Wellcome Trust Case Control Consortium Research Articles

Using the Bayesian variational spike and slab model in a genome-wide association study for finding associated loci with bipolar disorder.

The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined.

The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.

Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis.

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.

Dimensionality reduction approach for many-objective epistasis analysis.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes

Aims/hypothesisThe reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults.MethodsWe analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants.ResultsThe T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4).Conclusions/interpretationThe inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.Graphical abstract

A guidance of model selection for genomic prediction based on linear mixed models for complex traits.

Brain imaging outcomes are important for Alzheimer’s disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer’s Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Distinct Metabolite Profile in Pemphigus Vulgaris

Because of their high prevalence, (co)morbidity, still unsatisfactory treatment options, and imposed economic impact, chronic inflammatory skin diseases pose a major medical burden (Lamberts et al., 2019). In the past, multidimensional datasets unraveled novel pathways of disease pathogenesis and have led to the discovery of innovative therapeutic targets, exemplified by the discovery of Jak/nonreceptor tyrosine protein kinase 2 inhibitors for the treatment of psoriasis and alopecia areata (Genetic Analysis of Psoriasis Consortium & the Wellcome Trust Case Control Consortium 2 et al., 2010; Xing et al., 2014).

A systematic analysis of gene\u2013gene interaction in multiple sclerosis

BackgroundFor the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to investigate the interactions between distant loci, or epistasis. Among them, the recently proposed EpiGWAS models the interactions between a target variant and the rest of the genome. However, applying this approach to studying interactions along all genes of a disease map is not straightforward. Here, we propose a pipeline to that effect, which we illustrate by investigating a multiple sclerosis GWAS dataset from the Wellcome Trust Case Control Consortium 2 through 19 disease maps from the MetaCore pathway database.ResultsFor each disease map, we build an epistatic network by connecting the genes that are deemed to interact. These networks tend to be connected, complementary to the disease maps and contain hubs. In addition, we report 4 epistatic gene pairs involving missense variants, and 25 gene pairs with a deleterious epistatic effect mediated by eQTLs. Among these, we highlight the interaction of GLI-1 and SUFU, and of IP10 and NF-kappaB, as they both match known biological interactions. The latter pair is particularly promising for therapeutic development, as both genes have known inhibitors.ConclusionsOur study showcases the ability of EpiGWAS to uncover biologically interpretable epistatic interactions that are potentially actionable for the development of combination therapy.

A data-adaptive Bayesian regression approach for polygenic risk prediction.

MotivationPolygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy.ResultsSimulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods.Availability and implementationThe R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred.Supplementary information Supplementary data are available at Bioinformatics online.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity.

Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

EpiMC: Detecting Epistatic Interactions Using Multiple Clusterings.

Detecting single nucleotide polymorphisms (SNPs) interactions is crucial to identify susceptibility genes associated with complex human diseases in genome-wide association studies. Clustering-based approaches are widely used in reducing search space and exploring potential relationships between SNPs in epistasis analysis. However, these approaches all only use a single measure to filter out nonsignificant SNP combinations, which may be significant ones from another perspective. In this paper, we propose a two-stage approach named EpiMC (Epistatic Interactions detection based on Multiple Clusterings) that employs multiple clusterings to obtain more precise candidate sets and more comprehensively detect high-order interactions based on these sets. In the first stage, EpiMC proposes a matrix factorization based multiple clusterings algorithm to generate multiple diverse clusterings, each of which divide all SNPs into different clusters. This stage aims to reduce the chance of filtering out potential candidates overlooked by a single clustering and groups associated SNPs together from different clustering perspectives. In the next stage, EpiMC considers both the single-locus effects and interaction effects to select high-quality disease associated SNPs, and then uses Jaccard similarity to get candidate sets. Finally, EpiMC uses exhaustive search on the obtained small candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpiMC has a better performance in detecting high-order interactions than state-of-the-art solutions. On the Wellcome Trust Case Control Consortium (WTCCC) dataset, EpiMC detects several significant epistatic interactions associated with breast cancer (BC) and age-related macular degeneration (AMD), which again corroborate the effectiveness of EpiMC.

