Ashley EA: Clinical assessment incorporating a personal genome. Lancet. 2010, 375 (9725): 1525-1535. 10.1016/S0140-6736(10)60452-7.
CAS
PubMed
PubMed Central
Google Scholar
Ripatti S: A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010, 376 (9750): 1393-1400. 10.1016/S0140-6736(10)61267-6.
PubMed
PubMed Central
Google Scholar
Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007, 447 (7145): 661-678. 10.1038/nature05911.
Google Scholar
Donnelly P: Progress and challenges in genome-wide association studies in humans. Nature. 2008, 456 (7223): 728-731. 10.1038/nature07631.
CAS
PubMed
Google Scholar
Manolio TA: Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010, 363 (2): 166-176. 10.1056/NEJMra0905980.
CAS
PubMed
Google Scholar
Lander ES: Initial impact of the sequencing of the human genome. Nature. 2011, 470 (7333): 187-197. 10.1038/nature09792.
CAS
PubMed
Google Scholar
Maher B: Personal genomes: The case of the missing heritability. Nature. 2008, 456 (7218): 18-21. 10.1038/456018a.
CAS
PubMed
Google Scholar
Gibson G: Hints of hidden heritability in GWAS. Nat Genetics. 2010, 42 (7): 558-560. 10.1038/ng0710-558.
CAS
PubMed
Google Scholar
Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH: Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genetics. 2010, 11 (6): 446-450. 10.1038/nrg2809.
CAS
PubMed
Google Scholar
Zuk O, Hechter E, Sunyaev SR, Lander ES: The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012, 109 (4): 1193-1198. 10.1073/pnas.1119675109.
CAS
PubMed
PubMed Central
Google Scholar
Lehner B: Modelling genotype-phenotype relationships and human disease with genetic interaction networks. J Exp Biol. 2007, 210 (Pt 9): 1559-1566.
PubMed
Google Scholar
Moore JH, Williams SM: Epistasis and its implications for personal genetics. Am J Hum Genet. 2009, 85 (3): 309-320. 10.1016/j.ajhg.2009.08.006.
CAS
PubMed
PubMed Central
Google Scholar
Cordell HJ: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009, 10 (6): 392-404.
CAS
PubMed
PubMed Central
Google Scholar
Lehner B: Molecular mechanisms of epistasis within and between genes. Trends Genet. 2011, 27 (8): 323-331. 10.1016/j.tig.2011.05.007.
CAS
PubMed
Google Scholar
Moore JH, Asselbergs FW, Williams SM: Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010, 26 (4): 445-455. 10.1093/bioinformatics/btp713.
CAS
PubMed
PubMed Central
Google Scholar
Califano A, Butte AJ, Friend S, Ideker T, Schadt E: Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat Genet. 2012, 44 (8): 841-847. 10.1038/ng.2355.
CAS
PubMed
PubMed Central
Google Scholar
Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE: Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers. PLoS Genet. 2009, 5 (2): e1000337. 10.1371/journal.pgen.1000337.
PubMed
PubMed Central
Google Scholar
Wei Z, Wang K, Qu H-Q, Zhang H, Bradfield J: From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes. PLoS Genet. 2009, 5 (10): e1000678. 10.1371/journal.pgen.1000678.
PubMed
PubMed Central
Google Scholar
1000 Genomes Project: A map of genome variation from population-scale sequencing. Nature. 2010, 467 (7319): 1061-1073. 10.1038/nature09534.
Google Scholar
Kruppa J, Ziegler A, König IR: Risk estimation and risk prediction using machine-learning methods. Hum Genet. 2012, 131 (10): 1639-1654. 10.1007/s00439-012-1194-y.
PubMed
PubMed Central
Google Scholar
Pattin KA, Moore JH: Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases. Hum Genet. 2008, 124 (1): 19-29. 10.1007/s00439-008-0522-8.
CAS
PubMed
PubMed Central
Google Scholar
Barrenäs F, Chavali S, Alves AC, Coin L, Jarvelin MR, Jörnsten R, Langston MA, Ramasamy A, Rogers G, Wang H, Benson M: Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms. Genome Biol. 2012, 13 (6): R46. 10.1186/gb-2012-13-6-r46.
