We describe implementation of a set-based method to assess the significance of findings from genome-wide association study data. Our method, implemented in PLINK, is based on theoretical approximation of Fisher’s statistics such that the combination of p-vales at a gene or across a pathway are done in a manner that accounts for the correlation structure, or linkage disequilibrium, between SNPs. We compare our method to a permutation based product of p-values approach and show a typical correlation in excess of 0.98 for a number of comparisons. The method gives Type I error rates that are less than or equal to the corresponding nominal significance levels, making it robust to the effects of false positives. We show that in broadly similar populations, reference datasets of markers are an appropriate substrate for deriving marker-marker LD, negating the need to access individual level genotypes, greatly facilitating its generic applicability. We show that the method is thus robust to LD-associated bias and has equivalent performance to permutation-based methods, with a significantly shorter runtime. This is particularly relevant at a time of increasing public availability of significantly larger genetic datasets and should go a long way to assist in the rapid analysis of these datasets.

Psychotic symptoms occur in approximately 40% of subjects with Alzheimer’s disease (AD) and are associated with more rapid cognitive decline and increased functional deficits. They show heritability up to 61% and have been proposed as a marker for a disease subtype suitable for gene mapping efforts. We undertook a combined analysis of three genome-wide association studies (GWAS) to identify loci that a) increase susceptibility to an AD and subsequent psychotic symptoms; or b) modify risk of psychotic symptoms in the presence of neurodegeneration caused by AD. 1299 AD cases with psychosis (AD+P), 735 AD cases without psychosis (AD−P) and 5659 controls were drawn from GERAD1, the NIA-LOAD family study and the University of Pittsburgh ADRC GWAS. Unobserved genotypes were imputed to provide data on > 1.8 million SNPs. Analyses in each dataset were completed comparing a) AD+P to AD−P cases, and b) AD+P cases with controls (GERAD1, ADRC only). Aside from the APOE locus, the strongest evidence for association was observed in an intergenic region on chromosome 4 (rs753129; ‘AD+PvAD−P’ P=2.85 × 10−7; ‘AD+PvControls’ P=1.11 × 10−4). SNPs upstream of SLC2A9 (rs6834555, P=3.0×10−7) and within VSNL1 (rs4038131, P=5.9×10−7) showed strongest evidence for association with AD+P when compared to controls. These findings warrant further investigation in larger, appropriately powered samples in which the presence of psychotic symptoms in AD has been well characterised.

We assessed the role of rare copy number variants (CNVs) in Alzheimer's disease (AD) using intensity data from 3260 AD cases and 1290 age-matched controls from the genome-wide association study (GWAS) conducted by the Genetic and Environmental Risk for Alzheimer's disease Consortium (GERAD). We did not observe a significant excess of rare CNVs in cases, although we did identify duplications overlapping APP and CR1 which may be pathogenic. We looked for an excess of CNVs in loci which have been highlighted in previous AD CNV studies, but did not replicate previous findings. Through pathway analyses, we observed suggestive evidence for biological overlap between single nucleotide polymorphisms and CNVs in AD susceptibility. We also identified that our sample of elderly controls harbours significantly fewer deletions >1 Mb than younger control sets in previous CNV studies on schizophrenia and bipolar disorder (P = 8.9 × 10−4 and 0.024, respectively), raising the possibility that healthy elderly individuals have a reduced rate of large deletions. Thus, in contrast to diseases such as schizophrenia, autism and attention deficit/hyperactivity disorder, CNVs do not appear to make a significant contribution to the development of AD.

Testing whether genetic information could inform the selection of the best drug for patients with depression, Rudolf Uher and colleagues searched for genetic variants that could predict clinically meaningful responses to two major groups of antidepressants.

Background

It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.

Methods and Findings

The NEWMEDS consortium, an academia–industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10−8). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10−8) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.

Conclusions

No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

Genetic and environmental factors can influence a person's response to medications. Taking advantage of the recent advancements in genetics, scientists are working to match specific gene variations with responses to particular medications. Knowing whether a patient is likely to respond to a drug or have serious side effects would allow doctors to select the best treatment up front. Right now, there are only a handful of examples where a patient's version of a particular gene predicts their response to a particular drug. Some scientists believe that there will be many more such matches between genetic variants and treatment responses. Others think that because the action of most drugs is influenced by many different genes, a variant in one of those genes is unlikely to have measurable effect in most cases.

