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Background A number of different statistics are used for detecting natural selection using DNA sequencing data, including statistics that are summaries of the frequency spectrum, such as Tajima’s D . These statistics are now often being applied in the analysis of Next Generation Sequencing (NGS) data. However, estimates of frequency spectra from NGS data are strongly affected by low sequencing coverage; the inherent technology dependent variation in sequencing depth causes systematic differences in the value of the statistic among genomic regions. Results We have developed an approach that accommodates the uncertainty of the data when calculating site frequency based neutrality test statistics. A salient feature of this approach is that it implicitly solves the problems of varying sequencing depth, missing data and avoids the need to infer variable sites for the analysis and thereby avoids ascertainment problems introduced by a SNP discovery process. Conclusion Using an empirical Bayes approach for fast computations, we show that this method produces results for low-coverage NGS data comparable to those achieved when the genotypes are known without uncertainty. We also validate the method in an analysis of data from the 1000 genomes project. The method is implemented in a fast framework which enables researchers to perform these neutrality tests on a genome-wide scale.

Throughout history, populations across species have been decimated by epidemic outbreaks. Recent studies have raised the enticing idea that such outbreaks have led to strong natural selection acting on disease-protective genetic variants in the host population. However, so far few, if any, clear examples of such selection exist. This could be because previous studies were underpowered to detect the type of selection an outbreak must induce: extremely short-term selection on standing variation. Here we present a simulation-based framework that allows users to explore under what circumstances selection scan methods like F S T have power to detect epidemic-driven selection on a variant. Using two examples, we illustrate how the framework can be used. The examples also show that comparing those who died from an outbreak to survivors has the potential to render higher power than more commonly used sampling schemes. And importantly, they show that even for severe outbreaks, like the Black Death (≈50% mortality), selection may have led to only a modest increase in allele frequency, suggesting large sample sizes are required to obtain appropriate power. We hope this framework can help in designing well-powered future studies and thus help clarify the evolutionary role epidemic-driven selection has played in different species. Epidemic outbreaks can decimated populations, potentially driving natural selection on genetic variants that offer protection. This study presents SimOutbreakSelection (SOS), a simulation-based framework for designing well-powered studies to detect genetic variants that have been under epidemic-driven selection, showing that large sample sizes are needed even for severe epidemics.

The indigenous people of Greenland, the Inuit, have lived for a long time in the extreme conditions of the Arctic, including low annual temperatures, and with a specialized diet rich in protein and fatty acids, particularly omega-3 polyunsaturated fatty acids (PUFAs). A scan of Inuit genomes for signatures of adaptation revealed signals at several loci, with the strongest signal located in a cluster of fatty acid desaturases that determine PUFA levels. The selected alleles are associated with multiple metabolic and anthropometric phenotypes and have large effect sizes for weight and height, with the effect on height replicated in Europeans. By analyzing membrane lipids, we found that the selected alleles modulate fatty acid composition, which may affect the regulation of growth hormones. Thus, the Inuit have genetic and physiological adaptations to a diet rich in PUFAs.

