller's ratchet can be associated with a considerable reduction in genetic diversity below classical neutral expectation. The extent to which variability is reduced is a function of the deleterious mutation rate, the fitness effects of the mutations, and the population size. Approximate analytical expressions for the expected genetic diversity are compared with simulation results under two different models of deleterious mutations: a model where all deleterious mutations have equal effects and a model where there are two classes of deleterious mutations. We also find that Muller's ratchet can produce a considerable distortion in the neutral frequency spectrum toward an excess of rare variants.     

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some nonequilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We show an application of our method to the UK10K phased haplotype dataset of individuals.     

Five reasons why inbreeding may have considerable effect on post-reproductive human health

As the genetic architecture of common complex diseases of late onset is emerging through intensive research, it is intriguing to assess the predicted effect of inbreeding on those diseases. In this paper, we propose five reasons why we believe inbreeding may have a considerable effect on post-reproductive human health. (i) The joint effect of inbreeding depression on all polygenic quantitative phenotypes that confer risk for late-onset diseases is predicted to be multiplicative rather than additive. (ii) The "genetic load" of rare "Mendelian" variants with large deleterious effects in post-reproductive adults is unknown, but could be much greater than expected as these variants were invisible to selection through human history. (iii) Deleterious effects resulting from autozygosity in hundreds of affected rare recessive variants of small effect under common disease/rare variant (CD/RV) hypothesis could result in epistatic effects that could jointly impair capacity to compensate against environmental risks. (iv) Heterozygote advantage in loci under balancing selection could be reduced by inbreeding. (v) Published empirical evidence in animals and humans consistently report large inbreeding effects on late-onset traits. Since inbreeding is common in many populations and the effects of inbreeding depression could substantially contribute to disease burden and reduced life expectancy we believe there is now a clear need for further genetic epidemiological research in humans to investigate this issue.     

Selective Strolls: Fixation and Extinction in Diploids Are Slower for Weakly Selected Mutations Than for Neutral Ones

In finite populations, an allele disappears or reaches fixation due to two main forces, selection and drift. Selection is generally thought to accelerate the process: a selected mutation will reach fixation faster than a neutral one, and a disadvantageous one will quickly disappear from the population. We show that even in simple diploid populations, this is often not true. Dominance and recessivity unexpectedly slow down the evolutionary process for weakly selected alleles. In particular, slightly advantageous dominant and mildly deleterious recessive mutations reach fixation slightly more slowly than neutral ones (at most 5%). This phenomenon determines genetic signatures opposite to those expected under strong selection, such as increased instead of decreased genetic diversity around the selected site. Furthermore, we characterize a new phenomenon: mildly deleterious recessive alleles, thought to represent a wide fraction of newly arising mutations, on average survive in a population slightly longer than neutral ones, before getting lost. Consequently, these mutations are on average slightly older than neutral ones, in contrast with previous expectations. Furthermore, they slightly increase the amount of weakly deleterious polymorphisms, as a consequence of the longer unconditional sojourn times compared to neutral mutations.     

Deleterious- and Disease-Allele Prevalence in Healthy Individuals: Insights from Current Predictions,Mutation Databases,and Population-Scale Resequencing

We have assessed the numbers of potentially deleterious variants in the genomes of apparently healthy humans by using (1) low-coverage whole-genome sequence data from 179 individuals in the 1000 Genomes Pilot Project and (2) current predictions and databases of deleterious variants. Each individual carried 281–515 missense substitutions, 40–85 of which were homozygous, predicted to be highly damaging. They also carried 40–110 variants classified by the Human Gene Mutation Database (HGMD) as disease-causing mutations (DMs), 3–24 variants in the homozygous state, and many polymorphisms putatively associated with disease. Whereas many of these DMs are likely to represent disease-allele-annotation errors, between 0 and 8 DMs (0–1 homozygous) per individual are predicted to be highly damaging, and some of them provide information of medical relevance. These analyses emphasize the need for improved annotation of disease alleles both in mutation databases and in the primary literature; some HGMD mutation data have been recategorized on the basis of the present findings, an iterative process that is both necessary and ongoing. Our estimates of deleterious-allele numbers are likely to be subject to both overcounting and undercounting. However, our current best mean estimates of ∼400 damaging variants and ∼2 bona fide disease mutations per individual are likely to increase rather than decrease as sequencing studies ascertain rare variants more effectively and as additional disease alleles are discovered.     