Why Was This Study Done?

One of the areas where patients' responses to available drugs vary widely is severe depression (or major depressive disorder). Prescription of an antidepressant is often the first step in treating the disease. However, less than half of patients get well taking the first antidepressant prescribed. Those who don't respond to the first drug need to, together with their doctors, try multiple courses of treatment to find the right drug and the right dose for them. For some patients none of the existing drugs work well.

To see whether genetic information could help improve the choice of antidepressant, researchers from universities and the pharmaceutical industry joined forces in this large study. They examined two ways to use genetic information to improve the treatment of depression. First, they searched all genes for common genetic variants that could predict which patients would not respond to the two major groups of antidepressants (serotonin reuptake inhibitors, or SRIs, and noradrenaline reuptake inhibitors, or NRIs). They hoped that this would help with the development of new drugs that could help these patients. Second, they looked for common genetic variants in all genes that could identify patients who responded to one of the two major groups of antidepressants. Such predictors would make it possible to know which drug to prescribe for which patient.

What Did the Researchers Do and Find?

The researchers selected 1,790 patients with severe depression who had participated in one of several research studies; 1,222 of the patients had been treated with an SRI, the remaining 568 with an NRI, and it was recorded how well the drugs worked for each patient. The researchers also had a detailed picture of the genetic make-up of each patient, with information for over half a million genetic variants. They then looked for an association between genetic variants and responses to drugs.

They found not a single genetic variant that could predict clearly whether a person would respond to antidepressants in general, to one of the two main groups (SRIs and NRIs), or much better to one than the other. They also didn't find any combination of variants in groups of genes that work together that could predict responses. Combining their data with those from another large study did not yield any robust predictors either.

What Do These Findings Mean?

This study was large enough that it should have been possible to find common genetic variants that by themselves could predict a clinically meaningful response to SRIs and/or NRIs, had such variants existed. The fact that the study failed to find such variants suggests that such variants do not exist. It is still possible, however, that variants that are less common could predict response, or that combinations of variants could. To find those, if they do exist, even larger studies will need to be done.

Additional Information

Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001326

The National Institute of General Medical Sciences at the US National Institutes of Health has a fact sheet on personalized medicine

PubMed Health at the US National Library of Medicine has a page on major depressive disorder

Wikipedia has pages on major depressive disorder and pharmacogenetics, the study of how genetic variation affects response to certain drugs (note that Wikipedia is a free online encyclopedia that anyone can edit)

The UK National Health Service has comprehensive information pages on depression

Genome-wide association studies (GWAS) of body mass index (BMI) using large samples have yielded approximately a dozen robustly associated variants and implicated additional loci. Individually these variants have small effects and in aggregate explain a small proportion of the variance. As a result, replication attempts have limited power to achieve genome-wide significance, even with several thousand subjects. Since there is strong prior evidence for genetic influence on BMI for specific variants, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be tested. In the current study, GRSS comprising 56 top variants catalogued from two large meta-analyses was tested for association with BMI in the Molecular Genetics of Schizophrenia controls (2,653 European-Americans, 973 African-Americans). After accounting for covariates known to influence BMI (ancestry, sex, age), GRSS was highly associated with BMI (p value = 3.19E−06) although explained a limited amount of the variance (0.66%). However, area under receiver operator criteria curve (AUC) estimates indicated that the GRSS and covariates significantly predicted overweight and obesity classification with maximum discriminative ability for predicting class III obesity (AUC = 0.697). The relative contributions of the individual loci to GRSS were examined post hoc and the results were not due to a few highly significant variants, but rather the result of numerous variants of small effect. This study provides evidence of the utility of a GRSS as an alternative approach to replication of common polygenic variation in complex traits.