JAK2 GGCC (46/1) Haplotype in Unprovoked Venous Thrombotic Events

Background: JAK2 GGCC 46/1 haplotype can be represented by four main SNPs (rs3780367, rs10974944, rs12343867, and rs1159782) which replace one cytosine and three thymidines by two guanosines and two cytosines, generating a “GGCC” combination. These four SNPs located on JAK2 introns 10, 12, 14, and 15, respectively, and are always inherited together, being in complete linkage disequilibrium. The 46/1 component of the name came from Jones et al. study where the haplotype structure of the JAK2 gene was mapped using 14 SNPs genotyped by the Wellcome Trust Case Control Consortium (WTCCC) in 1500 healthy blood donors. Two haplotypes (numbers 46 and 1) were found to be identical except for one SNP, and they have a combined frequency of 0.24 in healthy individuals. Numerous observational studies associate this haplotype with myeloproliferative neoplasms (MPNs), as well as splanchnic vein thrombosis (SVT) and non-splanchnic vein thrombosis (non-SVT). In contrast to 24% frequency noted in healthy population, the frequency goes up to 40-80% in JAK2 V617F mutated MPN, and in 64% of those with JAK2 exon 12 mutations (Anelli et al. IJMS, 2018). We herein report our study of JAK2 GGCC (46/1) Haplotype in unprovoked Venous Thrombotic Events (VTE) in patients with negative thrombophilia workup, including negative JAK2 V617F mutation.Methods: We retrospectively identified patients positive for one of the two SNPs (rs12343867 and rs10974900) and unprovoked venous thrombotic among adult patients with negative thrombophilia workup (including JAK2 mutation) treated at tertiary care center from January 2018 to January 2021.Results: We have identified 8 patients, Table (1), that were positive for JAK2 46/1 haplotype SNPs, of whom 62.5% were homozygous 2/2, 25% heterozygous 1/2, while only 12.5% harbor homozygous 1/1 (a normal variant of JAK2 haplotype). The median age 48.5 years (23-65), and the majority (87.5%) were females. Thrombosis site was noted to be SVT in half of the patients, while non-SVT was noted in the other half (12.5% had cerebral vein thrombosis, 12.5% had deep venous thrombosis, 12.5% had a pulmonary embolism, and 12.5% had jugular vein thrombosis). Half of the patients had more than one site venous thrombosis and the other half had only one site. Around 37.50% of the patients had recurrent venous thrombosis on top of therapeutic anticoagulation. Two patients (25%) had high hemoglobin (17.4/16.7) g/dl, but did not fulfill the criteria for polycythemia vera diagnosis (of whom one is a male smoker and one was a female). None of the patients had leukocytosis or thrombocytosis. By imaging, one patient had mild splenomegaly which could be related to SVT.Conclusion: We report on a potential correlation between unprovoked thrombotic events, mainly venous thrombotic events, with JAK2 46/1 haplotype in patients with a negative thrombophilia workup, a finding that merit further investigation. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children.

Genetic susceptibility to multiple sclerosis in African Americans.