PubMed
PubMed Central
Google Scholar
Pahikkala T, Okser S, Airola A, Salakoski T, Aittokallio T: Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations. Algorithm Mol Biol. 2012, 7 (1): 11. 10.1186/1748-7188-7-11.
Google Scholar
Okser S, Lehtimäki T, Elo LL, Mononen N, Peltonen N: Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study. PLoS Genet. 2010, 6 (9): e1001146. 10.1371/journal.pgen.1001146.
PubMed
PubMed Central
Google Scholar
Kooperberg C, LeBlanc M, Obenchain V: Risk prediction using genome-wide association studies. Genet Epidemiol. 2010, 34 (7): 643-652. 10.1002/gepi.20509.
PubMed
PubMed Central
Google Scholar
Balding DJ: A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006, 7 (10): 781-791. 10.1038/nrg1916.
CAS
PubMed
Google Scholar
Evans DM, Visscher PM, Wray NR: Harnessing the Information Contained Within Genome-wide Association Studies to Improve Individual Prediction of Complex Disease Risk. Hum Mol Genet. 2009, 18 (18): 3525-3531. 10.1093/hmg/ddp295.
CAS
PubMed
Google Scholar
Clarke GM, Anderson CA, Pettersson FH, Cardon LR, Morris AP, Zondervan KT: Basic statistical analysis in genetic case-control studies. Nat Protoc. 2011, 6 (2): 121-133.
CAS
PubMed
PubMed Central
Google Scholar
Bansal V, Libiger O, Torkamani A, Schork NJ: Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet. 2010, 11 (11): 773-785.
CAS
PubMed
PubMed Central
Google Scholar
Ladouceur M, Dastani Z, Aulchenko YS, Greenwood CM, Richards JB: The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals. PLoS Genet. 2012, 8 (2): e1002496. 10.1371/journal.pgen.1002496.
CAS
PubMed
PubMed Central
Google Scholar
Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, Christiani DC, Wurfel MM, Lin X, NHLBI GO Exome Sequencing Project—ESP Lung Project Team: Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012, 91 (2): 224-237. 10.1016/j.ajhg.2012.06.007.
CAS
PubMed
PubMed Central
Google Scholar
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, 69 (1): 138-147. 10.1086/321276.
CAS
PubMed
PubMed Central
Google Scholar
Kraft P, Wacholder S, Cornelis MC, Hu FB, Hayes RB, Thomas G, Hoover R, Hunter DJ, Chanock S: Beyond odds ratios: communicating disease risk based on genetic profiles. Perspective. Nat Rev Genetics. 2009, 10: 264-269. 10.1038/nrg2516.
CAS
PubMed
Google Scholar
Saeys Y, Inza I, Larrañaga P: A review of feature selection techniques in bioinformatics. Bioinformatics. 2007, 23 (19): 2507-2517. 10.1093/bioinformatics/btm344.
CAS
PubMed
Google Scholar
Guyon I, Elisseeff A: An introduction to variable and feature selection. J Mach Learn Res. 2003, 3: 1157-1182.
Google Scholar
Wu TT, Chen YF, Hastie T, Sobel E, Lange K: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics. 2009, 25 (6): 714-721. 10.1093/bioinformatics/btp041.
CAS
PubMed
PubMed Central
Google Scholar
He Q, Lin DY: A variable selection method for genome-wide association studies. Bioinformatics. 2011, 27 (1): 1-8. 10.1093/bioinformatics/btq600.
CAS
PubMed
Google Scholar
Rakitsch B, Lippert C, Stegle O, Borgwardt K: A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics. 2013, 29 (2): 206-214. 10.1093/bioinformatics/bts669.
CAS
PubMed
Google Scholar
Aha DW, Bankert RL: A comparative evaluation of sequential feature selection algorithms. Learning from Data: Artificial Intelligence and Statistics V, Lecture Notes in Statistics. Edited by: Fisher DH, Lenz HJ. 1996, New York: Springer-Verlag, 199-206.