Background

Given that genome wide association studies (GWAS) of psychiatric disorders have identified only a small number of convincingly associated variants, there is interest in seeking additional evidence for associated variants using tests of gene-gene interaction. Comprehensive pair-wise SNP-SNP interaction analysis is computationally intensive and the penalty for multiple testing is severe given the number of interactions possible. Aiming to minimize these statistical and computational burdens, we have explored approaches to prioritise SNPs for interaction analyses.

Methods

Primary interaction analyses were performed using the Wellcome Trust Case Control Consortium Bipolar Disorder GWAS (1868 cases, 2938 controls). Replication analyses were performed using the Genetic Association Information Network BD dataset (1001 cases, 1033 controls). SNPs were prioritized for interaction analysis that showed evidence for association that surpassed a number of nominally significant thresholds, are within genome-wide significant genes, or are within genes that are functionally related.

Results

For no set of prioritized SNPs did we obtain evidence to support the hypothesis that the selection strategy identified pairs of variants that were enriched for true (statistical) interactions.

Conclusions

SNPs prioritized according to a number of criteria do not have a raised prior probability for significant interaction that is detectable in samples of this size. As is now widely accepted for single SNP analysis, we argue the use of significance levels reflecting only the number of tests performed does not offer an appropriate degree of protection against the potential for GWAS studies to generate an enormous number of false positive interactions.

Background

While genetic influences on Alcohol Dependence (AD) are substantial, progress in the identification of individual genetic variants that impact on risk has been difficult.

Methods

We performed a genome-wide association study on 3,169 alcohol consuming subjects from the population-based Molecular Genetics of Schizophrenia (MGS2) control sample. Subjects were asked 7 questions about symptoms of AD which were analyzed by confirmatory factor analysis. Genotyping was performed using the Affymetrix 6.0 array. Three sets of analyses were conducted separately for European American (EA, n=2,357) and African-American (AA, n=812) subjects: individual SNPs, candidate genes and enriched pathways using Gene Ontology (GO) categories.

Results

The symptoms of AD formed a highly coherent single factor. No SNP approached genome-wide significance. In the EA sample, the most significant intragenic SNP was in KCNMA1, the human homolog of the slo-1 gene in C. Elegans. Genes with clusters of significant SNPs included AKAP9, PIGG and KCNMA1. In the AA sample, the most significant intragenic SNP was CEACAM6 and genes showing empirically significant SNPs included KCNQ5, SLC35B4 and MGLL. In the candidate gene based analyses, the most significant findings were with ADH1C, NFKB1 and ANKK1 in the EA sample, and ADH5, POMC, and CHRM2 in the AA sample. The ALIGATOR program identified a significant excess of associated SNPs within and near genes in a substantial number of GO categories over a range of statistical stringencies in both the EA and AA sample.

Conclusions

While we cannot be highly confident about any single result from these analyses, a number of findings were suggestive and worthy of follow-up. Although quite large samples will be needed to obtain requisite power, the study of AD symptoms in general population samples is a viable complement to case-control studies in identifying genetic risk variants for AD.

Parkinson's disease (PD) occurs in both familial and sporadic forms, and both monogenic and complex genetic factors have been identified. Early onset PD (EOPD) is particularly associated with autosomal recessive (AR) mutations, and three genes, PARK2, PARK7 and PINK1, have been found to carry mutations leading to AR disease. Since mutations in these genes account for less than 10% of EOPD patients, we hypothesized that further recessive genetic factors are involved in this disorder, which may appear in extended runs of homozygosity.

We carried out genome wide SNP genotyping to look for extended runs of homozygosity (ROHs) in 1,445 EOPD cases and 6,987 controls. Logistic regression analyses showed an increased level of genomic homozygosity in EOPD cases compared to controls. These differences are larger for ROH of 9 Mb and above, where there is a more than three-fold increase in the proportion of cases carrying a ROH. These differences are not explained by occult recessive mutations at existing loci. Controlling for genome wide homozygosity in logistic regression analyses increased the differences between cases and controls, indicating that in EOPD cases ROHs do not simply relate to genome wide measures of inbreeding. Homozygosity at a locus on chromosome19p13.3 was identified as being more common in EOPD cases as compared to controls. Sequencing analysis of genes and predicted transcripts within this locus failed to identify a novel mutation causing EOPD in our cohort.

