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Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual’s genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations., Low frequency coding single-nucleotide variants (SNVs) are predicted to disproportionately affect protein function. Here, the authors evaluate 2,009 missense SNVs across 2,185 protein-protein interactions using yeast two-hybrid and protein complementation assays and find that disruptive SNVs often occur in disease-associated genes.

The gradual accumulation of mutations by any of a number of mutational processes is a major driving force of divergence and evolution. Here, we investigate a potentially novel mutational process that is based on the activity of members of the AID/APOBEC family of deaminases. This gene family has been recently shown to introduce—in multiple types of cancer—enzyme-induced clusters of co-occurring somatic mutations caused by cytosine deamination. Going beyond somatic mutations, we hypothesized that APOBEC3—following its rapid expansion in primates—can introduce unique germline mutation clusters that can play a role in primate evolution. In this study, we tested this hypothesis by performing a comprehensive comparative genomic screen for APOBEC3-induced mutagenesis patterns across different hominids. We detected thousands of mutation clusters introduced along primate evolution which exhibit features that strongly fit the known patterns of APOBEC3G mutagenesis. These results suggest that APOBEC3G-induced mutations have contributed to the evolution of all genomes we studied. This is the first indication of site-directed, enzyme-induced genome evolution, which played a role in the evolution of both modern and archaic humans. This novel mutational mechanism exhibits several unique features, such as its higher tendency to mutate transcribed regions and regulatory elements and its ability to generate clusters of concurrent point mutations that all occur in a single generation. Our discovery demonstrates the exaptation of an anti-viral mechanism as a new source of genomic variation in hominids with a strong potential for functional consequences.

Long chain polyunsaturated fatty acids (LCPUFA) are bioactive components of membrane phospholipids and serve as substrates for signaling molecules. LCPUFA can be obtained directly from animal foods or synthesized endogenously from 18 carbon precursors via the FADS2 coded enzyme. Vegans rely almost exclusively on endogenous synthesis to generate LCPUFA and we hypothesized that an adaptive genetic polymorphism would confer advantage. The rs66698963 polymorphism, a 22-bp insertion–deletion within FADS2, is associated with basal FADS1 expression, and coordinated induction of FADS1 and FADS2 in vitro. Here, we determined rs66698963 genotype frequencies from 234 individuals of a primarily vegetarian Indian population and 311 individuals from the US. A much higher I/I genotype frequency was found in Indians (68%) than in the US (18%). Analysis using 1000 Genomes Project data confirmed our observation, revealing a global I/I genotype of 70% in South Asians, 53% in Africans, 29% in East Asians, and 17% in Europeans. Tests based on population divergence, site frequency spectrum, and long-range haplotype consistently point to positive selection encompassing rs66698963 in South Asian, African, and some East Asian populations. Basal plasma phospholipid arachidonic acid (ARA) status was 8% greater in I/I compared with D/D individuals. The biochemical pathway product–precursor difference, ARA minus linoleic acid, was 31% and 13% greater for I/I and I/D compared with D/D, respectively. This study is consistent with previous in vitro data suggesting that the insertion allele enhances n-6 LCPUFA synthesis and may confer an adaptive advantage in South Asians because of the traditional plant-based diet practice.

In eutherian mammals, X-linked gene expression is normalized between XX females and XY males through the process of X chromosome inactivation (XCI). XCI results in silencing of transcription from one ChrX homolog per female cell. However, approximately 25% of human ChrX genes escape XCI to some extent and exhibit biallelic expression in females. The evolutionary basis of this phenomenon is not entirely clear, but high sequence conservation of XCI escapers suggests that purifying selection may directly or indirectly drive XCI escape at these loci. One hypothesis is that this signal results from contributions to developmental and physiological sex differences, but presently there is limited evidence supporting this model in humans. Another potential driver of this signal is selection for high and/or broad gene expression in both sexes, which are strong predictors of reduced nucleotide substitution rates in mammalian genes. Here, we compared purifying selection and gene expression patterns of human XCI escapers with those of X-inactivated genes in both sexes. When we accounted for the functional status of each ChrX gene's Y-linked homolog (or "gametolog"), we observed that XCI escapers exhibit greater degrees of purifying selection in the human lineage than X-inactivated genes, as well as higher and broader gene expression than X-inactivated genes across tissues in both sexes. These results highlight a significant role for gene expression in both sexes in driving purifying selection on XCI escapers, and emphasize these genes' potential importance in human disease.