Rare variants regulate expression of nearby individual genes in multiple tissues

The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses. However, identifying individual genes whose expression is regulated by rare variants has been challenging due to the relatively small sample size of expression quantitative trait loci studies and statistical approaches not optimized to detect the effects of rare variants. In this study, we analyze whole-genome sequencing and RNA-seq data of 681 European individuals collected for the Genotype-Tissue Expression (GTEx) project (v8) to identify individual genes in 49 human tissues whose expression is regulated by rare variants. To improve statistical power, we develop an approach based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. Using GTEx data, we identify many genes regulated by rare variants, and some of them are only regulated by rare variants and not by common variants. We also find that genes regulated by rare variants are enriched for expression outliers and disease-causing genes. These results suggest the regulatory effects of rare variants, which would be important in interpreting associations of rare variants with complex traits.     

Estimating the distribution of selection coefficients from phylogenetic data using sitewise mutation-selection models

Estimation of the distribution of selection coefficients of mutations is a long-standing issue in molecular evolution. In addition to population-based methods, the distribution can be estimated from DNA sequence data by phylogenetic-based models. Previous models have generally found unimodal distributions where the probability mass is concentrated between mildly deleterious and nearly neutral mutations. Here we use a sitewise mutation-selection phylogenetic model to estimate the distribution of selection coefficients among novel and fixed mutations (substitutions) in a data set of 244 mammalian mitochondrial genomes and a set of 401 PB2 proteins from influenza. We find a bimodal distribution of selection coefficients for novel mutations in both the mitochondrial data set and for the influenza protein evolving in its natural reservoir, birds. Most of the mutations are strongly deleterious with the rest of the probability mass concentrated around mildly deleterious to neutral mutations. The distribution of the coefficients among substitutions is unimodal and symmetrical around nearly neutral substitutions for both data sets at adaptive equilibrium. About 0.5% of the nonsynonymous mutations and 14% of the nonsynonymous substitutions in the mitochondrial proteins are advantageous, with 0.5% and 24% observed for the influenza protein. Following a host shift of influenza from birds to humans, however, we find among novel mutations in PB2 a trimodal distribution with a small mode of advantageous mutations.     

Bioenergetics in human evolution and disease: implications for the origins of biological complexity and the missing genetic variation of common diseases

Two major inconsistencies exist in the current neo-Darwinian evolutionary theory that random chromosomal mutations acted on by natural selection generate new species. First, natural selection does not require the evolution of ever increasing complexity, yet this is the hallmark of biology. Second, human chromosomal DNA sequence variation is predominantly either neutral or deleterious and is insufficient to provide the variation required for speciation or for predilection to common diseases. Complexity is explained by the continuous flow of energy through the biosphere that drives the accumulation of nucleic acids and information. Information then encodes complex forms. In animals, energy flow is primarily mediated by mitochondria whose maternally inherited mitochondrial DNA (mtDNA) codes for key genes for energy metabolism. In mammals, the mtDNA has a very high mutation rate, but the deleterious mutations are removed by an ovarian selection system. Hence, new mutations that subtly alter energy metabolism are continuously introduced into the species, permitting adaptation to regional differences in energy environments. Therefore, the most phenotypically significant gene variants arise in the mtDNA, are regional, and permit animals to occupy peripheral energy environments where rarer nuclear DNA (nDNA) variants can accumulate, leading to speciation. The neutralist–selectionist debate is then a consequence of mammals having two different evolutionary strategies: a fast mtDNA strategy for intra-specific radiation and a slow nDNA strategy for speciation. Furthermore, the missing genetic variation for common human diseases is primarily mtDNA variation plus regional nDNA variants, both of which have been missed by large, inter-population association studies.     

Are rare variants responsible for susceptibility to complex diseases?