ObjectiveTo explore the nature of genetic-susceptibility to multiple sclerosis (MS) in African-Americans.BackgroundRecently, the number of genetic-associations with MS has exploded although the MS-associations of specific haplotypes within the major histocompatibility complex (MHC) have been known for decades. For example, the haplotypes HLA-DRB1*15:01~HLA-DQB1*06:02, and HLA-DRB1*03:01~ HLA-DQB1*02:01 have odds ratios (ORs) for an MS-association orders of magnitude stronger than many of these newly-discovered associations. Nevertheless, all these haplotypes are part of much larger conserved extended haplotypes (CEHs), which span both the Class I and Class II MHC regions. African-Americans are at greater risk of developing MS compared to a native Africans but at lesser risk compared to Europeans. It is the purpose of this manuscript to explore the relationship between MS-susceptibility and the CEH make-up of our African-American cohort.Design/methodsThe African-American (AA) cohort consisted of 1,305 patients with MS and 1,155 controls, who self-identified as being African-American. For comparison, we used the 18,492 controls and 11,144 MS-cases from the predominantly European Wellcome Trust Case Control Consortium (WTCCC) and the 28,557 phased native Africans from the multinational “Be the Match” registry. The WTCCC and the African-Americans were phased at each of five HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRB1 and HLA-DQB1) and the at 11 SNPs (10 of which were in non-coding regions) surrounding the Class II region of the DRB1 gene using previously-published probabilistic phasing algorithms.ResultsOf the 32 most frequent CEHs, 18 (56%) occurred either more frequently or exclusively in Africans) whereas 9 (28%) occurred more frequently or exclusively in Europeans. The remaining 5 CEHs occurred in neither control group although, likely, these were African in origin. Eight of these CEHs carried the DRB1*15:03~DQB1*06:02~a36 haplotype and three carried the DRB1*15:01~DQB1*06:02~a1 haplotype. In African Americans, a single-copy of the European CEH (03:01_07:02_07:02_15:01_06:02_a1) was associated with considerable MS-risk (OR = 3.30; p = 0.0001)–similar to that observed in the WTCCC (OR = 3.25; p<10−168). By contrast, the MS-risk for the European CEH (02:01_07:02_07:02_15:01_06:02_a1) was less (OR = 1.49; ns)–again, similar to the WTCCC (OR = 2.2; p<10−38). Moreover, four African haplotypes were “protective” relative to a neutral reference, to three European CEHs, and also to the five other African CEHs.ConclusionsThe common CEHs in African Americans are divisible into those that are either African or European in origin, which are derived without modification from their source population. European CEHs, linked to MS-risk, in general, had similar impacts in African-Americans as they did in Europeans. By contrast, African CEHs had mixed MS-risks. For a few, the MS-risk exceeded that in a neutral-reference group whereas, for many others, these CEHs were “protective”–perhaps providing a partial rationale for the lower MS-risk in African-Americans compared to European-Americans.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The ‘sociology of expectations’ has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the ‘problematic’ found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

Large Multi-Ethnic Genome Wide Association Study Reveals Novel Genetic Associations with Childhood Acute Lymphoblastic Leukemia at Chromosomes 8q24 and 17q12