The age of polymorphic alleles in humans is often estimated from population genetic patterns in extant human populations, such as allele frequencies, linkage disequilibrium, and rate of mutations. Ancient DNA can improve the accuracy of such estimates, as well as facilitate testing the validity of demographic models underlying many population genetic methods. Specifically, the presence of an allele in a genome derived from an ancient sample testifies that the allele is at least as old as that sample. In this study, we consider a common method for estimating allele age based on allele frequency as applied to variants from the US National Institutes of Health (NIH) Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project. We view these estimates in the context of the presence or absence of each allele in the genomes of the 5300 year old Tyrolean Iceman, Ötzi, and of the 50,000 year old Altai Neandertal. Our results illuminate the accuracy of these estimates and their sensitivity to demographic events that were not included in the model underlying age estimation. Specifically, allele presence in the Iceman genome provides a good fit of allele age estimates to the expectation based on the age of that specimen. The equivalent based on the Neandertal genome leads to a poorer fit. This is likely due in part to the older age of the Neandertal and the older time of the split between modern humans and Neandertals, but also due to gene flow from Neandertals to modern humans not being considered in the underlying demographic model. Thus, the incorporation of ancient DNA can improve allele age estimation, demographic modeling, and tests of natural selection. Our results also point to the importance of considering a more diverse set of ancient samples for understanding the geographic and temporal range of individual alleles.

XWAS is a new software suite for the analysis of the X chromosome in association studies and similar genetic studies. The X chromosome plays an important role in human disease and traits of many species, especially those with sexually dimorphic characteristics. Special attention needs to be given to its analysis due to the unique inheritance pattern, which leads to analytical complications that have resulted in the majority of genome-wide association studies (GWAS) either not considering X or mishandling it with toolsets that had been designed for non-sex chromosomes. We hence developed XWAS to fill the need for tools that are specially designed for analysis of X. Following extensive, stringent, and X-specific quality control, XWAS offers an array of statistical tests of association, including: 1) the standard test between a SNP (single nucleotide polymorphism) and disease risk, including after first stratifying individuals by sex, 2) a test for a differential effect of a SNP on disease between males and females, 3) motivated by X-inactivation, a test for higher variance of a trait in heterozygous females as compared with homozygous females, and 4) for all tests, a version that allows for combining evidence from all SNPs across a gene. We applied the toolset analysis pipeline to 16 GWAS datasets of immune-related disorders and 7 risk factors of coronary artery disease, and discovered several new X-linked genetic associations. XWAS will provide the tools and incentive for others to incorporate the X chromosome into GWAS and similar studies in any species with an XX/XY system, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.

A majority of mitochondrial DNA (mtDNA) mutations reported to be implicated in diseases are heteroplasmic, a status with coexisting mtDNA variants in a single cell. Quantifying the prevalence of mitochondrial heteroplasmy and its pathogenic effect in healthy individuals could further our understanding of its possible roles in various diseases. A total of 1,085 human individuals from 14 global populations have been sequenced by the 1000 Genomes Project to a mean coverage of ∼2,000× on mtDNA. Using a combination of stringent thresholds and a maximum-likelihood method to define heteroplasmy, we demonstrated that ∼90% of the individuals carry at least one heteroplasmy. At least 20% of individuals harbor heteroplasmies reported to be implicated in disease. Mitochondrial heteroplasmy tend to show high pathogenicity, and is significantly overrepresented in disease-associated loci. Consistent with their deleterious effect, heteroplasmies with derived allele frequency larger than 60% within an individual show a significant reduction in pathogenicity, indicating the action of purifying selection. Purifying selection on heteroplasmies can also be inferred from nonsynonymous and synonymous heteroplasmy comparison and the unfolded site frequency spectra for different functional sites in mtDNA. Nevertheless, in comparison with population polymorphic mtDNA mutations, the purifying selection is much less efficient in removing heteroplasmic mutations. The prevalence of mitochondrial heteroplasmy with high pathogenic potential in healthy individuals, along with the possibility of these mutations drifting to high frequency inside a subpopulation of cells across lifespan, emphasizes the importance of managing mitochondrial heteroplasmy to prevent disease progression.

