Migraine is a debilitating neurological disorder affecting around 1 in 7 people worldwide, but its molecular mechanisms remain poorly understood. Some debate exists over whether migraine is a disease of vascular dysfunction, or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we performed the largest genetic study of migraine to date, comprising 59,674 cases and 316,078 controls from 22 GWA studies. We identified 45 independent single nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 x 10-8) that map to 38 distinct genomic loci, including 28 loci not previously reported and the first locus identified on chromosome X. Furthermore, a subset analysis for migraine without aura (MO) identified seven of the same loci as from the full sample, whereas no loci reached genome-wide significance in the migraine with aura (MA) subset. In subsequent computational analyzes, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.

Here, we predict gene expression from epigenetic features based on public data available through the Epigenome Roadmap Project. This rich new dataset includes samples from primary tissues, which to our knowledge have not previously been studied in this context. Specifically, we used computational machine learning algorithms on five histone modifications to predict gene expression in a variety of samples. Our models reveal a high predictive accuracy, especially in cell cultures, with predictive ability dependent on sample type and anatomy. The relative importance of each histone mark feature varied across samples. We localized each histone mark signal to its relevant region, revealing that chromatin state enrichment varies greatly between histone marks. Our results provide several novel insights into epigenetic regulation of transcription in new contexts.

Here we present the first dataset based on human microbiota samples of an urban middle-income population in South America. We characterized the microbiota of six different body habitats: palatine tonsils, saliva, buccal mucosa, throat, anterior nares and gut from samples of healthy individual living in a metropolitan area in Argentina. Our initial findings revealed differences in the structure and composition of the microbial communities compared to the US urban population. By sharing our data, we want to actively encourage its reuse for comparison purposes. This will hopefully result in novel biological insights on the variability of the microbiota of healthy individuals across populations worldwide. Moreover, the understanding of the human microbiota ecosystem in a health-associated state will help to answer questions related to the role of the microbiota in disease.

Hybridization between humans and Neanderthals has resulted in a low level of Neanderthal ancestry scattered across the genomes of many modern-day humans. After hybridization, on average, selection appears to have removed Neanderthal alleles from the human population. Quantifying the strength and causes of this selection against Neanderthal ancestry is key to understanding our relationship to Neanderthals and, more broadly, how populations remain distinct after secondary contact. Here, we develop a novel method for estimating the genome-wide average strength of selection and the density of selected sites using estimates of Neanderthal allele frequency along the genomes of modern-day humans. We confirm that East Asians had somewhat higher initial levels of Neanderthal ancestry than Europeans even after accounting for selection. We find that there are systematically lower levels of initial introgression on the X chromosome, a finding consistent with a strong sex bias in the initial matings between the populations. We find that the bulk of purifying selection against Neanderthal ancestry is best understood as acting on many weakly deleterious alleles. We propose that the majority of these alleles were effectively neutral-and segregating at high frequency-in Neanderthals, but became selected against after entering human populations of much larger effective size. While individually of small effect, these alleles potentially imposed a heavy genetic load on the early-generation human-Neanderthal hybrids. This work suggests that differences in effective population size may play a far more important role in shaping levels of introgression than previously thought.

Understanding the relationship between genomic variation and variation in phenotypes for quantitative traits such as physiology, yield, fitness or behavior, will provide important insights for both predicting adaptive evolution and for breeding schemes. A particular question is whether the genetic variation that influences quantitative phenotypes is typically the result of one or two mutations of large effect, or multiple mutations of small effect. In this paper we explore this issue using the wild model legume Medicago truncatula. We show that phenotypes, such as quantitative disease resistance, can be well-predicted using genome-wide patterns of admixture, from which it follows that there must be many mutations of small effect. Our findings prove the potential of our novel ‘whole-genome modeling’ -WhoGEM- method and experimentally validate, for the first time, the infinitesimal model as a mechanism for adaptation of quantitative phenotypes in plants. This insight can accelerate breeding and biomedicine research programs.

Approximately 2-4% of the human genome is in non-Africans comprised of DNA intro- gressed from Neanderthals. Recent studies have shown that there is a paucity of introgressed DNA around functional regions, presumably caused by selection after introgression. This observation has been suggested to be a possible consequence of the accumulation of a large amount of Dobzhansky-Muller incompatibilities, i.e. epistatic effects between human and Neanderthal specific mutations, since the divergence of humans and Neanderthals approx. 400-600 kya. However, using previously published estimates of inbreeding in Neanderthals, and of the distribution of fitness effects from human protein coding genes, we show that the average Neanderthal would have had at least 40% lower fitness than the average human due to higher levels of inbreeding and an increased mutational load, regardless of the dominance coefficients of new mutations. Using simulations, we show that under the assumption of additive dominance effects, early Neanderthal/human hybrids would have experienced strong negative selection, though not so strong that it would prevent Neanderthal DNA from entering the human population. In fact, the increased mutational load in Neanderthals predicts the observed reduction in Neanderthal introgressed segments around protein coding genes, without any need to invoke epistasis. The simulations also predict that there is a residual Neanderthal derived mutational load in non-African humans, leading to an average fitness reduction of at least 0.5%. Although there has been much previous debate about the effects of the out-of-Africa bottleneck on mutational loads in non-Africans, the significant deleterious effects of Neanderthal introgression have hitherto been left out of this discussion, but might be just as important for understanding fitness differences among human populations. We also show that if deleterious mutations are recessive, the Neanderthal admixture fraction would gradually increase over time due to selection for Neanderthal haplotypes that mask human deleterious mutations in the heterozygous state. This effect of dominance heterosis might partially explain why adaptive introgression appears to be widespread in nature.

There has been intense debate over the immunological basis of schizophrenia, and the potential utility of adjunct immunotherapies. The major histocompatibility complex is consistently the most powerful region of association in genome-wide association studies (GWASs) of schizophrenia, and has been interpreted as strong genetic evidence supporting the immune hypothesis. However, global pathway analyses provide inconsistent evidence of immune involvement in schizophrenia, and it remains unclear whether genetic data support an immune etiology per se. Here we empirically test the hypothesis that variation in immune genes contributes to schizophrenia. We show that there is no genome-wide enrichment of immune loci in the largest genetic study of schizophrenia conducted to date, in contrast to six diseases of known immune origin. Among 108 regions of the genome previously associated with schizophrenia, we identify six immune candidates (DPP4, HSPD1, EGR1, CLU, ESAM, NFATC3) encoding proteins with alternative, non-immune roles in the brain. Our results suggest that genetically mediated alterations in immune function may not play a major role in schizophrenia susceptibility. Instead, there may be a role for pleiotropic effects of a small number of immune genes regulating brain development and plasticity. While our findings do not refute evidence that has accumulated in support of the immune hypothesis, they indicate a non-genetic etiology for immune processes that may be involved in schizophrenia. Whether immune alterations drive schizophrenia progression is an important question to be addressed by future research, especially in light of the growing interest in applying immunotherapies in schizophrenia.