Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Wednesday, May 31, 2006

Genome-Wide Genetic Analysis using Genetic Programming

Our paper on "Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis" by Moore and White has been peer-reviewed and accepted for presentation at the Parallel Problem Solving from Nature conference in Iceland in September. This paper will be published in the Lecture Notes in Computer Science series from Springer. Here is the abstract:

Moore JH and White BC. Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis. Lecture Notes in Computer Science, in press (2006)

Abstract:

Human genetics is undergoing an information explosion. The availability of chip-based technology facilitates the measurement of thousands of DNA sequence variation from across the human genome. The challenge is to sift through these high-dimensional datasets to identify combinations of interacting DNA sequence variations that are predictive of common diseases. We have previously developed and evaluated a genetic programming (GP) approach to attribute selection and classification in this domain. We showed that GP is no better than a simple random search when classification accuracy is used as the fitness function. We then showed that including pre-processed estimates of attribute quality (i.e. expert knowledge) using Tuned ReliefF (TuRF) in a multiobjective fitness function that also includes accuracy significantly improves the performance of GP over that of random search. The goal of this paper was to develop and evaluate a GP approach that uses expert knowledge such as TuRF scores during selection to ensure trees with good building blocks are being recombined and reproduced. We simulated genetic datasets of varying effect size (i.e. signal strength) in which the disease model consists of two interacting DNA sequence variations that exhibit no independent effects on class (i.e. epistasis). We show here that using expert knowledge to select trees performs as well as a multiobjective fitness function but requires only a tenth of the population size. This study demonstrates that GP may be a useful computational discovery tool in this domain. This study raises important questions about the general utility of GP for these types of problems, the importance of data pre-processing, and the importance of expert knowledge. We anticipate this study will provide an important baseline for future studies investigating the usefulness of GP as a general computational discovery tool for large-scale genetic studies.

This work is funded by NIH grants R01 LM009012 (PI-Moore) and R01 AI59694 (PI-Moore).

Monday, May 29, 2006

MDR Applications

Our multifactor dimensionality reduction (MDR) method has been successfully applied to a wide range of different diseases and clinical traits. Here is an almost complete list of papers that apply MDR to real data. I have sorted them by general disease categories. Please feel free to cite this list in your MDR papers and grants. Email me if you know of a paper that should be on the list. I am sure there are some out there I don't have listed.

This list was last updated by JHM on June 9, 2008.

Click here to carry out a PubMed search for MDR publications.

ASTHMA AND ALLERGY

Chan IH, Leung TF, Tang NL, Li CY, Sung YM, Wong GW, Wong CK, Lam CW. Gene-gene interactions for asthma and plasma total IgE concentration in Chinese children. J Allergy Clin Immunol. 2006 Jan;117(1):127-33.

Leung TF, Chan IH, Wong GW, Li CY, Tang NL, Yung E, Lam CW. Association between candidate genes and lung function growth in Chinese asthmatic children. Clin Exp Allergy. 2007 Oct;37(10):1480-6.

Li Y, Wu B, Xiong H, Zhu C, Zhang L. Polymorphisms of STAT-6, STAT-4 and IFN-gamma genes and the risk of asthma in Chinese population. Respir Med. 2007 Sep;101(9):1977-81.

Kim SH, Jeong HH, Cho BY, Kim M, Lee HY, Lee J, Wee K, Park HS. Association of Four-locus Gene Interaction with Aspirin-intolerant Asthma in Korean Asthmatics. J Clin Immunol. 2008 Apr 1, in press.

Millstein J, Conti DV, Gilliland FD, Gauderman WJ. A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet. 2006 Jan;78(1):15-27.

Park HW, Shin ES, Lee JE, Kwon HS, Chun E, Kim SS, Chang YS, Kim YK, Min KU, Kim YY, Cho SH. Multilocus analysis of atopy in Korean children using multifactor-dimensionality reduction. Thorax 2007 Mar;62(3):265-9.

AUTOIMMUNE DISEASES

Beretta L, Cappiello F, Moore JH, Scorza R. Interleukin-1 gene complex single nucleotide polymorphisms in systemic sclerosis: a further step ahead. Hum Immunol. 2008 Mar;69(3):187-92.