Little is known about the nature of genetic variation underlying complex diseases in humans. One popular view proposes that mapping efforts should focus on identification of susceptibility mutations that are relatively old and at high frequency. It is generally assumed-at least for modeling purposes-that selection against complex disease mutations is so weak that it can be ignored. In this article, I propose an explicit model for the evolution of complex disease loci, incorporating mutation, random genetic drift, and the possibility of purifying selection against susceptibility mutations. I show that, for the most plausible range of mutation rates, neutral susceptibility alleles are unlikely to be at intermediate frequencies and contribute little to the overall genetic variance for the disease. Instead, it seems likely that the bulk of genetic variance underlying diseases is due to loci where susceptibility mutations are mildly deleterious and where there is a high overall mutation rate to the susceptible class. At such loci, the total frequency of susceptibility mutations may be quite high, but there is likely to be extensive allelic heterogeneity at many of these loci. I discuss some practical implications of these results for gene mapping efforts.     

Rare mutations in XRCC2 increase the risk of breast cancer

An exome-sequencing study of families with multiple breast-cancer-affected individuals identified two families with XRCC2 mutations, one with a protein-truncating mutation and one with a probably deleterious missense mutation. We performed a population-based case-control mutation-screening study that identified six probably pathogenic coding variants in 1,308 cases with early-onset breast cancer and no variants in 1,120 controls (the severity grading was p < 0.02). We also performed additional mutation screening in 689 multiple-case families. We identified ten breast-cancer-affected families with protein-truncating or probably deleterious rare missense variants in XRCC2. Our identification of XRCC2 as a breast cancer susceptibility gene thus increases the proportion of breast cancers that are associated with homologous recombination-DNA-repair dysfunction and Fanconi anemia and could therefore benefit from specific targeted treatments such as PARP (poly ADP ribose polymerase) inhibitors. This study demonstrates the power of massively parallel sequencing for discovering susceptibility genes for common, complex diseases.     

Excess of rare amino acid polymorphisms in the Toll-like receptor 4 in humans

The Toll-like receptor 4 protein acts as the transducing subunit of the lipopolysaccharide receptor complex and assists in the detection of Gram-negative pathogens within the mammalian host. Several lines of evidence support the view that variation at the TLR4 locus may alter host susceptibility to Gram-negative infection or the outcome of infection. Here, we surveyed TLR4 sequence variation in the complete coding region (2.4 kb) in 348 individuals from several population samples; in addition, a subset of the individuals was surveyed at 1.1 kb of intronic sequence. More than 90% of the chromosomes examined encoded the same structural isoform of TLR4, while the rest harbored 12 rare amino acid variants. Conversely, the variants at silent sites (intronic and synonymous positions) occur at both low and high frequencies and are consistent with a neutral model of mutation and random drift. The spectrum of allele frequencies for amino acid variants shows a significant skew toward lower frequencies relative to both the neutral model and the pattern observed at linked silent sites. This is consistent with the hypothesis that weak purifying selection acted on TLR4 and that most mutations affecting TLR4 protein structure have at least mildly deleterious phenotypic effects. These results may imply that genetic variants contributing to disease susceptibility occur at low frequencies in the population and suggest strategies for optimizing the design of disease-mapping studies.     

Whole-genome sequencing in French Canadians from Quebec

Genome-wide association studies (GWAS) have had a tremendous success in the identification of common DNA sequence variants associated with complex human diseases and traits. However, because of their design, GWAS are largely inappropriate to characterize the role of rare and low-frequency DNA variants on human phenotypic variation. Rarer genetic variation is geographically more restricted, supporting the need for local whole-genome sequencing (WGS) efforts to study these variants in specific populations. Here, we present the first large-scale low-pass WGS of the French-Canadian population. Specifically, we sequenced at ~5.6× coverage the whole genome of 1970 French Canadians recruited by the Montreal Heart Institute Biobank and identified 29 million bi-allelic variants (31 % novel), including 19 million variants with a minor allele frequency (MAF) <0.5 %. Genotypes from the WGS data are highly concordant with genotypes obtained by exome array on the same individuals (99.8 %), even when restricting this analysis to rare variants (MAF <0.5, 99.9 %) or heterozygous sites (98.9 %). To further validate our data set, we showed that we can effectively use it to replicate several genetic associations with myocardial infarction risk and blood lipid levels. Furthermore, we analyze the utility of our WGS data set to generate a French-Canadian-specific imputation reference panel and to infer population structure in the Province of Quebec. Our results illustrate the value of low-pass WGS to study the genetics of human diseases in the founder French-Canadian population.     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.