Genome-wide association studies (GWAS) have revealed heritable genetic risk factors for childhood acute lymphoblastic leukemia (ALL), highlighting the role of lymphoid development and cell cycle control genes in disease etiology. Most studies have focused on non-Latino white populations; however, the highest global rates of ALL are found in Latinos in the US and Latin America. To identify novel genetic susceptibility alleles, we carried out a large, multi-ethnic GWAS of ALL in the Californian population.Childhood ALL cases were identified using the California Cancer Registry, and age, gender, and ethnicity-matched controls were identified from the Department of Vital Statistics. Newborn bloodspots from California-born children were obtained from the California Biobank Program. Genome-wide SNP genotyping was carried out for 1,949 Latino, 1,184 non-Latino white, and 130 African-American cases as well as 3,506 controls, using the Affymetrix Axiom World Latin array. Additional controls (n=12,471) from the Kaiser Resource for Genetic Epidemiology Research on Aging Cohort (GERA) were genotyped on the same array. For increased statistical power, we carried out a meta-analysis of the three ethnicity-stratified GWAS. Replication of putative novel loci was performed in two independent datasets: the European-ancestry based Children's Oncology Group (COG) (959 cases, plus 2624 controls from the Wellcome Trust Case-Control Consortium), and in Latino subjects from the California Childhood Leukemia Study (CCLS) (530 cases, 511 controls). Local imputation across top association loci was carried out, using 1000 Genomes Project SNP data as a reference, to identify causal variants.Our childhood ALL GWAS meta-analysis of 3,263 cases and 15,977 controls identified two novel genome-wide significant SNP loci (P<5.0 x 10-8) at chromosomes 8q24 (OR=1.27, 95% CI:1.17-1.38) and 17q12 (OR=1.18, 95% CI:1.11-1.25) that replicated in COG and CCLS datasets. The 8q24 locus lies within a 2.5Mb region containing known GWAS hits for multiple phenotypes, including several cancer types and developmental traits. Imputation narrowed the locus to a ~100kb intergenic region, including top hit SNP rs4617118, that demonstrates chromatin looping with the MYC oncogene in lymphoblastoid cell lines. The 17q12 association peak encompasses the lymphoid transcription factor IKZF3, as well as ZPBP2, GSDMB, and ORMDL3. Top SNP rs2290400 disrupts a BLIMP-1 binding motif, and is a highly significant expression quantitative trait locus (eQTL) for both GSDMB and ORMDL3 . Rs2290400 is also a GWAS hit for type 1 diabetes and asthma, with the ALL risk allele conferring protection against these autoimmune conditions. In addition to novel loci, we replicated established GWAS hits in ARID5B, IKZF1, CEBPE, PIP4K2A, and CDKN2A at genome-wide significance, and in GATA3 at P=5.2x10-6. Recently identified loci at LHPP and ELK3 also replicated, at P=5.7x10-6 and P=2.1x10-3 respectively.Capitalizing on the large proportion of Latinos in our study, we utilized the reduced linkage disequilibrium of this genetically admixed population in combination with fine-mapping to identify new putative causal variants at known ALL-associated loci.Our large, multi-ethnic GWAS of childhood ALL has identified two novel genetic associations, at loci that function in hematopoiesis (17q12) and B-cell proliferation (MYC), in addition to pinpointing causal variants at known ALL loci. Further work is required to elucidate the biological mechanisms underlying these associations. Our results suggest that analysis of larger ALL datasets may yield additional genetic risk loci of moderate effect size. DisclosuresMa:Incyte Corp.: Consultancy.

244-OR: Toward Improved Identification of Adult-Onset Type 1 Diabetes (T1D): Clinical Characteristics Associated with T1D Polygenic Risk Scores in the Million Veteran Program (MVP)