Berretta L, Cappiello JH, Moore JH, Greene CS, Barili M, Scorza R. Epistatic interactions of cytokine single nucleotide polymorphisms predict susceptibility to disease subsets in systemic sclerosis patients. Arthritis Care and Research, 2008, in press.

Chanda P, Zhang A, Brazeau D, Sucheston L, Freudenheim JL, Ambrosone C, Ramanathan M. Information-theoretic metrics for visualizing gene-environment interactions. Am J Hum Genet. 2007 Nov;81(5):939-63. (Crohn's Disease)

Gong R, Liu Z, Li L. Epistatic effect of plasminogen activator inhibitor 1 and beta-fibrinogen genes on risk of glomerular microthrombosis in lupus nephritis: interaction with environmental/clinical factors. Arthritis Rheum. 2007 May;56(5):1608-17.

Julia A, Moore J, Miquel L, Alegre C, Barcelo P, Ritchie M, Marsal S. Identification of a two-loci epistatic interaction associated with susceptibility to rheumatoid arthritis through reverse engineering and multifactor dimensionality reduction. Genomics. 2007 Jul;90(1):6-13.

Mei L, Li X, Yang K, Cui J, Fang B, Guo X, Rotter JI. Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15. BMC Proc. 2007;1 Suppl 1:S17. Epub 2007 Dec 18, in press.

Okazaki T, Wang MH, Rawsthorne P, Sargent M, Datta LW, Shugart YY, Bernstein CN, Brant SR. Contributions of IBD5, IL23R, ATG16L1, and NOD2 to Crohn's disease risk in a population-based case-control study: Evidence of gene-gene interactions.Inflamm Bowel Dis. 2008 Jun 2, in press.

Ritchie MD, Bartlett J, Bush WS, Edwards TL, Motsinger AA, Torstenson ES. Exploring epistasis in candidate genes for rheumatoid arthritis. BMC Proc. 2007;1 Suppl 1:S70. Epub 2007 Dec 18.

CANCER

Andrew AS, Nelson HH, Kelsey KT, Moore JH, Meng AC, Casella DP, Tosteson TD, Schned AR, Karagas MR. Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. Carcinogenesis. 2006 May;27(5):1030-1037.

Andrew AS, Karagas MR, Nelson HH, Guarrera S, Polidoro S, Gamberini S, Sacerdote C, Moore JH, Kelsey KT, Demidenko E, Vineis P, Matullo G. DNA repair polymorphisms modify bladder cancer risk: a multi-factor analytic strategy. Hum Hered. 2008;65(2):105-18.

Briollais L, Wang Y, Rajendram I, Onay V, Shi E, Knight J, Ozcelik H. Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario. BMC Med. 2007 Aug 7;5:22.

Cao G, Lu H, Feng J, Shu J, Zheng D, Hou Y. Lung cancer risk associated with Thr495Pro polymorphism of GHR in Chinese population. Jpn J Clin Oncol. 2008 Apr;38(4):308-16.

Chen M, Kamat AM, Huang M, Grossman HB, Dinney CP, Lerner S, Wu X, Gu J. High-order interactions among genetic polymorphisms in nucleotide excision repair pathway genes and smoking in modulating bladder cancer risk. Carcinogenesis. 2007 Oct;28(10):2160-5.

Duell EJ, Moore JH, Bracci PM, Burk RD, Kelsey KT, Holly EA. Detecting complex, pathway-based gene-gene and gene-environment interactions in pancreatic cancer. Carcinogenesis, in press (2008).

Hu Z, Wang H, Shao M, Jin G, Sun W, Wang Y, Liu H, Wang Y, Ma H, Qian J, Jin L, Wei Q, Lu D, Huang W, Shen H. Genetic variants in MGMT and risk of lung cancer in Southeastern Chinese: a haplotype-based analysis. Hum Mutat. 2007 May;28(5):431-40. .