There is an increased rate of genome wide homozygosity in EOPD, as measured by an increase in ROHs. These ROHs are a signature of inbreeding and do not necessarily harbour disease-causing genetic variants. Although there might be other regions of interest apart from chromosome 19p13.3, we lack the power to detect them with this analysis.

Recent findings from genetic epidemiology and from genome-wide association studies point strongly to a partial overlap in the genes that contribute susceptibility to schizophrenia and bipolar disorder (BD). Previous data have also directly implicated one of the best supported schizophrenia-associated loci, zinc finger binding protein 804A (ZNF804A), as showing trans-disorder effects, and the same is true for one of the best supported bipolar loci, calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C) which has also been associated with schizophrenia. We have undertaken a cross-phenotype study based upon the remaining variants that show genome-wide evidence for association in large schizophrenia and BD meta-analyses. These comprise in schizophrenia, SNPs in or in the vicinity of transcription factor 4 (TCF4), neurogranin (NRGN) and an extended region covering the MHC locus on chromosome 6. For BD, the strongly supported variants are in the vicinity of ankyrin 3, node of Ranvier (ANK3) and polybromo-1 (PBRM1). Using data sets entirely independent of their original discoveries, we observed strong evidence that the PBRM1 locus is also associated with schizophrenia (P = 0.00015) and nominally significant evidence (P < 0.05) that the NRGN and the extended MHC region are associated with BD. Moreover, considering this highly restricted set of loci as a group, the evidence for trans-disorder effects is compelling (P = 4.7 × 10−5). Including earlier reported data for trans-disorder effects for ZNF804A and CACNA1C, six out of eight of the most robustly associated loci for either disorder show trans-disorder effects.

Individuals with schizophrenia tend to be heavy smokers and are at high risk for tobacco dependence. However, the nature of the comorbidity is not entirely clear. We previously reported evidence for association of schizophrenia with SNPs and SNP haplotypes in a region of chromosome 5q containing the SPEC2, PDZ-GEF2 and ACSL6 genes. In this current study, analysis of the control subjects of the Molecular Genetics of Schizophrenia (MGS) sample showed similar pattern of association with number of cigarettes smoked per day (numCIG) for the same region. To further test if this locus is associated with tobacco smoking as measured by numCIG and FTND, we conducted replication and meta-analysis in 12 independent samples (n>16,000) for two markers in ACSL6 reported in our previous schizophrenia study. In the meta-analysis of the replication samples, we found that rs667437 and rs477084 were significantly associated with numCIG (p = 0.00038 and 0.00136 respectively) but not with FTND scores. We then used in vitro and in vivo techniques to test if nicotine exposure influences the expression of ACSL6 in brain. Primary cortical culture studies showed that chronic (5-day) exposure to nicotine stimulated ACSL6 mRNA expression. Fourteen days of nicotine administration via osmotic mini pump also increased ACSL6 protein levels in the prefrontal cortex and hippocampus of mice. These increases were suppressed by injection of the nicotinic receptor antagonist mecamylamine, suggesting that elevated expression of ACSL6 requires nicotinic receptor activation. These findings suggest that variations in the ACSL6 gene may contribute to the quantity of cigarettes smoked. The independent associations of this locus with schizophrenia and with numCIG in non-schizophrenic subjects suggest that this locus may be a common liability to both conditions.

Background

Submicroscopic, rare chromosomal copy number variants (CNVs) contribute to neurodevelopmental disorders but it is not known whether they define atypical clinical cases.

Aims

To identify whether large, rare CNVs in attention-deficit hyperactivity disorder (ADHD) are confined to a distinct clinical subgroup.

Method

A total of 567 children with ADHD aged 5–17 years were recruited from community clinics. Psychopathology was assessed using the Child and Adolescent Psychiatric Assessment. Large, rare CNVs (>500 kb, <1% frequency) were defined from single nucleotide polymorphism data.

Results

Copy number variant carriers (13.6%) showed no differences from non-carriers in ADHD symptom severity, symptom type, comorbidity, developmental features, family history or pre-/perinatal markers. The only significant difference was a higher rate of intellectual disability (24% v. 9%, χ2 = 15.5, P = 0.001). Most CNV carriers did not have intellectual disability.