Objective Individuals with mixed dyslipidemia have elevated triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), and increased risk for coronary disease. Fibrate therapy is commonly used to lower TG and increase HDL-C. Common genetic variants are known to affect the response to fibrate therapy. We sought to identify rare genetic variants (frequency ≤ 1%) in genes involved in TG and HDL-C metabolism that affect the response to fenofibric acid (FA) therapy. Methods Four genes with a major role in HDL-C and TG metabolism APOA1 , APOC2 , APOC-III and LPL were sequenced in 2385 participants with mixed dyslipidemia in a randomized, double-blind, active-controlled study comparing therapy with FA alone, in combination with statins, or statin alone. Rare variants collapsing or SKAT methods were used for the analysis. Results Synonymous rare variants in the LPL gene were significantly associated with absolute HDL-C change ( P = 9 × 10 −4 ) and TG percent change ( P = 6.76 × 10 −4 ) in those treated with FA only. Participants with these rare variants had a 2 mg/dL increase in HDL-C and 39 mg/dL decrease in TG as compared to 6.2 mg/dL increase in HDL-C and 100 mg/dL decrease in TG in those without these variants. Rare variants in the APOC-III gene were associated with a modest 3 mg/dL less reduction in APOB ( P = 8.72 × 10 −4 ) in those receiving FA and statin. Conclusion In individuals with mixed dyslipidemia rare synonymous variants within LPL gene were associated with attenuated response to FA therapy while APOCIII rare variants were associated with a modest effect on APOB response to FA-statin therapy. These results should be replicated in a similar clinical trial for further confirmation.

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

Autoimmune diseases often share common susceptibility genes. Most genetic variants associated with susceptibility to systemic lupus erythematosus are also associated with other autoimmune diseases. The X-linked variant rs2294020 is positioned in exon 7 of the CCDC22 gene. The encoded protein functions in the regulation of NF-kB, a master regulator in immune response. The aim of this study is to investigate whether the rs2294020 polymorphism may be a general susceptibility factor for autoimmunity. We evaluated case-control association between the occurrence of rs2294020 and different autoimmune diseases, including new data for systemic lupus erythematosus and previous genome-wide association studies (GWAS) (though most did not analyse the X chromosome) of psoriasis, celiac disease, Crohn’s disease, ulcerative colitis, multiple sclerosis, vitiligo, type-1 diabetes, rheumatoid arthritis, and ankylosing spondylitis. Cases from patients affected by amyotrophic lateral sclerosis and type-2 diabetes were also included in the study. We detected nominal significant associations of rs2294020 with systemic lupus erythematosus (additive model test: p=0.01), vitiligo (p =0.016), psoriasis (p =0.038), and in only one of two studies of multiple sclerosis (p =0.03). Our results suggest that rs2294020 is associated with the risk of several autoimmune diseases in European populations, specifically with diseases that present themselves, among else, in the skin.

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.

Earlier this year, we described an analysis of mitochondrial DNA (mtDNA) variants in myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) patients and healthy controls. We reported that there was no significant association of haplogroups or singe nucleotide polymorphisms (SNPs) with disease status. Nevertheless, a commentary about our paper appeared (Finsterer and Zarrouk-Mahjoub. J Transl Med14:182, 2016) that criticized the association of mtDNA haplogroups with ME/CFS, a conclusion that was absent from our paper. The aforementioned commentary also demanded experiments that were outside of the scope of our study, ones that we had suggested as follow-up studies. Because they failed to consult a published and cited report describing the cohorts we studied, the authors also cast aspersions on the method of selection of cases for inclusion. We reiterate that we observed statistically significant association of mtDNA variants with particular symptoms and their severity, though we observed no association with disease status.

The advent of next-generation sequencing technology has allowed the collection of vast amounts of genetic variation data. A recurring discovery from studying larger and larger samples of individuals had been the extreme, previously unexpected, excess of very rare genetic variants, which has been shown to be mostly due to the recent explosive growth of human populations. Here, we review recent literature that inferred recent changes in population size in different human populations and with different methodologies, with many pointing to recent explosive growth, especially in European populations for which more data has been available. We also review the state-of-the-art methods and software for the inference of historical population size changes that lead to these discoveries. Finally, we discuss the implications of recent population growth on personalized genomics, on purifying selection in the non-equilibrium state it entails and, as a consequence, on the genetic architecture underlying complex disease and the performance of mapping methods in discovering rare variants that contribute to complex disease risk.