Huang M, Dinney CP, Lin X, Lin J, Grossman HB, Wu X. High-Order Interactions among Genetic Variants in DNA Base Excision Repair Pathway Genes and Smoking in Bladder Cancer Susceptibility. Cancer Epidemiol Biomarkers Prev. 2007 Jan;16(1):84-91.

Justenhoven C, Hamann U, Schubert F, Zapatka M, Pierl CB, Rabstein S, Selinski S, Mueller T, Ickstadt K, Gilbert M, Ko YD, Baisch C, Pesch B, Harth V, Bolt HM, Vollmert C, Illig T, Eils R, Dippon J, Brauch H. Breast cancer: a candidate gene approach across the estrogen metabolic pathway. Breast Cancer Res Treat. 2008 Mar;108(1):137-49.

Liu H, Jin G, Wang H, Wu W, Liu Y, Qian J, Fan W, Ma H, Miao R, Hu Z, Sun W, Wang Y, Jin L, Wei Q, Shen H, Huang W, Lu D. Association of polymorphisms in one-carbon metabolizing genes and lung cancer risk: a case-control study in Chinese population. Lung Cancer. 2008 Jan 21.

Liu Y, Zhang H, Zhou K, Chen L, Xu Z, Zhong Y, Liu H, Li R, Shugart YY, Wei Q, Jin L, Huang F, Lu D, Zhou L. Tagging SNPs in Nonhomologous End-Joining Pathway Genes and Risk of Glioma. Carcinogenesis. 2007 Mar 26; [Epub ahead of print]

Liu Y, Zhou K, Zhang H, Shugart YY, Chen L, Xu Z, Zhong Y, Liu H, Jin L, Wei Q, Huang F, Lu D, Zhou L. Polymorphisms of LIG4 and XRCC4 involved in the NHEJ pathway interact to modify risk of glioma. Hum Mutat. 2008 Mar;29(3):381-9.

Manuguerra M, Matullo G, Veglia F, Autrup H, Dunning AM, Garte S, Gormally E, Malaveille C, Guarrera S, Polidoro S, Saletta F, Peluso M, Airoldi L, Overvad K, Raaschou-Nielsen O, Clavel-Chapelon F, Linseisen J, Boeing H, Trichopoulos D, Kalandidi A, Palli D, Krogh V, Tumino R, Panico S, Bueno-De-Mesquita HB, Peeters PH, Lund E, Pera G, Martinez C, Amiano P, Barricarte A, Tormo MJ, Quiros JR, Berglund G, Janzon L, Jarvholm B, Day NE, Allen NE, Saracci R, Kaaks R, Ferrari P, Riboli E, Vineis P. Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions. Carcinogenesis. 2007 Feb;28(2):414-22.

Milne RL, Fagerholm R, Nevanlinna H, Benítez J. The importance of replication in gene-gene interaction studies: Multifactor Dimensionality Reduction applied to a two-stage breast cancer case-control study. Carcinogenesis. 2008 May 14, in press.

Oestergaard MZ, Tyrer J, Cebrian A, Shah M, Dunning AM, Ponder BA, Easton DF, Pharoah PD. Interactions between genes involved in the antioxidant defence system and breast cancer risk.Br J Cancer. 2006 Aug 21;95(4):525-31.

Nordgard SH, Ritchie MD, Jensrud SD, Motsinger AA, Alnaes GI, Lemmon G, Berg M, Geisler S, Moore JH, Lonning PE, Borresen-Dale AL, Kristensen VN. ABCB1 and GST polymorphisms associated with TP53 status in breast cancer. Pharmacogenet Genomics. 2007 Feb;17(2):127-136.

Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001 Jul;69(1):138-47.

Vaclavicek A, Bermejo JL, Wappenschmidt B, Meindl A, Sutter C, Schmutzler RK, Kiechle M, Bugert P, Burwinkel B, Bartram CR, Hemminki K, Forsti A. Genetic variation in the major mitotic checkpoint genes does not affect familial breast cancer risk. Breast Cancer Res Treat. in press, 2007.