Conclusions

Large, rare CNVs are not restricted to an atypical form of ADHD but may be more highly enriched in children with cognitive problems.

Objective

Although twin and family studies have shown Attention Deficit/Hyperactivity Disorder (ADHD) to be highly heritable, genetic variants influencing the trait at a genome-wide significant level have yet to be identified. As prior genome-wide association scans (GWAS) have not yielded significant results, we conducted a meta-analysis of existing studies to boost statistical power.

Method

We used data from four projects: a) the Children’s Hospital of Philadelphia (CHOP), b) phase I of the International Multicenter ADHD Genetics project (IMAGE), c) phase II of IMAGE (IMAGE II), and d) the Pfizer funded study from the University of California, Los Angeles, Washington University and the Massachusetts General Hospital (PUWMa). The final sample size consisted of 2,064 trios, 896 cases and 2,455 controls. For each study, we imputed HapMap SNPs, computed association test statistics and transformed them to Z-scores, and then combined weighted Z-scores in a meta-analysis.

Results

No genome-wide significant associations were found, although an analysis of candidate genes suggests they may be involved in the disorder.

Conclusions

Given that ADHD is a highly heritable disorder, our negative results suggest that the effects of common ADHD risk variants must, individually, be very small or that other types of variants, e.g. rare ones, account for much of the disorder’s heritability.

Objective

Although twin and family studies have shown attention deficit/hyperactivity disorder (ADHD) to be highly heritable, genetic variants influencing the trait at a genome-wide significant level have yet to be identified. Thus, additional genomewide association studies (GWAS) are needed.

Method

We used case-control analyses of 896 cases with DSM-IV ADHD genotyped using the Affymetrix 5.0 array and 2,455 repository controls screened for psychotic and bipolar symptoms genotyped using Affymetrix 6.0 arrays. A consensus SNP set was imputed using BEAGLE 3.0, resulting in an analysis dataset of 1,033,244 SNPs. The data were analyzed using a generalized linear model.

Results

No genome-wide significant associations were found. The most significant results implicated the following genes: PRKG1, FLNC, TCERG1L, PPM1H, NXPH1, PPM1H, CDH13, HK1 and HKDC1.

Conclusions

The current analyses are a useful addition to the present literature and will make a valuable contribution to future meta-analyses. The candidate gene findings are consistent with a prior meta-analysis in suggesting that the effects of ADHD risk variants must, individually, be very small and/or include multiple rare alleles.

Background

Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes.

Methodology

We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.

Principal Findings

We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD.

Significance

Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.

Summary

Background

Large, rare chromosomal deletions and duplications known as copy number variants (CNVs) have been implicated in neurodevelopmental disorders similar to attention-deficit hyperactivity disorder (ADHD). We aimed to establish whether burden of CNVs was increased in ADHD, and to investigate whether identified CNVs were enriched for loci previously identified in autism and schizophrenia.

Methods

We undertook a genome-wide analysis of CNVs in 410 children with ADHD and 1156 unrelated ethnically matched controls from the 1958 British Birth Cohort. Children of white UK origin, aged 5–17 years, who met diagnostic criteria for ADHD or hyperkinetic disorder, but not schizophrenia and autism, were recruited from community child psychiatry and paediatric outpatient clinics. Single nucleotide polymorphisms (SNPs) were genotyped in the ADHD and control groups with two arrays; CNV analysis was limited to SNPs common to both arrays and included only samples with high-quality data. CNVs in the ADHD group were validated with comparative genomic hybridisation. We assessed the genome-wide burden of large (>500 kb), rare (<1% population frequency) CNVs according to the average number of CNVs per sample, with significance assessed via permutation. Locus-specific tests of association were undertaken for test regions defined for all identified CNVs and for 20 loci implicated in autism or schizophrenia. Findings were replicated in 825 Icelandic patients with ADHD and 35 243 Icelandic controls.