An association mapping study of type-2-diabetes-related quantitative traits in the Greenlandic population identified a common variant in TBC1D4 that increases plasma glucose levels and serum insulin levels after an oral glucose load and type 2 diabetes risk, with effect sizes several times larger than any previous findings of large-scale genome-wide association studies for these traits. Diabetes-linked gene variant identified This systematic genetic association study of quantitative traits related to type 2 diabetes (T2D) has identified a nonsense variant in the gene TBC1D4 which is present in 17% of the Greenlandic population, known to be a small founder population with a high incidence of T2D. The gene variant increases the levels of plasma glucose, serum insulin, and dramatically increases T2D risk. It also modestly reduces the concentrations of fasting plasma and fasting serum insulin. This work illustrates the value of founder populations — or of small and historically isolated populations — in maximizing the effectiveness of genetic association studies of this type. The Greenlandic population, a small and historically isolated founder population comprising about 57,000 inhabitants, has experienced a dramatic increase in type 2 diabetes (T2D) prevalence during the past 25 years 1 . Motivated by this, we performed association mapping of T2D-related quantitative traits in up to 2,575 Greenlandic individuals without known diabetes. Using array-based genotyping and exome sequencing, we discovered a nonsense p.Arg684Ter variant (in which arginine is replaced by a termination codon) in the gene TBC1D4 with an allele frequency of 17%. Here we show that homozygous carriers of this variant have markedly higher concentrations of plasma glucose ( β = 3.8 mmol l −1 , P = 2.5 × 10 −35 ) and serum insulin ( β = 165 pmol l −1 , P = 1.5 × 10 −20 ) 2 hours after an oral glucose load compared with individuals with other genotypes (both non-carriers and heterozygous carriers). Furthermore, homozygous carriers have marginally lower concentrations of fasting plasma glucose ( β = −0.18 mmol l −1 , P = 1.1 × 10 −6 ) and fasting serum insulin ( β = −8.3 pmol l −1 , P = 0.0014), and their T2D risk is markedly increased (odds ratio (OR) = 10.3, P = 1.6 × 10 −24 ). Heterozygous carriers have a moderately higher plasma glucose concentration 2 hours after an oral glucose load than non-carriers ( β = 0.43 mmol l −1 , P = 5.3 × 10 −5 ). Analyses of skeletal muscle biopsies showed lower messenger RNA and protein levels of the long isoform of TBC1D4, and lower muscle protein levels of the glucose transporter GLUT4, with increasing number of p.Arg684Ter alleles. These findings are concomitant with a severely decreased insulin-stimulated glucose uptake in muscle, leading to postprandial hyperglycaemia, impaired glucose tolerance and T2D. The observed effect sizes are several times larger than any previous findings in large-scale genome-wide association studies of these traits 2 , 3 , 4 and constitute further proof of the value of conducting genetic association studies outside the traditional setting of large homogeneous populations.

The origin of contemporary Europeans remains contentious. We obtained a genome sequence from Kostenki 14 in European Russia dating from 38,700 to 36,200 years ago, one of the oldest fossils of anatomically modern humans from Europe. We find that Kostenki 14 shares a close ancestry with the 24,000-year-old Mal'ta boy from central Siberia, European Mesolithic hunter-gatherers, some contemporary western Siberians, and many Europeans, but not eastern Asians. Additionally, the Kostenki 14 genome shows evidence of shared ancestry with a population basal to all Eurasians that also relates to later European Neolithic farmers. We find that Kostenki 14 contains more Neandertal DNA that is contained in longer tracts than present Europeans. Our findings reveal the timing of divergence of western Eurasians and East Asians to be more than 36,200 years ago and that European genomic structure today dates back to the Upper Paleolithic and derives from a metapopulation that at times stretched from Europe to central Asia.

We have identified a variant in ADCY3 (encoding adenylate cyclase 3) associated with markedly increased risk of obesity and type 2 diabetes in the Greenlandic population. The variant disrupts a splice acceptor site, and carriers have decreased ADCY3 RNA expression. Additionally, we observe an enrichment of rare ADCY3 loss-of-function variants among individuals with type 2 diabetes in trans-ancestry cohorts. These findings provide new information on disease etiology relevant for future treatment strategies. Individuals from a Greenlandic Inuit population with homozygous loss-of-function variants in ADCY3 (adenylate cyclase 3) have increased risk for obesity and type 2 diabetes. Carriers of rare ADCY3 variants in trans-ancestry populations also show increased association with type 2 diabetes.

A better understanding of the biological factors underlying antidepressant treatment in patients with major depressive disorder (MDD) is needed. We perform gene expression analyses and explore sources of variability in peripheral blood related to antidepressant treatment and treatment response in patients suffering from recurrent MDD at baseline and after 8 weeks of treatment. The study includes 281 patients, which were randomized to 8 weeks of treatment with vortioxetine (N = 184) or placebo (N = 97). To our knowledge, this is the largest dataset including both gene expression in blood and placebo-controlled treatment response measured by a clinical scale in a randomized clinical trial. We identified three novel genes whose RNA expression levels at baseline and week 8 are significantly (FDR < 0.05) associated with treatment response after 8 weeks of treatment. Among these genes were SOCS3 (FDR = 0.0039) and PROK2 (FDR = 0.0028), which have previously both been linked to depression. Downregulation of these genes was associated with poorer treatment response. We did not identify any genes that were differentially expressed between placebo and vortioxetine groups at week 8 or between baseline and week 8 of treatment. Nor did we replicate any genes identified in previous peripheral blood gene expression studies examining treatment response. Analysis of genome-wide expression variability showed that type of treatment and treatment response explains very little of the variance, a median of <0.0001% and 0.05% in gene expression across all genes, respectively. Given the relatively large size of the study, the limited findings suggest that peripheral blood gene expression might not be the best approach to explore the biological factors underlying antidepressant treatment.