Genetic risk for common autoimmune diseases is influenced by hundreds of small effect, mostly non-coding variants, enriched in regulatory regions active in adaptive-immune cell types. DNaseI hypersensitivity sites (DHSs) are a genomic mark for regulatory DNA. Here, we generated a single DHSs annotation from fifteen deeply sequenced DNase-seq experiments in adaptive-immune as well as non-immune cell types. Using this annotation we quantified accessibility across cell types in a matrix format amenable to statistical analysis, deduced the subset of DHSs unique to adaptive-immune cell types, and grouped DHSs by cell-type accessibility profiles. Measuring enrichment with cell-type-specific TF binding sites as well as proximal gene expression and function, we show that accessibility profiles grouped DHSs into coherent regulatory functions. Using the adaptive-immune-specific DHSs as input (0.37% of genome), we associated DHSs to six autoimmune diseases with GWAS data. Associated loci showed higher replication rates when compared to loci identified by GWAS or by considering all DHSs, allowing the additional discovery of 327 loci (FDR

Cochin Jews form a small and unique community on the Malabar coast in southwest India. While the arrival time of any putative Jewish ancestors of the community has been speculated to have taken place as far back as biblical times (King Solomon’s era), a Jewish community in the Malabar coast has been documented only since the 9th century CE. Here, we explore the genetic history of Cochin Jews by collecting and genotyping 21 community members and combining the data with that of 707 individuals from 72 other Indian, Jewish, and Pakistani populations, together with additional individuals from worldwide populations. We applied comprehensive genome-wide analyses based on principal component analysis, FST, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing, allele sharing autocorrelation decay and contrasting the X chromosome with the autosomes. We find that, as reported by several previous studies, the genetics of Cochin Jews resembles that of local Indian populations. However, we also identify considerable Jewish genetic ancestry that is not present in any other Indian or Pakistani populations (with the exception of the Jewish Bene Israel, which we characterized previously). Combined, Cochin Jews have both Jewish and Indian ancestry. Specifically, we detect a significant recent Jewish gene flow into this community 13–22 generations (~470–730 years) ago, with contributions from Yemenite, Sephardi, and Middle-Eastern Jews, in accordance with historical records. Genetic analyses also point to high endogamy and a recent population bottleneck in this population, which might explain the increased prevalence of some recessive diseases in Cochin Jews. Electronic supplementary material The online version of this article (doi:10.1007/s00439-016-1698-y) contains supplementary material, which is available to authorized users.

Long chain polyunsaturated fatty acids (LCPUFA) are bioactive components of membrane phospholipids and serve as substrates for signaling molecules. LCPUFA can be obtained directly from animal foods or synthesized endogenously from 18 carbon precursors via the FADS2 coded enzyme. Vegans rely almost exclusively on endogenous synthesis to generate LCPUFA and we hypothesized that an adaptive genetic polymorphism would confer advantage. The rs66698963 polymorphism, a 22 bp insertion-deletion within FADS2, is associated with basal FADS1 expression, and coordinated induction of FADS1 and FADS2 in vitro. Here we determined rs66698963 genotype frequencies from 234 individuals of a primarily vegetarian Indian population and 311 individuals from the U.S. A much higher I/I genotype frequency was found in Indians (68%) than in the U.S. (18%). Analysis using 1000 Genomes Project data confirmed our observation, revealing a global I/I genotype of 70% in South Asians, 53% in Africans, 29% in East Asians, and 17% in Europeans. Tests based on population divergence, site frequency spectrum and long-range haplotype consistently point to positive selection encompassing rs66698963 in South Asian, African and some East Asian populations. Basal plasma phospholipid arachidonic acid status was 8% greater in I/I compared to D/D individuals. The biochemical pathway product-precursor difference, arachidonic acid minus linoleic acid, was 31% and 13% greater for I/I and I/D compared to D/D, respectively. Our study is consistent with previous in vitro data suggesting that the insertion allele enhances n-6 LCPUFA synthesis and may confer an adaptive advantage in South Asians because of the traditional plant-based diet practice.

The mixed dyslipidemia phenotype is characterized by elevated triglycerides (TG), low HDL cholesterol (HDL-C), increased ApoB levels, and premature coronary atherosclerosis. Fibrate-statin combination therapy reduces ApoB levels and coronary events in the mixed dyslipidemia population. We sought to identify gene-gene interactions that affect ApoB response to statin-fenofibric acid therapy in the mixed dyslipidemia population. Using a predefined subset of single-nucleotide polymorphisms (SNPs) that were previously associated with TG, VLDL, or HDL-C, we applied gene-gene interaction testing in a randomized, double-blind, clinical trial examining the response to fenofibric acid (FNA) and its combination with statin in 1,865 individuals with mixed dyslipidemia. Of 11,783 possible SNP pairs examined, we detected a single significant interaction between rs12130333, located within the ANGPTL3 gene region, and rs4240705, within the RXRA gene, on ApoB reduction after statin-FNA therapy (P = 4.0 × 10(-6)). ApoB response to therapy gradually reduced with the increasing number of T alleles in the rs12130333 but only in the presence of the GG genotype of rs4240705. Individuals doubly homozygous for the minor alleles at rs12130333 and rs4240705 showed a paradoxical increase of 1.8% in ApoB levels after FNA-statin combination therapy. No gene-gene interaction was identified other than an interaction between SNPs in the ANGPTL3 and RXRA regions, which results in the inhibition of ApoB reduction in response to statin-FNA therapy. Further study is required to examine the clinical applicability of this genetic interaction and its effect on coronary events.