Vaarala MH, Mattila H, Ohtonen P, Tammela TL, Paavonen TK, Schleutker J. The interaction of CYP3A5 polymorphisms along the androgen metabolism pathway in prostate cancer. Int J Cancer. 2008 Jun 1;122(11):2511-6.

Wang Y, Spitz MR, Lee JJ, Huang M, Lippman SM, Wu X. Nucleotide excision repair pathway genes and oral premalignant lesions. Clin Cancer Res. 2007 Jun 15;13(12):3753-8.

Xu J, Lowey J, Wiklund F, Sun J, Lindmark F, Hsu FC, Dimitrov L, Chang B, Turner AR, Liu W, Adami HO, Suh E, Moore JH, Zheng SL, Isaacs WB, Trent JM, Gronberg H. The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk. Cancer Epidemiol Biomarkers Prev. 2005 Nov;14(11 Pt 1):2563-8.

CARDIOVASCULAR DISEASES

Agirbasli D, Agirbasli M, Williams SM, Phillips JA 3rd. Interaction among 5,10 methylenetetrahydrofolate reductase, plasminogen activator inhibitor and endothelial nitric oxide synthase gene polymorphisms predicts the severity of coronary artery disease in Turkish patients.Coron Artery Dis. 2006 Aug;17(5):413-7.

Akagawa H, Narita A, Yamada H, Tajima A, Krischek B, Kasuya H, Hori T, Kubota M, Saeki N, Hata A, Mizutani T, Inoue I. Systematic screening of lysyl oxidase-like (LOXL) family genes demonstrates that LOXL2 is a susceptibility gene to intracranial aneurysms. Hum Genet 2007; 121:377-87.

Asselbergs FW, Moore JH, van den Berg MP, Rimm EB, de Boer RA, Dullaart RP, Navis G, van Gilst WH. A role for CETP TaqIB polymorphism in determining susceptibility to atrial fibrillation: a nested case control study. BMC Med Genet. 2006 Apr 19;7:39.

Bastone L, Reilly M, Rader DJ, Foulkes AS. MDR and PRP: a comparison of methods for high-order genotype-phenotype associations.Hum Hered. 2004;58(2):82-92.

Canter JA, Summar ML, Smith HB, Rice GD, Hall LD, Ritchie MD, Motsinger AA, Christian KG, Drinkwater DC Jr, Scholl FG, Dyer KL, Kavanaugh-McHugh AL, Barr FE. Genetic variation in the mitochondrial enzyme carbamyl-phosphate synthetase I predisposes children to increased pulmonary artery pressure following surgical repair of congenital heart defects: A validated genetic association study. Mitochondrion, 2007, in press.

Coffey CS, Hebert PR, Ritchie MD, Krumholz HM, Gaziano JM, Ridker PM, Brown NJ, Vaughan DE, Moore JH. An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation. BMC Bioinformatics. 2004 Apr 30;5:49

Heidema AG, Feskens EJ, Doevendans PA, Ruven HJ, van Houwelingen HC, Mariman EC, Boer JM. Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs. Genet Epidemiol. 2007 Dec;31(8):910-21.

Kohara K, Tabara Y, Nakura J, Imai Y, Ohkubo T, Hata A, Soma M, Nakayama T, Umemura S, Hirawa N, Ueshima H, Kita Y, Ogihara T, Katsuya T, Takahashi N, Tokunaga K, Miki T. Identification of hypertension-susceptibility genes and pathways by a systemic multiple candidate gene approach: the millennium genome project for hypertension. Hypertens Res. 2008 Feb;31(2):203-12.

Mannila MN, Eriksson P, Ericsson CG, Hamsten A, Silveira A. Epistatic and pleiotropic effects of polymorphisms in the fibrinogen and coagulation factor XIII genes on plasma fibrinogen concentration, fibrin gel structure and risk of myocardial infarction.Thromb Haemost. 2006 Mar;95(3):420-7.

Mannila MN, Lovely RS, Kazmierczak SC, Eriksson P, Samnegard A, Farrell DH, Hamsten A, Silveira A. Elevated plasma fibrinogen gamma' concentration is associated with myocardial infarction: effects of variation in fibrinogen genes and environmental factors. J Thromb Haemost. in press, 2007.

Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney N, White BC. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol. 2006 Jul 21;241(2):252-61.

Moore JH, Williams SM. New strategies for identifying gene-gene interactions in hypertension. Ann Med. 2002;34(2):88-95.

Motsinger AA, Donahue BS, Brown NJ, Roden DM, Ritchie MD. Risk Factor Interactions and Genetic Effects Associated with Post Operative Atrial Fibrillation. Pacific Symposium on Biocomputing 2006;11:584-595.

Niu W, Qi Y, Cen W, Cui C, Zhuoma C, Cai D, Zhou W, Qiu C. Genetic polymorphisms of angiotensinogen and essential hypertension in a Tibetan population. Hypertens Res. 2007 Nov;30(11):1129-37.

Qi Y, Niu W, Zhu T, Zhou W, Qiu C. Synergistic effect of the genetic polymorphisms of the renin-angiotensin-aldosterone system on high-altitude pulmonary edema: a study from Qinghai-Tibet altitude. Eur J Epidemiol. 2008;23(2):143-52.

Saeed M, Perwaiz Iqbal M, Yousuf F, Perveen S, Shafiq M, Sajid J, Frossard P. Interactions and associations of paraoxonase gene cluster polymorphisms with myocardial infarction in a Pakistani population. Clin Genet. 2007 Mar;71(3):238-44.

Sanada H, Yatabe J, Midorikawa S, Hashimoto S, Watanabe T, Moore JH, Ritchie MD, Williams SM, Pezzullo JC, Sasaki M, Eisner GM, Jose PA, Felder RA. Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension. Clin Chem. 2006 Mar;52(3):352-60.

Shen CD, Zhang WL, Sun K, Wang YB, Zhen YS, Hui RT. Interaction of genetic risk factors confers higher risk for thrombotic stroke in male Chinese: a multicenter case-control study. Ann Hum Genet. 2007 Sep;71(Pt 5):620-9.

Tsai CT, Lai LP, Lin JL, Chiang FT, Hwang JJ, Ritchie MD, Moore JH, Hsu KL, Tseng CD, Liau CS, Tseng YZ. Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation. 2004 Apr 6;109(13):1640-6.

Tsai CT, Hwang JJ, Ritchie MD, Moore JH, Chiang FT, Lai LP, Hsu KL, Tseng CD, Lin JL, Tseng YZ. Renin-angiotensin system gene polymorphisms and coronary artery disease in a large angiographic cohort: Detection of high order gene-gene interaction. Atherosclerosis 2007, in press.

Williams SM, Ritchie MD, Phillips JA 3rd, Dawson E, Prince M, Dzhura E, Willis A, Semenya A, Summar M, White BC, Addy JH, Kpodonu J, Wong LJ, Felder RA, Jose PA, Moore JH. Multilocus analysis of hypertension: a hierarchical approach. Hum Hered. 2004;57(1):28-38.

Zhao Q, Wang L, Yang W, Chen S, Huang J, Fan Z, Li H, Lu X, Gu D. Interactions among genetic variants from contractile pathway of vascular smooth muscle cell in essential hypertension susceptibility of Chinese Han population. Pharmacogenet Genomics. 2008 Jun;18(6):459-466.

CHRONIC FATIGUE SYNDROME

Chung Y, Lee SY, Elston RC, Park T. 2007. Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics 23(1):71-6.

DIABETES, OBESITY AND METABOLIC SYNDROME

Cho YM, Ritchie MD, Moore JH, Park JY, Lee KU, Shin HD, Lee HK, Park KS. Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus. Diabetologia. 2004 Mar;47(3):549-54.

Fiorito M, Torrente I, De Cosmo S, Guida V, Colosimo A, Prudente S, Flex E, Menghini R, Miccoli R, Penno G, Pellegrini F, Tassi V, Federici M, Trischitta V, Dallapiccola B. Interaction of DIO2 T92A and PPARgamma2 P12A polymorphisms in the modulation of metabolic syndrome. Obesity. 2007 Dec;15(12):2889-95.