Findings

Data for full analyses were available for 366 children with ADHD and 1047 controls. 57 large, rare CNVs were identified in children with ADHD and 78 in controls, showing a significantly increased rate of CNVs in ADHD (0·156 vs 0·075; p=8·9×10−5). This increased rate of CNVs was particularly high in those with intellectual disability (0·424; p=2·0×10−6), although there was also a significant excess in cases with no such disability (0·125, p=0·0077). An excess of chromosome 16p13.11 duplications was noted in the ADHD group (p=0·0008 after correction for multiple testing), a finding that was replicated in the Icelandic sample (p=0·031). CNVs identified in our ADHD cohort were significantly enriched for loci previously reported in both autism (p=0·0095) and schizophrenia (p=0·010).

Interpretation

Our findings provide genetic evidence of an increased rate of large CNVs in individuals with ADHD and suggest that ADHD is not purely a social construct.

Funding

Action Research; Baily Thomas Charitable Trust; Wellcome Trust; UK Medical Research Council; European Union.

We investigated the involvement of rare (<1%) copy number variants (CNVs) in 471 cases of schizophrenia and 2792 controls that had been genotyped using the Affymetrix GeneChip® 500K Mapping Array. Large CNVs >1 Mb were 2.26 times more common in cases (P = 0.00027), with the effect coming mostly from deletions (odds ratio, OR = 4.53, P = 0.00013) although duplications were also more common (OR = 1.71, P = 0.04). Two large deletions were found in two cases each, but in no controls: a deletion at 22q11.2 known to be a susceptibility factor for schizophrenia and a deletion on 17p12, at 14.0–15.4 Mb. The latter is known to cause hereditary neuropathy with liability to pressure palsies. The same deletion was found in 6 of 4618 (0.13%) cases and 6 of 36 092 (0.017%) controls in the re-analysed data of two recent large CNV studies of schizophrenia (OR = 7.82, P = 0.001), with the combined significance level for all three studies achieving P = 5 × 10−5. One large duplication on 16p13.1, which has been previously implicated as a susceptibility factor for autism, was found in three cases and six controls (0.6% versus 0.2%, OR = 2.98, P = 0.13). We also provide the first support for a recently reported association between deletions at 15q11.2 and schizophrenia (P = 0.026). This study confirms the involvement of rare CNVs in the pathogenesis of schizophrenia and contributes to the growing list of specific CNVs that are implicated.

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission.1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases2, but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10−9). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.

We have studied the impact of the apolipoprotein E gene (APOE) on the chromosome 19 linkage peak from an analysis of sib-pairs affected by Alzheimer’s disease. We genotyped 417 affected sib-pairs (ASPs) collected in Sweden and Norway (SWE), the UK and the USA for 10 microsatellite markers on chromosome 19. The highest Zlr (3.28, chromosome-wide P-value 0.036) from the multipoint linkage analysis was located approximately 1 Mb from APOE, at marker D19S178. The linkage to chromosome 19 was well explained by APOE in the whole sample as well as in the UK and USA subsamples, as identity by descent (IBD) increased with the number of ε4 alleles in ASPs. There was a suggestion from the SWE subsample that linkage was higher than would be expected from APOE alone, although the test for this did not reach formal statistical significance. There was also a significant age at onset (aao) effect on linkage to chromosome 19q13 in the whole sample, which manifested itself as increased IBD sharing in relative pairs with lower mean aao. This effect was partially, although not completely, explained by APOE. The aao effect varied considerably between the different subsamples, with most of the effect coming from the UK sample. The other samples showed smaller effects in the same direction, but these were not significant.

Background

We reported genome-wide significant linkage on chromosome 15q25.3–26.2 to recurrent early-onset major depressive disorder (MDD-RE). Here we present initial linkage-disequilibrium (LD) fine-mapping of this signal and sequence analysis of NTRK3 (neurotrophic receptor kinase-3), a biologically plausible candidate gene.

Methods

In 300 pedigrees informative for family-based association, 1195 individuals were genotyped for 795 SNPs. We resequenced 21 exons and seven highly conserved NTRK3 regions in 176 MDD-RE cases to test for an excess of rare functional variants, and in 176 controls for case-control analysis of common variants.