Adult onset T1D is often misdiagnosed as T2D, leading to poor metabolic control and increased risk of microvascular complications and cardiovascular morbidity and mortality. We examined characteristics associated with a T1D genetic risk score in Veterans, a population where new diagnoses are generally presumed to be T2D, since persons with typical juvenile-onset T1D are excluded from military service. A 30 SNP T1D genetic risk score (GRS), previously validated with the Wellcome Trust Case Control Consortium (WTCCC), was applied to 111,663 Veterans with diabetes in the MVP. Patients were stratified by T1D GRS centile groups (≤5th, &amp;gt;5th-≤36th, &amp;gt;36th-≤50th, and &amp;gt;50th) which corresponded to T1D specificity in the WTCCC of 29%, 53%, 90%, and 95%, respectively. Typical T1D clinical characteristics were significantly more common (p&amp;lt;0.001) across increasing GRS centile groups: diabetic ketoacidosis 1.07%, 1.34%, 1.95%, 3.55%; hypoglycemia prompting an emergency department visit 3.95%, 3.84%, 4.45%, 5.45%; random plasma glucose &amp;lt;50 mg/dl at a primary care visit 8.05%, 7.99%, 9.01%, 12.43%; years to starting outpatient insulin Rx 4.40, 4.31, 3.97, 3.38; any use of a T1D ICD code 16.56%, 17.76%, 20.90%, 27.62%; low C-peptide if tested 2.91%, 9.75%, 18.96%, 32.55%; and GAD antibody (+) if tested 8.80%, 23.39%, 32.73%, 45.00%. Among the 5% tested, years to starting insulin fell significantly (p&amp;lt;0.001) in lowest to highest centile groups, from 2.59 to 1.28 yr for GAD(-), and from 1.77 vs. 0.90 yr for GAD(+). Conclusions: The 10% of US Veterans with diabetes in MVP in the two highest T1D GRS centile groups have characteristics suggestive of T1D, although only 5% were tested for GAD antibodies, and most had no use of T1D diagnosis codes. Since adult-onset T1D may be misdiagnosed as T2D in settings where T2D is more common, genetic analyses and/or clinical characteristics may warrant definitive testing and appropriate management. Disclosure P. K. Yang: None. M. K. Rhee: None. C. Mercado: None. R. A. Oram: Consultant; Self; Janssen Research &amp; Development, LLC. M. Vujkovic: None. P. Reaven: Research Support; Self; AstraZeneca, Dexcom, Inc. K. Cho: None. L. S. Phillips: Other Relationship; Self; Diasyst Inc., Research Support; Self; AbbVie Inc., Eli Lilly and Company, GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Kowa Pharmaceuticals America, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Pharma Ltd., Pfizer Inc., Sanofi-Aventis. A. Leong: None. S. Jackson: None. B. R. Charest: None. M. N. Weedon: None. Y. J. Cheng: None. Y. V. Sun: None. S. Raghavan: None. E. M. Litkowski: None. B. T. Legvold: None. Funding U.S. Department of Veterans Affairs (CSP2008, I01CX001899, I01CX001737); National Institutes of Health (R18DK066204, R21AI156161, U01DK091958, U01DK098246, UL1TR002378)

A Bayesian hierarchically structured prior for rare-variant association testing.

Although genome-wide association studies have been widely used to identify associations between complex diseases and genetic variants, standard single-variant analyses often have limited power when applied to rare variants. To overcome this problem, set-based methods have been developed with the aim of boosting power by borrowing strength from multiple rare variants. We propose the adaptive hierarchically structured variable selection (HSVS-A) before test for association of rare variants in a set with continuous or dichotomous phenotypes and to estimate the effect of individual rare variants simultaneously. HSVS-A has the flexibility to integrate a pairwise weighting scheme, which adaptively induces desirable correlations among variants of similar significance such that we can borrow information from potentially causal and noncausal rare variants to boost power. Simulation studies show that for both continuous and dichotomous phenotypes, HSVS-A is powerful when there are multiple causal rare variants, either in the same or opposite direction of effect, with the presence of a large number of noncausal variants. We also apply HSVS-A to the Wellcome Trust Case Control Consortium Crohn's disease data for testing the association of Crohn's disease with rare variants in pathways. HSVS-A identifies two pathways harboring novel protective rare variants for Crohn's disease.

A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.

Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been developed for epistasis detection. Multifactor dimensionality reduction (MDR) is the method commonly used in epistasis detection. It uses two class groups-high risk and low risk-in human genetic disease and complex genetic traits. However, it cannot handle uncertainties from genetic information. This chapter describes the fuzzy sigmoid membership-based MDR (FSMDR) method of epistasis detection. The algorithmic steps in FSMDR are also elaborated with simulated data generated from GAMETES and a real coronary artery disease patient epistasis data set obtained from the Wellcome Trust Case Control Consortium(WTCCC). Moreover, a belief degree-associated fuzzy MDR framework is also proposed for epistasis detection, which can overcome the uncertainties of MDR-based methods. This framework improves the detection efficiency. It works like fuzzy set-based MDR methods. Simulated epistasis data sets are used to compare different MDR-based methods. Belief degree-associated fuzzy MDR was shown togives good results by taking into account the uncertainly of the high/low risk classification.