Browsing by Author "Koller, Daniel"

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    Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics
    (SpringerOpen, 2018-11-11) Breuer, René; Mattheisen, Manuel; Frank, Josef; Krumm, Bertram; Treutlein, Jens; Kassem, Layla; Strohmaier, Jana; Herms, Stefan; Mühleisen, Thomas W.; Degenhardt, Franziska; Cichon, Sven; Nöthen, Markus M.; Karypis, George; Kelsoe, John; Greenwood, Tiffany; Nievergelt, Caroline; Shilling, Paul; Shekhtman, Tatyana; Edenberg, Howard; Craig, David; Szelinger, Szabolcs; Nurnberger, John; Gershon, Elliot; Alliey‑Rodriguez, Ney; Zandi, Peter; Goes, Fernando; Schork, Nicholas; Smith, Erin; Koller, Daniel; Zhang, Peng; Badner, Judith; Berrettini, Wade; Bloss, Cinnamon; Byerley, William; Coryell, William; Foroud, Tatiana; Guo, Yirin; Hipolito, Maria; Keating, Brendan; Lawson, William; Liu, Chunyu; Mahon, Pamela; McInnis, Melvin; Murray, Sarah; Nwulia, Evaristus; Potash, James; Rice, John; Scheftner, William; Zöllner, Sebastian; McMahon, Francis J.; Rietschel, Marcella; Schulze, Thomas G.; Biochemistry and Molecular Biology, School of Medicine
    BACKGROUND: Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. RESULTS: Two of these rules-one associated with eating disorder and the other with anxiety-remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. CONCLUSION: Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
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    Family-based association analysis of alcohol dependence criteria and severity
    (Wiley Blackwell (Blackwell Publishing), 2014-02) Wetherill, Leah; Kapoor, Manav; Agrawal, Arpana; Bucholz, Kathleen; Koller, Daniel; Bertelsen, Sarah E.; Le, Nhung; Wang, Jen-Chyong; Almasy, Laura; Hesselbrock, Victor; Kramer, John; Nurnberger, John I.; Schuckit, Marc; Tischfield, Jay A.; Xuei, Xiaoling; Porjesz, Bernice; Edenberg, Howard J.; Goate, Alison M.; Foroud, Tatiana; Department of Medical and Molecular Genetics, IU School of Medicine
    Background Despite the high heritability of alcohol dependence (AD), the genes found to be associated with it account for only a small proportion of its total variability. The goal of this study was to identify and analyze phenotypes based on homogeneous classes of individuals to increase the power to detect genetic risk factors contributing to the risk of AD. Methods The 7 individual DSM-IV criteria for AD were analyzed using latent class analysis (LCA) to identify classes defined by the pattern of endorsement of the criteria. A genome-wide association study was performed in 118 extended European American families (n = 2,322 individuals) densely affected with AD to identify genes associated with AD, with each of the seven DSM-IV criteria, and with the probability of belonging to two of three latent classes. Results Heritability for DSM-IV AD was 61%, and ranged from 17-60% for the other phenotypes. A SNP in the olfactory receptor OR51L1 was significantly associated (7.3 × 10−8) with the DSM-IV criterion of persistent desire to, or inability to, cut down on drinking. LCA revealed a three-class model: the “low risk” class (50%) rarely endorsed any criteria, and none met criteria for AD; the “moderate risk” class (33) endorsed primarily 4 DSM-IV criteria, and 48% met criteria for AD; the “high risk” class (17%) manifested high endorsement probabilities for most criteria and nearly all (99%) met criteria for AD One single nucleotide polymorphism (SNP) in a sodium leak channel NALCN demonstrated genome-wide significance with the high risk class (p=4.1 × 10−8). Analyses in an independent sample did not replicate these associations. Conclusion We explored the genetic contribution to several phenotypes derived from the DSM-IV alcohol dependence criteria. The strongest evidence of association was with SNPs in NALCN and OR51L1.
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    Genome-wide association study of intracranial aneurysm identifies a new association on chromosome 7
    (Ovid Technologies Wolters Kluwer – American Heart Association, 2014-11) Foroud, Tatiana; Lai, Dongbing; Koller, Daniel; van’t Hof, Femke; Kurki, Mitja I.; Anderson, Craig S.; Brown, Robert D.; Connolly, E. Sander; Eriksson, Johan G.; Flaherty, Matthew; Fornage, Myriam; von und zuFraunberg, Mikael; Gaál, Emília I.; Laakso, Aki; Hernesniemi, Juha; Huston, John; Jääskeläinen, Juha E.; Kiemeney, Lambertus A.; Kivisaari, Riku; Kleindorfer, Dawn; Ko, Nerissa; Lehto, Hanna; Mackey, Jason; Meissner, Irene; Moomaw, Charles J.; Mosley, Thomas H.; Moskala, Marek; Niemelä, Mika; Palotie, Aarno; Pera, Joanna; Rinkel, Gabriel; Ripke, Stephan; Rouleau, Guy; Ruigrok, Ynte; Sauerbeck, Laura; Słowik, Agnieszka; Vermeulen, Sita H.; Woo, Daniel; Worrall, Bradford B.; Broderick, Joseph; Department of Medical & Molecular Genetics, IU School of Medicine
    BACKGROUND AND PURPOSE: Common variants have been identified using genome-wide association studies which contribute to intracranial aneurysms (IA) susceptibility. However, it is clear that the variants identified to date do not account for the estimated genetic contribution to disease risk. METHODS: Initial analysis was performed in a discovery sample of 2617 IA cases and 2548 controls of white ancestry. Novel chromosomal regions meeting genome-wide significance were further tested for association in 2 independent replication samples: Dutch (717 cases; 3004 controls) and Finnish (799 cases; 2317 controls). A meta-analysis was performed to combine the results from the 3 studies for key chromosomal regions of interest. RESULTS: Genome-wide evidence of association was detected in the discovery sample on chromosome 9 (CDKN2BAS; rs10733376: P<1.0×10(-11)), in a gene previously associated with IA. A novel region on chromosome 7, near HDAC9, was associated with IA (rs10230207; P=4.14×10(-8)). This association replicated in the Dutch sample (P=0.01) but failed to show association in the Finnish sample (P=0.25). Meta-analysis results of the 3 cohorts reached statistical significant (P=9.91×10(-10)). CONCLUSIONS: We detected a novel region associated with IA susceptibility that was replicated in an independent Dutch sample. This region on chromosome 7 has been previously associated with ischemic stroke and the large vessel stroke occlusive subtype (including HDAC9), suggesting a possible genetic link between this stroke subtype and IA.
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    A multivariate finite mixture latent trajectory model with application to dementia studies
    (Taylor & Francis, 2016) Lai, Dongbing; Xu, Huiping; Katz, Barry; Koller, Daniel; Foroud, Tatiana; Gao, Sujuan; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.