A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework.

BACKGROUND: There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). RESULTS: We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. CONCLUSIONS: With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders

Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance.

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.This study was funded by the UK Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1. This work was supported by the MRC Metabolic Diseases Unit (MC_UU_12012/5) and the Cambridge NIHR Biomedical Research Centre and EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant 115372). Funding for the InterAct project was provided by the EU FP6 program (grant LSHM_CT_2006_037197). This work was funded, in part, through an EFSD Rising Star award to R.A.S. supported by Novo Nordisk. D.B.S. is supported by Wellcome Trust grant 107064. M.I.M. is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. M.v.d.B. is supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. I.B. is supported by Wellcome Trust grant WT098051. S.O'R. acknowledges funding from the Wellcome Trust (Wellcome Trust Senior Investigator Award 095515/Z/11/Z and Wellcome Trust Strategic Award 100574/Z/12/Z)

Meta-analysis identifies novel risk loci and yields systematic insights into the biology of male-pattern baldness

Life & Brain GmbH; the BONFOR programme of the University of Bonn; and the Agency for Science, Technology and Research (A*STAR). M.M.N. is a member of the DFG Excellence Cluster ImmunoSensation

Decoy peptide targeted to Toll-IL-1R domain inhibits LPS and TLR4-active metabolite morphine-3 glucuronide sensitization of sensory neurons

Accumulating evidence indicates that Toll-like receptor (TLR) signaling adapter protein interactions with Toll/Interleukin-1 Receptor (TIR) domains present in sensory neurons may modulate neuropathic pain states. Following ligand interaction with TLRs, TIR serves to both initiate intracellular signaling and facilitate recruitment of signaling adapter proteins to the intracytoplasmic domain. Although TLR TIR is central to a number of TLR signaling cascades, its role in sensory neurons is poorly understood. In this study we investigated the degree to which TLR TIR decoy peptide modified to include a TAT sequence (Trans-Activator of Transcription gene in HIV; TAT-4BB) affected LPS-induced intracellular calcium flux and excitation in sensory neurons, and behavioral changes due to TLR4 active metabolite, morphine-3-glucuronide (M3G) exposure in vivo. TAT-4BB inhibited LPS-induced calcium changes in a majority of sensory neurons and decreased LPS-dependent neuronal excitability in small diameter neurons. Acute systemic administration of the TAT-4BB reversed M3G-induced tactile allodynia in a dose-dependent manner but did not affect motor activity, anxiety or responses to noxious thermal stimulus. These data suggest that targeting TLR TIR domains may provide novel pharmacological targets to reduce or reverse TLR4-dependent pain behavior in the rodent

Genetic prediction of male pattern baldness

Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40-69 years. We identified over 250 independent genetic loci associated with severe hair loss (P<5x10-8). By splitting the cohort into a discovery sample of 40,000 and target sample of 12,000, we developed a prediction algorithm based entirely on common genetic variants that discriminated (AUC = 0.78, sensitivity = 0.74, specificity = 0.69, PPV = 59%, NPV = 82%) those with no hair loss from those with severe hair loss. The results of this study might help identify those at greatest risk of hair loss, and also potential genetic targets for intervention

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.Multi-ancestry meta-analyses of genome-wide association studies for self-reported physical activity during leisure time, leisure screen time, sedentary commuting and sedentary behavior at work identify 99 loci associated with at least one of these traits

Bags of Graphs for Human Action Recognition

International audienceBags of visual words are a well known approach for images classification that also has been used in human action recognition. This model proposes to represent images or videos in a structure referred to as bag of visual words before classifying. The process of representing a video in a bag of visual words is known as the encoding process and is based on mapping the interest points detected in the scene into the new structure by means of a codebook. In this paper we propose to improve the representativeness of this model including the structural relations between the interest points using graph sequences. The proposed model achieves very competitive results for human action recognition and could also be applied to solve graph sequences classification problems