Genetic studies of schizophrenia have implicated numerous risk loci including several copy number variants (CNVs) of large effect and hundreds of loci of small effect. In only a few cases has a ...specific gene been clearly identified. Rare CNVs affecting a single gene offer a potential avenue to discovering schizophrenia risk genes.
CNVs were generated from exome sequencing of 4913 schizophrenia cases and 6188 control subjects from Sweden. We integrated two CNV calling methods (XHMM and ExomeDepth) to expand our set of single-gene CNVs and leveraged two different approaches for validating these variants (quantitative polymerase chain reaction and NanoString).
We found a significant excess of all rare CNVs (deletions: p = .0004, duplications: p = .0006) and single-gene CNVs (deletions: p = .04, duplications: p = .03) in schizophrenia cases compared with control subjects. An expanded set of CNVs generated from integrating multiple approaches showed a significant burden of deletions in 11 of 21 gene sets previously implicated in schizophrenia and across all genes in those sets (p = .008), although no tests survived correction. We performed an extensive validation of all deletions in the significant set of voltage-gated calcium channels among CNVs called from both exome sequencing and genotyping arrays. In total, 4 exonic, single-gene deletions were validated in schizophrenia cases and none in control subjects (p = .039), of which all were identified by exome sequencing.
These results point to the potential contribution of single-gene CNVs to schizophrenia, indicate that the utility of exome sequencing for CNV calling has yet to be maximized, and note that single-gene CNVs should be included in gene-focused studies using other classes of variation.
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.
We undertook a two-stage genome-wide association study (GWAS) of Alzheimer's disease (AD) involving over 16,000 individuals, the most powerful AD GWAS to date. In stage 1 (3,941 cases and 7,848 ...controls), we replicated the established association with the apolipoprotein E (APOE) locus (most significant SNP, rs2075650, P = 1.8 × 10−157) and observed genome-wide significant association with SNPs at two loci not previously associated with the disease: at the CLU (also known as APOJ) gene (rs11136000, P = 1.4 × 10−9) and 5′ to the PICALM gene (rs3851179, P = 1.9 × 10−8). These associations were replicated in stage 2 (2,023 cases and 2,340 controls), producing compelling evidence for association with Alzheimer's disease in the combined dataset (rs11136000, P = 8.5 × 10−10, odds ratio = 0.86; rs3851179, P = 1.3 × 10−9, odds ratio = 0.86).
A genome-wide association study was carried out in 1020 case subjects with recurrent early-onset major depressive disorder (MDD) (onset before age 31) and 1636 control subjects screened to exclude ...lifetime MDD. Subjects were genotyped with the Affymetrix 6.0 platform. After extensive quality control procedures, 671 424 autosomal single nucleotide polymorphisms (SNPs) and 25 068 X chromosome SNPs with minor allele frequency greater than 1% were available for analysis. An additional 1 892 186 HapMap II SNPs were analyzed based on imputed genotypic data. Single-SNP logistic regression trend tests were computed, with correction for ancestry-informative principal component scores. No genome-wide significant evidence for association was observed, assuming that nominal P<5 × 10(-8) approximates a 5% genome-wide significance threshold. The strongest evidence for association was observed on chromosome 18q22.1 (rs17077540, P=1.83 × 10(-7)) in a region that has produced some evidence for linkage to bipolar-I or -II disorder in several studies, within an mRNA detected in human brain tissue (BC053410) and approximately 75 kb upstream of DSEL. Comparing these results with those of a meta-analysis of three MDD GWAS data sets reported in a companion article, we note that among the strongest signals observed in the GenRED sample, the meta-analysis provided the greatest support (although not at a genome-wide significant level) for association of MDD to SNPs within SP4, a brain-specific transcription factor. Larger samples will be required to confirm the hypothesis of association between MDD (and particularly the recurrent early-onset subtype) and common SNPs.
We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium ...(ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10(-5). We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10(-17); including ADGC data, meta P = 5.0 × 10(-21)) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10(-14); including ADGC data, meta P = 1.2 × 10(-16)) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome-wide significance: CD2AP (GERAD+, P = 8.0 × 10(-4); including ADGC data, meta P = 8.6 × 10(-9)), CD33 (GERAD+, P = 2.2 × 10(-4); including ADGC data, meta P = 1.6 × 10(-9)) and EPHA1 (GERAD+, P = 3.4 × 10(-4); including ADGC data, meta P = 6.0 × 10(-10)).
Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is two to seven times more common in male individuals than in female individuals. We examined two putative genetic ...mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases.
We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (n = 20,183 cases, n = 35,191 controls) and Swedish population register data (n = 77,905 cases, n = 1,874,637 population controls).
Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that female individuals with ADHD may be at especially high risk for certain comorbid developmental conditions (i.e., autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score analysis did not support a higher burden of ADHD common risk variants in female cases (odds ratio confidence interval = 1.02 0.98–1.06, p = .28). In contrast, epidemiological sibling analyses revealed that the siblings of female individuals with ADHD are at higher familial risk for ADHD than the siblings of affected male individuals (odds ratio confidence interval = 1.14 1.11–1.18, p = 1.5E-15).
Overall, this study supports a greater familial burden of risk in female individuals with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence.
We evaluate a method for the incorporation of covariates into linkage analysis using the Genetic Analysis Workshop 14 simulated data. Focusing on a randomly chosen replicate (42) we investigated the ...effect of the 12 subclinical phenotypes, sex, population, and parent-of-origin on the linkage signal from a model-free linkage analysis of Kofendrerd Personality Disorder.
We detected a linkage peak on chromosome 1, at about 175 cM, which varied depending upon individuals' status for subclinical phenotype b. A linkage peak on chromosome 3 (310 cM) was found not to depend upon subclinical phenotype status. Further peaks were found on chromosomes 5 (12 cM), 9 (4 cM), and 10 (95 cM), depending on the status of subclinical phenotypes a, k, and c/d/g, respectively.
Retrospective comparison of our results with the simulation model showed correct identification of disease loci D1-5 on chromosomes 1, 3, 5, 9 and 10, respectively.
Genome-wide association (GWAS) analyses have identified susceptibility loci for many diseases, but most risk for any complex disorder remains unattributed. There is therefore scope for complementary ...approaches to these data sets. Gene-wide approaches potentially offer additional insights. They might identify association to genes through multiple signals. Also, by providing support for genes rather than single nucleotide polymorphisms (SNPs), they offer an additional opportunity to compare the results across data sets. We have undertaken gene-wide analysis of two GWAS data sets: schizophrenia and bipolar disorder. We performed two forms of analysis, one based on the smallest P-value per gene, the other on a truncated product of P method. For each data set and at a range of statistical thresholds, we observed significantly more SNPs within genes (P(min) for excess<0.001) showing evidence for association than expected whereas this was not true for extragenic SNPs (P(min) for excess>0.1). At a range of thresholds of significance, we also observed substantially more associated genes than expected (P(min) for excess in schizophrenia=1.8 x 10(-8), in bipolar=2.4 x 10(-6)). Moreover, an excess of genes showed evidence for association across disorders. Among those genes surpassing thresholds highly enriched for true association, we observed evidence for association to genes reported in other GWAS data sets (CACNA1C) or to closely related family members of those genes including CSF2RB, CACNA1B and DGKI. Our analyses show that association signals are enriched in and around genes, large numbers of genes contribute to both disorders and gene-wide analyses offer useful complementary approaches to more standard methods.
Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable ...variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.
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