Hsieh CH, Liang KH, Hung YJ, Huang LC, Pei D, Liao YT, Kuo SW, Bey MS, Chen JL, Chen EY. Analysis of epistasis for diabetic nephropathy among type 2 diabetic patients.Hum Mol Genet. 2006 Sep 15;15(18):2701-8.

Qi L, van Dam RM, Asselbergs FW, Hu FB. Gene-gene interactions between HNF4A and KCNJ11 in predicting Type 2 diabetes in women. Diabet Med. 2007 Nov;24(11):1187-91.

MENDELIAN DISEASES

Garcia-Barcelo MM, Miao X, Lui VC, So MT, Ngan ES, Leon TY, Lau DK, Liu TT, Lao X, Guo W, Holden WT, Moore J, Tam PK. Correlation Between Genetic Variations in Hox Clusters and Hirschsprung's Disease. Ann Hum Genet. in press, 2007.

Soares ML, Coelho T, Sousa A, Batalov S, Conceicao I, Sales-Luis ML, Ritchie MD, Williams SM, Nievergelt CM, Schork NJ, Saraiva MJ, Buxbaum JN. Susceptibility and modifier genes in Portuguese transthyretin V30M amyloid polyneuropathy: complexity in a single-gene disease. Hum Mol Genet. 2005 Feb 15;14(4):543-53.

NEUROPSYCHIATRIC DISEASES

Ashley-Koch AE, Mei H, Jaworski J, Ma DQ, Ritchie MD, Menold MM, Delong GR, Abramson RK, Wright HH, Hussman JP, Cuccaro ML, Gilbert JR, Martin ER, Pericak-Vance MA. An analysis paradigm for investigating multi-locus effects in complex disease: examination of three GABA receptor subunit genes on 15q11-q13 as risk factors for autistic disorder. Ann Hum Genet. 2006 May;70(Pt 3):281-92.

Ashley-Koch AE, Jaworski J, Ma de Q, Mei H, Ritchie MD, Skaar DA, Robert Delong G, Worley G, Abramson RK, Wright HH, Cuccaro ML, Gilbert JR, Martin ER, Pericak-Vance MA. Investigation of potential gene-gene interactions between APOE and RELN contributing to autism risk. Psychiatr Genet. 2007 Aug;17(4):221-6.

Brassat D, Motsinger AA, Caillier SJ, Erlich HA, Walker K, Steiner LL, Cree BA, Barcellos LF, Pericak-Vance MA, Schmidt S, Gregory S, Hauser SL, Haines JL, Oksenberg JR, Ritchie MD. Multifactor dimensionality reduction reveals gene-gene interactions associated with multiple sclerosis susceptibility in African Americans. Genes Immun. 2006 Jun;7(4):310-5.

Coutinho AM, Sousa I, Martins M, Correia C, Morgadinho T, Bento C, Marques C, Ataide A, Miguel TS, Moore JH, Oliveira G, Vicente AM. Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels. Hum Genet Apr;121(2):243-56.

Kang SG, Lee HJ, Choi JE, Park YM, Park JH, Han C, Kim YK, Kim SH, Lee MS, Joe SH, Jung IK, Kim L. Association Study between Antipsychotics- Induced Restless Legs Syndrome and Polymorphisms of Dopamine D1, D2, D3, and D4 Receptor Genes in Schizophrenia. Neuropsychobiology. 2008 May 2;57(1-2):49-54.

Lee SY, Chung Y, Elston RC, Kim Y, Park T. Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions. Bioinformatics. 2007 Oct 1;23(19):2589-95. (Alzheimer Disease)

Li MD, Lou XY, Chen G, Ma JZ, Elston RC. Gene-Gene Interactions Among CHRNA4, CHRNB2, BDNF, and NTRK2 in Nicotine Dependence. Biol Psychiatry. 2008, in press.

Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, Li MD. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet. 2007 Jun;80(6):1125-37.

Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H, Ritchie MD, Delong GR, Abramson RK, Wright HH, Cuccaro ML, Hussman JP, Gilbert JR, Pericak-Vance MA. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet. 2005 Sep;77(3):377-88

Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH. A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet Epidemiol. 2006 Feb;30(2):111-23.