Results

LD mapping showed nominally significant association in nine genes–NTRK3, FLJ12484, RHCG, DKFZp547K1113, VPS33B, SV2B, SLCO3A1, RGMA and MCTP2–with MDD-RE. In NTRK3, five SNPs had nominally significant p-values (0.035–0.001). Sequence analysis revealed 35 variants (24 novel, including nine rare exonic); the number of rare variants did not exceed chance expectation. Case-control analysis of 13 common variants showed modest nominal association of MDD-RE with rs4887379, rs6496463 and rs3825882 (p = 0.008, 0.048, and 0.034), which were in partial LD with four of five associated SNPs from the family-based experiment.

Conclusions

Common variants in NTRK3 or one of the other genes identified might play a role in MDD-RE. However, much larger studies will be required for full evaluation of this region.

Background

DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.

Results

k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.

Conclusion

Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.

We have used the genome-wide marker genotypes from Genetic Analysis Workshop 15 Problem 2 to explore joint evidence for genetic linkage to rheumatoid arthritis across several samples. The data consisted of four high-density genome scans on samples selected for rheumatoid arthritis. We cleaned the data, removed intermarker linkage disequilibrium, and assembled the samples onto a common genetic map using genome sequence positions as a reference for map interpolation. The individual studies were combined first at the genotype level (mega-analysis) prior to a multipoint linkage analysis on the combined sample, and second using the genome scan meta-analysis method after linkage analysis of each sample. The two approaches were compared, and give strong support to the HLA locus on chromosome 6 as a susceptibility locus. Other regions of interest include loci on chromosomes 11, 2, and 12.

Rheumatoid arthritis is the most common systematic autoimmune disease and its etiology is believed to have both strong genetic and environmental components. We demonstrate the utility of including genetic and clinical phenotypes as covariates within a linkage analysis framework to search for rheumatoid arthritis susceptibility loci. The raw genotypes of 1302 affected relative pairs were combined from four large family-based samples (North American Rheumatoid Arthritis Consortium, United Kingdom, European Consortium on Rheumatoid Arthritis Families, and Canada). The familiality of the clinical phenotypes was assessed. The affected relative pairs were subjected to autosomal multipoint affected relative-pair linkage analysis. Covariates were included in the linkage analysis to take account of heterogeneity within the sample. Evidence of familiality was observed with age at onset (p << 0.001) and rheumatoid factor (RF) IgM (p << 0.001), but not definite erosions (p = 0.21). Genome-wide significant evidence for linkage was observed on chromosome 6. Genome-wide suggestive evidence for linkage was observed on chromosomes 13 and 20 when conditioning on age at onset, chromosome 15 conditional on gender, and chromosome 19 conditional on RF IgM after allowing for multiple testing of covariates.

Using parametric and nonparametric techniques, our study investigated the presence of single locus and pairwise effects between 20 markers of the Genetic Analysis Workshop 15 (GAW15) North American Rheumatoid Arthritis Consortium (NARAC) candidate gene data set (Problem 2), analyzing 463 independent patients and 855 controls. Specifically, our work examined the correspondence between logistic regression (LR) analysis of single-locus and pairwise interaction effects, and random forest (RF) single and joint importance measures. For this comparison, we selected small but stable RFs (500 trees), which showed strong correlations (r~0.98) between their importance measures and those by RFs grown on 5000 trees. Both RF importance measures captured most of the LR single-locus and pairwise interaction effects, while joint importance measures also corresponded to full LR models containing main and interaction effects. We furthermore showed that RF measures were particularly sensitive to data imputation. The most consistent pairwise effect on rheumatoid arthritis was found between two markers within MAP3K7IP2/SUMO4 on 6q25.1, although LR and RFs assigned different significance levels.

Within a hypothetical two-stage design, pairwise LR analysis of all markers with significant RF single importance would have reduced the number of possible combinations in our small data set by 61%, whereas joint importance measures would have been less efficient for marker pair reduction. This suggests that RF single importance measures, which are able to detect a wide range of interaction effects and are computationally very efficient, might be exploited as pre-screening tool for larger association studies. Follow-up analysis, such as by LR, is required since RFs do not indicate high-risk genotype combinations.