A two-stage meta-analysis identifies several new loci for Parkinson's Disease

A previous genome-wide association (GWA) meta-analysis of 12,386 PD cases and 21,026 controls conducted by the International Parkinson's Disease Genomics Consortium (IPDGC) discovered or confirmed 11 Parkinson's disease (PD) loci. This first analysis of the two-stage IPDGC study focused on the set of loci that passed genome-wide significance in the first stage GWA scan. However, the second stage genotyping array, the ImmunoChip, included a larger set of 1,920 SNPs selected on the basis of the GWA analysis. Here, we analyzed this set of 1,920 SNPs, and we identified five additional PD risk loci (combined p<5×10−10, PARK16/1q32, STX1B/16p11, FGF20/8p22, STBD1/4q21, and GPNMB/7p15). Two of these five loci have been suggested by previous association studies (PARK16/1q32, FGF20/8p22), and this study provides further support for these findings. Using a dataset of post-mortem brain samples assayed for gene expression (n = 399) and methylation (n = 292), we identified methylation and expression changes associated with PD risk variants in PARK16/1q32, GPNMB/7p15, and STX1B/16p11 loci, hence suggesting potential molecular mechanisms and candidate genes at these risk loci

Copy-Number Variation: The Balance between Gene Dosage and Expression in Drosophila melanogaster

Copy-number variants (CNVs) reshape gene structure, modulate gene expression, and contribute to significant phenotypic variation. Previous studies have revealed CNV patterns in natural populations of Drosophila melanogaster and suggested that selection and mutational bias shape genomic patterns of CNV. Although previous CNV studies focused on heterogeneous strains, here, we established a number of second-chromosome substitution lines to uncover CNV characteristics when homozygous. The percentage of genes harboring CNVs is higher than found in previous studies. More CNVs are detected in homozygous than heterozygous substitution strains, suggesting the comparative genomic hybridization arrays underestimate CNV owing to heterozygous masking. We incorporated previous gene expression data collected from some of the same substitution lines to investigate relationships between CNV gene dosage and expression. Most genes present in CNVs show no evidence of increased or diminished transcription, and the fraction of such dosage-insensitive CNVs is greater in heterozygotes. More than 70% of the dosage-sensitive CNVs are recessive with undetectable effects on transcription in heterozygotes. A deficiency of singletons in recessive dosage-sensitive CNVs supports the hypothesis that most CNVs are subject to negative selection. On the other hand, relaxed purifying selection might account for the higher number of protein–protein interactions in dosage-insensitive CNVs than in dosage-sensitive CNVs. Dosage-sensitive CNVs that are upregulated and downregulated coincide with copy-number increases and decreases. Our results help clarify the relation between CNV dosage and gene expression in the D. melanogaster genome

Genome-wide Association Studies

Genome-wide association studies (GWAS) are a powerful hypothesis-free tool for the dissection of susceptibility to common heritable human diseases, including osteoporosis. To date, more than 2000 loci for common human diseases have been identified by GWAS. Success using the GWAS model depends on genetic risk being determined by shared stretches of DNA carried with different frequencies in cases and controls, inherited from ancient ancestors, termed the “common disease–common variant” hypothesis. Not all disease risk is caused by common variants, however, and thus GWAS will not detect all variants involved. Successful GWAS performance requires careful quality control, especially as the effect sizes under study are modest, and there are multiple potential sources of error. Conservative interpretation, use of stringent significance thresholds, and replication in independent cohorts are required to ensure results are robust. Despite these challenging parameters, much has been learnt from GWAS and, as the approach matures and is modified to identify a wider range of variants, significantly more will be learnt about the etiopathogenesis of common diseases such as osteoporosis

Bayes Factor Based on the Trend Test Incorporating Hardy-Weinberg Disequilibrium: More Power to Detect Genetic Association

10.1111/j.1469-1809.2012.00714.xAnnals of Human Genetics764301-311ANHG

Single marker association analysis for unrelated samples

10.1007/978-1-61779-555-8_18Methods in Molecular Biology850347-35

What role for genetics in the prediction of multiple sclerosis?

For most of us, the foundations of our understanding of genetics were laid by considering Mendelian diseases in which familial recurrence risks are high, and mutant alleles are both necessary and sufficient. One consequence of this deterministic teaching is that our conceptualization of genetics tends to be dominated by the notion that the genetic aspects of disease are caused by rare alleles exerting large effects. Unfortunately, the preconceptions that flow from this training are frequently erroneous and misleading in the context of common traits, where familial recurrence risks are modest, and for the most part the relevant alleles are neither rare, necessary, nor sufficient. For these common traits, the genetic architecture is far more complex, with susceptibility rather than causality resulting from the combined effects of many alleles, each exerting only a modest effect on risk. None of these alleles is sufficient to cause disease on its own, and none is essential for the development of disease. Furthermore, most are carried by large sections of the population, the vast majority of which does not develop the disease. One consequence of our innate belief in the Mendelian paradigm is that we have an inherent expectation that knowledge about the genetic basis for a disease should allow genetic testing and thereby accurate risk prediction. There is an inevitable feeling that the same should be true in complex disease, but is it

Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the Schizophrenia PGC

The Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC) highlighted 81 single-nucleotide polymorphisms (SNPs) with moderate evidence for association to schizophrenia. After follow-up in independent samples, seven loci attained genome-wide significance (GWS), but multi-locus tests suggested some SNPs that did not do so represented true associations. We tested 78 of the 81 SNPs in 2640 individuals with a clinical diagnosis of schizophrenia attending a clozapine clinic (CLOZUK), 2504 cases with a research diagnosis of bipolar disorder, and 2878 controls. In CLOZUK, we obtained significant replication to the PGC-associated allele for no fewer than 37 (47%) of the SNPs, including many prior GWS major histocompatibility complex (MHC) SNPs as well as 3/6 non-MHC SNPs for which we had data that were reported as GWS by the PGC. After combining the new schizophrenia data with those of the PGC, variants at three loci (ITIH3/4, CACNA1C and SDCCAG8) that had not previously been GWS in schizophrenia attained that level of support. In bipolar disorder, we also obtained significant evidence for association for 21% of the alleles that had been associated with schizophrenia in the PGC. Our study independently confirms association to three loci previously reported to be GWS in schizophrenia, and identifies the first GWS evidence in schizophrenia for a further three loci. Given the number of independent replications and the power of our sample, we estimate 98% (confidence interval (CI) 78–100%) of the original set of 78 SNPs represent true associations. We also provide strong evidence for overlap in genetic risk between schizophrenia and bipolar disorder