Mei H, Cuccaro ML, Martin ER. Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables. Am J Hum Genet. 2007 Dec;81(6):1251-61.

Motsinger AA, Brassat D, Caillier SJ, Erlich HA, Walker K, Steiner LL, Barcellos LF, Pericak-Vance MA, Schmidt S, Gregory S, Hauser SL, Haines JL, Oksenberg JR, Ritchie MD. Complex gene-gene interactions in multiple sclerosis: a multifactorial approach reveals associations with inflammatory genes.Neurogenetics. 2007 Jan;8(1):11-20.

Qin S, Zhao X, Pan Y, Liu J, Feng G, Fu J, Bao J, Zhang Z, He L. An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray. Eur J Hum Genet. 2005 Jul;13(7):807-14.

Thornton-Wells TA, Moore JH, Martin ER, Pericak-Vance MA, Haines JL. Confronting complexity in late-onset Alzheimer disease: application of two-stage analysis approach addressing heterogeneity and epistasis. Genet Epidemiol. 2008 Apr;32(3):187-203.

Vilella E, Costas J, Sanjuan J, Guitart M, De Diego Y, Carracedo A, Martorell L, Valero J, Labad A, De Frutos R, Najera C, Molto MD, Toirac I, Guillamat R, Brunet A, Valles V, Perez L, Leon M, de Fonseca FR, Phillips C, Torres M. Association of schizophrenia with DTNBP1 but not with DAO, DAOA, NRG1 and RGS4 nor their genetic interaction. J Psychiatr Res. 2008 Mar;42(4):278-88.

Yasuno K, Ando S, Misumi S, Makino S, Kulski JK, Muratake T, Kaneko N, Amagane H, Someya T, Inoko H, Suga H, Kanemoto K, Tamiya G. Synergistic association of mitochondrial uncoupling protein (UCP) genes with schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2007 Mar 5;144B(2):250-3.

Zhao X, Qin S, Shi Y, Zhang A, Zhang J, Bian L, Wan C, Feng G, Gu N, Zhang G, He G, He L. Systematic study of association of four GABAergic genes: glutamic acid decarboxylase 1 gene, glutamic acid decarboxylase 2 gene, GABA(B) receptor 1 gene and GABA(A) receptor subunit beta2 gene, with schizophrenia using a universal DNA microarray.Schizophr Res. 2007 Jul;93(1-3):374-84.

OBSTETRICS AND GYNECOLOGY

Menon R, Velez DR, Simhan H, Ryckman K, Jiang L, Thorsen P, Vogel I, Jacobsson B, Merialdi M, Williams SM, Fortunato SJ. Multilocus interactions at maternal tumor necrosis factor-alpha, tumor necrosis factor receptors, interleukin-6 and interleukin-6 receptor genes predict spontaneous preterm labor in European-American women. Am J Obstet Gynecol. 2006 Jun;194(6):1616-24.

Suh YJ, Kim YJ, Park H, Park EA, Ha EH. Oxidative stress-related gene interactions with preterm delivery in Korean women.Am J Obstet Gynecol. 2008 May;198(5):541.e1-7.

OPHTHAMOLOGIC DISEASES

Jakobsdottir J, Conley YP, Weeks DE, Ferrell RE, Gorin MB. C2 and CFB genes in age-related maculopathy and joint action with CFH and LOC387715 genes. PLoS ONE. 2008 May 21;3(5):e2199.

OSTEOPOROSIS

Xiong DH, Shen H, Zhao LJ, Xiao P, Yang TL, Guo Y, Wang W, Guo YF, Liu YJ, Recker RR, Deng HW. Robust and comprehensive analysis of 20 osteoporosis candidate genes by very high-density single-nucleotide polymorphism screen among 405 white nuclear families identified significant association and gene-gene interaction. J Bone Miner Res 2006 21(11):1678-95.

PHARMACOGENETICS

Chung HH, Kim MK, Kim JW, Park NH, Song YS, Kang SB, Lee HP. XRCC1 R399Q polymorphism is associated with response to platinum-based neoadjuvant chemotherapy in bulky cervical cancer. Gynecol Oncol. 2006 Jul 26.