Exponential Growth Effects Humans are an extraordinarily successful species, as measured by our large population size—approximately 7 billion—much of which can be put down to recent explosive growth. Leveraging human genomic data, Keinan and Clark (p. 740 ) examined the effects of population growth on our ability to detect rare genetic variants, those hypothesized to be most likely associated with disease. It appears that rapid recent growth increases the load of rare variants and is likely to play an important role in the individual genetic burden of complex disease risk.

Synonymous mutations are considered to be ‘‘silent’’ as they do not affect protein sequence. However, different silent codons have different translation efficiency (TE), which raises the question to what extent such mutations are really neutral. We perform the first genome-wide study of natural selection operating on TE in recent human evolution, surveying 13,798 synonymous single nucleotide polymorphisms (SNPs) in 1,198 unrelated individuals from 11 populations. We find evidence for both negative and positive selection on TE, as measured based on differentiation in allele frequencies between populations. Notably, the likelihood of an SNP to be targeted by positive or negative selection is correlated with the magnitude of its effect on the TE of the corresponding protein. Furthermore, negative selection acting against changes in TE is more marked in highly expressed genes, highly interacting proteins, complex members, and regulatory genes. It is also more common in functional regions and in the initial segments of highly expressed genes. Positive selection targeting sites with a large effect on TE is stronger in lowly interacting proteins and in regulatory genes. Similarly, essential genes are enriched for negative TE selection while underrepresented for positive TE selection. Taken together, these results point to the significant role of TE as a selective force operating in humans and hence underscore the importance of considering silent SNPs in interpreting associations with complex human diseases. Testifying to this potential, we describe two synonymous SNPs that may have clinical implications in phenylketonuria and in Best’s macular dystrophy due to TE differences between alleles.

Among the various statistical methods for identifying gene⁻gene interactions in qualitative genome-wide association studies (GWAS), gene-based methods have recently grown in popularity because they confer advantages in both statistical power and biological interpretability. However, most of these methods make strong assumptions about the form of the relationship between traits and single-nucleotide polymorphisms, which result in limited statistical power. In this paper, we propose a gene-based method based on the distance correlation coefficient called gene-based gene-gene interaction via distance correlation coefficient (GBDcor). The distance correlation (dCor) is a measurement of the dependency between two random vectors with arbitrary, and not necessarily equal, dimensions. We used the difference in dCor in case and control datasets as an indicator of gene⁻gene interaction, which was based on the assumption that the joint distribution of two genes in case subjects and in control subjects should not be significantly different if the two genes do not interact. We designed a permutation-based statistical test to evaluate the difference between dCor in cases and controls for a pair of genes, and we provided the p-value for the statistic to represent the significance of the interaction between the two genes. In experiments with both simulated and real-world data, our method outperformed previous approaches in detecting interactions accurately.

Historical episodes of natural selection can skew the frequencies of genetic variants, leaving a signature that can persist for many tens or even hundreds of thousands of years. However, formal tests for selection based on allele frequency skew require strong assumptions about demographic history and mutation, which are rarely well understood. Here, we develop an empirical approach to test for signals of selection that compares patterns of genetic variation at a candidate locus with matched random regions of the genome collected in the same way. We apply this approach to four genes that have been implicated in syndromes of impaired neurological development, comparing the pattern of variation in our re-sequencing data with a large-scale, genomic data set that provides an empirical null distribution. We confirm a previously reported signal at FOXP2, and find a novel signal of selection centered at AHI1, a gene that is involved in motor and behavior abnormalities. The locus is marked by many high frequency derived alleles in non-Africans that are of low frequency in Africans, suggesting that selection at this or a closely neighboring gene occurred in the ancestral population of non-Africans. Our study also provides a prototype for how empirical scans for ancient selection can be carried out once many genomes are sequenced.

