Genome-wide association analysis of chronic lymphocytic leukaemia, Hodgkin lymphoma and multiple myeloma identifies pleiotropic risk loci.

B-cell malignancies (BCM) originate from the same cell of origin, but at different maturation stages and have distinct clinical phenotypes. Although genetic risk variants for individual BCMs have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. We explored genome-wide association studies of chronic lymphocytic leukaemia (CLL, N = 1,842), Hodgkin lymphoma (HL, N = 1,465) and multiple myeloma (MM, N = 3,790). We identified a novel pleiotropic risk locus at 3q22.2 (NCK1, rs11715604, P = 1.60 × 10-9) with opposing effects between CLL (P = 1.97 × 10-8) and HL (P = 3.31 × 10-3). Eight established non-HLA risk loci showed pleiotropic associations. Within the HLA region, Ser37 + Phe37 in HLA-DRB1 (P = 1.84 × 10-12) was associated with increased CLL and HL risk (P = 4.68 × 10-12), and reduced MM risk (P = 1.12 × 10-2), and Gly70 in HLA-DQB1 (P = 3.15 × 10-10) showed opposing effects between CLL (P = 3.52 × 10-3) and HL (P = 3.41 × 10-9). By integrating eQTL, Hi-C and ChIP-seq data, we show that the pleiotropic risk loci are enriched for B-cell regulatory elements, as well as an over-representation of binding of key B-cell transcription factors. These data identify shared biological pathways influencing the development of CLL, HL and MM. The identification of these risk loci furthers our understanding of the aetiological basis of BCMs.

Genome-wide association study identifies variation at 6q25.1 associated with survival in multiple myeloma.

Survival following a diagnosis of multiple myeloma (MM) varies between patients and some of these differences may be a consequence of inherited genetic variation. In this study, to identify genetic markers associated with MM overall survival (MM-OS), we conduct a meta-analysis of four patient series of European ancestry, totalling 3,256 patients with 1,200 MM-associated deaths. Each series is genotyped for ∼600,000 single nucleotide polymorphisms across the genome; genotypes for six million common variants are imputed using 1000 Genomes Project and UK10K as the reference. The association between genotype and OS is assessed by Cox proportional hazards model adjusting for age, sex, International staging system and treatment. We identify a locus at 6q25.1 marked by rs12374648 associated with MM-OS (hazard ratio=1.34, 95% confidence interval=1.22-1.48, P=4.69 × 10(-9)). Our findings have potential clinical implications since they demonstrate that inherited genotypes can provide prognostic information in addition to conventional tumor acquired prognostic factors.

Novel genetic matching methods for handling population stratification in genome-wide association studies.

BACKGROUND: A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure. RESULTS: We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer's disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach. CONCLUSIONS: Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.

Rare variant testing of imputed data: an analysis pipeline typified.

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group ('bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of 'rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

Whole exome sequencing of distant relatives in multiplex families implicates rare variants in candidate genes for oral clefts.

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Increased levels of glucocorticoid receptors and enhanced glucocorticoid receptor auto-regulation after hydrocortisone challenge in B-lymphoblastoids from patients with affective disorders.

The stress response is mediated by a negative feedback effect of glucocorticoids on corticosteroid receptors. Here, we examine the potential contribution of these receptors and their response to a glucocorticoid challenge to dysfunctions of the hypothalamic-pituitary-adrenal axis reported for patients with affective disorders. In a pilot-study, we established B-lymphoblastoid cell lines from patients suffering from affective disorders and healthy subjects and measured the quantity of glucocorticoid receptors at steady state conditions after 12-weeks cell culture. After short-term incubation with 0.1 microM hydrocortisone for 48 h, the decrease of glucocorticoid receptors was also investigated. After 12-weeks cell culture, we found a significantly higher number of cytosolic glucocorticoid receptors in B-lymphoblastoids from patients (B(max)=804.9+/-342.5 fmol/mg protein) compared to those from healthy subjects (B(max)=576.9+/-190.3 fmol/mg protein: p=0.045; t-test). The increase of the glucocorticoid receptor level in the group of patients could be attributed largely to the higher number of these receptors measured in B-lymphoblastoids of patients suffering from major depressive disorder. The in vitro regulation of glucocorticoid receptors in response to 0.1 microM hydrocortisone for 48 h resulted in a significantly larger decrease in cultures of B-lymphoblastoids derived from patients (to 32.9+/-7.5%) than in those from healthy subjects (to 45.8+/-8.2%). The stronger decrease of glucocorticoid receptors in the group of patients (p=0.0001; t-test) was independent of the duration of illness and medication, suggesting a trait-like characteristic of the response.