The genetic architecture of antidepressant response is poorly understood. Polygenic risk scores (PRS), exploration of placebo response and the use of sub-scales might provide insights. Here, we investigate the association between PRSs for relevant complex traits and response to vortioxetine treatment and placebo using clinical scales, including sub-scales and self-reported assessments. We collected a clinical test sample of Major Depressive Disorder (MDD) patients treated with vortioxetine ( N  = 907) or placebo ( N  = 455) from seven randomized, double-blind, clinical trials. In parallel, we obtained data from an observational web-based study of vortioxetine-treated patients ( N  = 642) with self-reported response. PRSs for antidepressant response, psychiatric disorders, and symptom traits were derived using summary statistics from well-powered genome-wide association studies (GWAS). Association tests were performed between the PRSs and treatment response in each of the two test samples and empirical p -values were evaluated. In the clinical test sample, no PRSs were significantly associated with response to vortioxetine treatment or placebo following Bonferroni correction. However, clinically assessed treatment response PRS was nominally associated with vortioxetine treatment and placebo response given by several secondary outcome scales (improvement on HAM-A, HAM-A Psychic Anxiety sub-scale, CPFQ & PDQ), ( P ≤ 0.026). Further, higher subjective well-being PRS ( P ≤ 0.033) and lower depression PRS ( P  = 0.01) were nominally associated with higher placebo response. In the self-reported test sample, higher schizophrenia PRS was significantly associated with poorer self-reported response ( P  = 0.0001). The identified PRSs explain a low proportion of the variance (1.2–5.3%) in placebo and treatment response. Although the results were limited, we believe that PRS associations bear unredeemed potential as a predictor for treatment response, as more well-powered and phenotypically similar GWAS bases become available.

Aims/hypothesisThe common muscle-specific TBC1D4 p.Arg684Ter loss-of-function variant defines a subtype of non-autoimmune diabetes in Arctic populations. Homozygous carriers are characterised by elevated postprandial glucose and insulin levels. Because 3.8% of the Greenlandic population are homozygous carriers, it is important to explore possibilities for precision medicine. We aimed to investigate whether physical activity attenuates the effect of this variant on 2 h plasma glucose levels after an oral glucose load.MethodsIn a Greenlandic population cohort (n = 2655), 2 h plasma glucose levels were obtained after an OGTT, physical activity was estimated as physical activity energy expenditure and TBC1D4 genotype was determined. We performed TBC1D4–physical activity interaction analysis, applying a linear mixed model to correct for genetic admixture and relatedness.ResultsPhysical activity was inversely associated with 2 h plasma glucose levels (β[main effect of physical activity] −0.0033 [mmol/l] / [kJ kg−1 day−1], p = 6.5 × 10−5), and significantly more so among homozygous carriers of the TBC1D4 risk variant compared with heterozygous carriers and non-carriers (β[interaction] −0.015 [mmol/l] / [kJ kg−1 day−1], p = 0.0085). The estimated effect size suggests that 1 h of vigorous physical activity per day (compared with resting) reduces 2 h plasma glucose levels by an additional ~0.7 mmol/l in homozygous carriers of the risk variant.Conclusions/interpretationPhysical activity improves glucose homeostasis particularly in homozygous TBC1D4 risk variant carriers via a skeletal muscle TBC1 domain family member 4-independent pathway. This provides a rationale to implement physical activity as lifestyle precision medicine in Arctic populations.Data repositoryThe Greenlandic Cardio-Metabochip data for the Inuit Health in Transition study has been deposited at the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/dacs/EGAC00001000736) under accession EGAD00010001428.

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here, we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.