Background Mitochondrial dysfunction has been hypothesized to occur in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a disease characterized by fatigue, cognitive difficulties, pain, malaise, and exercise intolerance. We investigated whether haplogroup, single nucleotide polymorphisms (SNPs), or heteroplasmy of mitochondrial DNA (mtDNA) were associated with health status and/or symptoms. Methods Illumina sequencing of PCR-amplified mtDNA was performed to analyze sequence and extent of heteroplasmy of mtDNAs of 193 cases and 196 age- and gender-matched controls from DNA samples collected by the Chronic Fatigue Initiative. Association testing was carried out to examine possible correlations of mitochondrial sequences with case/control status and symptom constellation and severity as reported by subjects on Short Form-36 and DePaul Symptom Questionnaires. Results No ME/CFS subject exhibited known disease-causing mtDNA mutations. Extent of heteroplasmy was low in all subjects. Although no association between mtDNA SNPs and ME/CFS vs. healthy status was observed, haplogroups J, U and H as well as eight SNPs in ME/CFS cases were significantly associated with individual symptoms, symptom clusters, or symptom severity. Conclusions Analysis of mitochondrial genomes in ME/CFS cases indicates that individuals of a certain haplogroup or carrying specific SNPs are more likely to exhibit certain neurological, inflammatory, and/or gastrointestinal symptoms. No increase in susceptibility to ME/CFS of individuals carrying particular mitochondrial genomes or SNPs was observed. Electronic supplementary material The online version of this article (doi:10.1186/s12967-016-0771-6) contains supplementary material, which is available to authorized users.

The Bene Israel Jewish community from West India is a unique population whose history before the 18th century remains largely unknown. Bene Israel members consider themselves as descendants of Jews, yet the identity of Jewish ancestors and their arrival time to India are unknown, with speculations on arrival time varying between the 8th century BCE and the 6th century CE. Here, we characterize the genetic history of Bene Israel by collecting and genotyping 18 Bene Israel individuals. Combining with 486 individuals from 41 other Jewish, Indian and Pakistani populations, and additional individuals from worldwide populations, we conducted comprehensive genome-wide analyses based on FST, principal component analysis, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing and allele sharing autocorrelation decay, as well as contrasted patterns between the X chromosome and the autosomes. The genetics of Bene Israel individuals resemble local Indian populations, while at the same time constituting a clearly separated and unique population in India. They are unique among Indian and Pakistani populations we analyzed in sharing considerable genetic ancestry with other Jewish populations. Putting together the results from all analyses point to Bene Israel being an admixed population with both Jewish and Indian ancestry, with the genetic contribution of each of these ancestral populations being substantial. The admixture took place in the last millennium, about 19-33 generations ago. It involved Middle-Eastern Jews and was sex-biased, with more male Jewish and local female contribution. It was followed by a population bottleneck and high endogamy, which can lead to increased prevalence of recessive diseases in this population. This study provides an example of how genetic analysis advances our knowledge of human history in cases where other disciplines lack the relevant data to do so.

The site frequency spectrum (SFS) and other genetic summary statistics are at the heart of many population genetics studies. Previous studies have shown that human populations had undergone a recent epoch of fast growth in effective population size. These studies assumed that growth is exponential, and the ensuing models leave unexplained excess amount of extremely rare variants. This suggests that human populations might have experienced a recent growth with speed faster than exponential. Recent studies have introduced a generalized growth model where the growth speed can be faster or slower than exponential. However, only simulation approaches were available for obtaining summary statistics under such models. In this study, we provide expressions to accurately and efficiently evaluate the SFS and other summary statistics under generalized models, which we further implement in a publicly available software. Investigating the power to infer deviation of growth from being exponential, we observed that decent sample sizes facilitate accurate inference, e.g. a sample of 3000 individuals with the amount of data expected from exome sequencing allows observing and accurately estimating growth with speed deviating by 10% or more from that of exponential. Applying our inference framework to data from the NHLBI Exome Sequencing Project, we found that a model with a generalized growth epoch fits the observed SFS significantly better than the equivalent model with exponential growth (p-value = 3.85 × 10-6). The estimated growth speed significantly deviates from exponential (p-value << 10-12), with the best-fit estimate being of growth speed 12% faster than exponential.

This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evolutionary algorithms. ENM is based on a standard genetic algorithm with an additional step during reproduction in which synaptic connections are irreversibly eliminated. It receives as input a successfully evolved neurocontroller and aims to output a pruned neurocontroller, while maintaining the original fitness level. The small neurocontrollers produced by ENM provide upper bounds on the neurocontroller size needed to perform a given task successfully, and can provide for more effcient hardware implementations.

This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting.