Whole-genome association analysis of treatment response in obsessive-compulsive disorder.

Up to 30% of patients with obsessive-compulsive disorder (OCD) exhibit an inadequate response to serotonin reuptake inhibitors (SRIs). To date, genetic predictors of OCD treatment response have not been systematically investigated using genome-wide association study (GWAS). To identify specific genetic variations potentially influencing SRI response, we conducted a GWAS study in 804 OCD patients with information on SRI response. SRI response was classified as 'response' (n=514) or 'non-response' (n=290), based on self-report. We used the more powerful Quasi-Likelihood Score Test (the MQLS test) to conduct a genome-wide association test correcting for relatedness, and then used an adjusted logistic model to evaluate the effect size of the variants in probands. The top single-nucleotide polymorphism (SNP) was rs17162912 (P=1.76 × 10(-8)), which is near the DISP1 gene on 1q41-q42, a microdeletion region implicated in neurological development. The other six SNPs showing suggestive evidence of association (P<10(-5)) were rs9303380, rs12437601, rs16988159, rs7676822, rs1911877 and rs723815. Among them, two SNPs in strong linkage disequilibrium, rs7676822 and rs1911877, located near the PCDH10 gene, gave P-values of 2.86 × 10(-6) and 8.41 × 10(-6), respectively. The other 35 variations with signals of potential significance (P<10(-4)) involve multiple genes expressed in the brain, including GRIN2B, PCDH10 and GPC6. Our enrichment analysis indicated suggestive roles of genes in the glutamatergic neurotransmission system (false discovery rate (FDR)=0.0097) and the serotonergic system (FDR=0.0213). Although the results presented may provide new insights into genetic mechanisms underlying treatment response in OCD, studies with larger sample sizes and detailed information on drug dosage and treatment duration are needed

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach

Changing the name of the NBPF/DUF1220 domain to the Olduvai domain

We are jointly proposing a new name for a protein domain of approximately 65 amino acids that has been previously termed NBPF or DUF1220. Our two labs independently reported the initial studies of this domain, which is encoded almost entirely within a single gene family. The name Neuroblastoma Breakpoint Family (NBPF) was applied to this gene family when the first identified member of the family was found to be interrupted in an individual with neuroblastoma. Prior to this discovery, the PFAM database had termed the domain DUF1220, denoting it as one of many protein domains of unknown function. It has been PFAM’s intention to use “DUF” nomenclature to serve only as a temporary placeholder until more appropriate names are proposed based on research findings. We believe that additional studies of this domain, primarily from our laboratories over the past 10 years, have resulted in furthering our understanding of these sequences to the point where proposing a new name for this domain is warranted. Because of considerable data linking the domain to human-specific evolution, brain expansion and cognition, we believe a name reflecting these findings would be appropriate. With this in mind, we have chosen to name the domain (and the repeat that encodes it) Olduvai. The gene family will remain as NBPF for now. The primary domain subtypes will retain their previously assigned names (e.g. CON1-3; HLS1-3), and the three-domain block that expanded dramatically in the human lineage will be termed the Olduvai triplet. The new name refers to Olduvai Gorge, which is a site in East Africa that has been the source of major anthropological discoveries in the early-mid 1900’s. We also chose the name as a tribute to the scientists who made important contributions to the early studies of human origins and our African genesis

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10−8), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis

Distributed BLAST in a grid computing context

The Basic Local Alignment Search Tool (BLAST) is one of the best known sequence comparison programs available in bioinformatics. It is used to compare query sequences to a set of target sequences, with the intention of finding similar sequences in the target set. Here, we present a distributed BLAST service which operates over a set of heterogeneous Grid resources and is made available through a Globus toolkit v.3 Grid service. This work has been carried out in the context of the BRIDGES project, a UK e-Science project aimed at providing a Grid based environment for biomedical research. Input consisting of multiple query sequences is partitioned into sub-jobs on the basis of the number of idle compute nodes available and then processed on these in batches. To achieve this, we have implemented our own Java-based scheduler which distributes sub-jobs across an array of resources utilizing a variety of local job scheduling systems

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

Genome-wide association studies of the self-rating of effects of ethanol (SRE).

The level of response (LR) to alcohol as measured with the Self-Report of the Effects of Alcohol Retrospective Questionnaire (SRE) evaluates the number of standard drinks usually required for up to four effects. The need for a higher number of drinks for effects is genetically influenced and predicts higher risks for heavy drinking and alcohol problems. We conducted genome-wide association study (GWAS) in the African-American (COGA-AA, N = 1527 from 309 families) and European-American (COGA-EA, N = 4723 from 956 families) subsamples of the Collaborative Studies on the Genetics of Alcoholism (COGA) for two SRE scores: SRE-T (average of first five times of drinking, the period of heaviest drinking, and the most recent 3 months of consumption) and SRE-5 (the first five times of drinking). We then meta-analyzed the two COGA subsamples (COGA-AA + EA). Both SRE-T and SRE-5 were modestly heritable (h2 : 21%-31%) and genetically correlated with alcohol dependence (AD) and DSM-IV AD criterion count (rg : 0.35-0.76). Genome-wide significant associations were observed (SRE-T: chromosomes 6, rs140154945, COGA-EA P = 3.30E-08 and 11, rs10647170, COGA-AA+EA P = 3.53E-09; SRE-5: chromosome13, rs4770359, COGA-AA P = 2.92E-08). Chromosome 11 was replicated in an EA dataset from the National Institute on Alcohol Abuse and Alcoholism intramural program. In silico functional analyses and RNA expression analyses suggest that the chromosome 6 locus is an eQTL for KIF25. Polygenic risk scores derived using the COGA SRE-T and SRE-5 GWAS predicted 0.47% to 2.48% of variances in AD and DSM-IV AD criterion count in independent datasets. This study highlights the genetic contribution of alcohol response phenotypes to the etiology of alcohol use disorders