Haas DW, Geraghty DE, Andersen J, Mar J, Motsinger AA, D'Aquila RT, Unutmaz D, Benson CA, Ritchie MD, Landay A. Immunogenetics of CD4 Lymphocyte Count Recovery during Antiretroviral Therapy: An AIDS Clinical Trials Group Study. J Infect Dis. 2006 Oct 15;194(8):1098-107.

Motsinger AA, Ritchie MD, Shafer RW, Robbins GK, Morse GD, Labbe L, Wilkinson GR, Clifford DB, D'aquila RT, Johnson VA, Pollard RB, Merigan TC, Hirsch MS, Donahue JP, Kim RB, Haas DW. Multilocus genetic interactions and response to efavirenz-containing regimens: an Adult AIDS Clinical Trials Group study.Pharmacogenet Genomics. 2006 Nov;16(11):837-845.

Ritchie MD, Motsinger AA. Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies. Pharmacogenomics. 2005 Dec;6(8):823-34.

Ritchie MD, Haas DW, Motsinger AA, Donahue JP, Erdem H, Raffanti S, Rebeiro P, George AL, Kim RB, Haines JL, Sterling TR. Drug transporter and metabolizing enzyme gene variants and nonnucleoside reverse-transcriptase inhibitor hepatotoxicity. Clin Infect Dis. 2006 43(6):779-82.

Sabbagh A, Darlu P. SNP selection at the NAT2 locus for an accurate prediction of the acetylation phenotype. Genet Med. 2006 Feb;8(2):76-85.

Wilke RA, Reif DM, Moore JH. Combinatorial pharmacogenetics.Nat Rev Drug Discov. 2005 Nov;4(11):911-8.

Wilke RA, Moore JH, Burmester JK. Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage. Pharmacogenet Genomics. 2005 Jun;15(6):415-21.

Sunday, May 28, 2006

Machine Learning Methods

There are a number of different machine learning methods that have been applied to detecting gene-gene interactions. We review a few of these in a new paper that was just published in Applied Bioinformatics.

McKinney BA, Reif DM, Ritchie MD, Moore JH. Machine Learning for Detecting Gene-Gene Interactions : A Review. Applied Bioinformatics. 2006;5(2):77-88.

Abstract:

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Sunday, May 14, 2006

Genetic Programming cont.

I just returned from the fourth workshop on Genetic Programming Theory and Practice (GPTP 2006) at the University of Michigan in Ann Arbor. It is clear from this small gathering of theorists and practitioners that GP is maturing as a general computational discovery tool. The real test of any method is whether people use it solve difficult problems. I was impressed by the fact that people from several large investment firms and several industrial giants are successfully applying GP to their real-world problems. More importantly, they are discovering that GP outperforms many traditional data mining and machine learning methods such as linear regression and support vector machines. The key development over the last few years has been the 'evolution' of GP from a simple algorithm that was applied to toy problems in computer science to a more complex multi-layered algorithm that is on the front line of solving tough problems in business, engineering, and biology.

See my post from March 14th for information about the GP paper I presented at the workshop.

See www.genetic-programming.org and the GP entry on Wikipedia for more information on the method and its applications.

Here are some good books on GP to start with:

Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D. (1998), Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann

Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press

Koza, J.R. (1994), Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press

Koza, J.R., Bennett, F.H., Andre, D., and Keane, M.A. (1999), Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann

Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G. (2003), Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer Academic Publishers

Langdon, W. B., Poli, R. (2002), Foundations of Genetic Programming, Springer-Verlag

Here are the previous books from the GPTP workshop. The book from GPTP 2006 will be published by Springer later this year or early next year.

Riolo and Worzel. 2003. Genetic Programming Theory and Practice. Boston: Kluwer Academic Publishers.

O'Reilly et al. 2004. Genetic Programming Theory and Practice II. Boston: Kluwer Academic Publishers.

Yu et al. 2005. Genetic Programming Theory and Practice III. Springer.