Google Scholar
Ambroise C, McLachlan GJ: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A. 2002, 99 (10): 6562-6566. 10.1073/pnas.102102699.
CAS
PubMed
PubMed Central
Google Scholar
Simon R, Radmacher MD, Dobbin K, McShane LM: Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003, 95 (1): 14-18. 10.1093/jnci/95.1.14.
CAS
PubMed
Google Scholar
Varma S, Simon R: Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006, 7: 91. 10.1186/1471-2105-7-91.
PubMed
PubMed Central
Google Scholar
Smialowski P, Frishman D, Kramer S: Pitfalls of supervised feature selection. Bioinformatics. 2010, 26 (3): 440-443. 10.1093/bioinformatics/btp621.
CAS
PubMed
Google Scholar
Statnikov A, Wang L, Aliferis C: A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics. 2008, 9 (1): 319. 10.1186/1471-2105-9-319.
PubMed
PubMed Central
Google Scholar
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM: Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010, 42 (7): 565-569. 10.1038/ng.608.
CAS
PubMed
PubMed Central
Google Scholar
Makowsky R, Pajewski NM, Klimentidis YC, Vazquez AI, Duarte CW, Allison DB, de los Campos G: Beyond missing heritability: prediction of complex traits. PLoS Genet. 2011, 7 (4): e1002051. 10.1371/journal.pgen.1002051.
CAS
PubMed
PubMed Central
Google Scholar
Lambert CG, Black LJ: Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics. 2012, 13 (2): 195-203. 10.1093/biostatistics/kxr055.
PubMed
PubMed Central
Google Scholar
Castaldi PJ, Dahabreh IJ, Ioannidis JP: An empirical assessment of validation practices for molecular classifiers. Brief Bioinform. 2011, 12 (3): 189-202. 10.1093/bib/bbq073.
PubMed
PubMed Central
Google Scholar
König I: Validation in genetic association studies. Brief Bioinform. 2011, 12 (3): 253-258. 10.1093/bib/bbq074.
PubMed
Google Scholar
Tian C, Gregersen PK, Seldin MF: Accounting for ancestry: population substructure and genome-wide association studies. Hum Mol Genet. 2008, 17 (R2): R143-R150. 10.1093/hmg/ddn268.
CAS
PubMed
PubMed Central
Google Scholar
Greene CS, Penrod NM, Williams SM, Moore JH: Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One. 2009, 4 (6): e5639. 10.1371/journal.pone.0005639.
PubMed
PubMed Central
Google Scholar
Torkamani A, Topol EJ, Schork NJ: Pathway analysis of seven common diseases assessed by genome-wide association. Genomics. 2008, 92 (5): 265-272. 10.1016/j.ygeno.2008.07.011.
CAS
PubMed
PubMed Central
Google Scholar
Torkamani A, Schork NJ: Pathway and network analysis with high-density allelic association data. Methods Mol Biol. 2009, 563: 289-301. 10.1007/978-1-60761-175-2_16.
CAS
PubMed
Google Scholar
Zhong H, Yang X, Kaplan LM, Molony C, Schadt EE: Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am J Hum Genet. 2010, 86 (4): 581-591. 10.1016/j.ajhg.2010.02.020.
CAS
PubMed
PubMed Central
Google Scholar
Wang K, Li M, Hakonarson H: Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010, 11 (12): 843-854. 10.1038/nrg2884.
CAS
PubMed
Google Scholar
Ramanan VK, Shen L, Moore JH, Saykin AJ: Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet. 2012, 28 (7): 323-332. 10.1016/j.tig.2012.03.004.
CAS
PubMed
PubMed Central
Google Scholar
Srinivasan BS, Doostzadeh J, Absalan F, Mohandessi S, Jalili R, Bigdeli S, Wang J, Mahadevan J, Lee CL, Davis RW, William Langston J, Ronaghi M: Whole genome survey of coding SNPs reveals a reproducible pathway determinant of Parkinson disease. Hum Mutat. 2009, 30 (2): 228-238. 10.1002/humu.20840.
CAS
PubMed
Google Scholar
Askland K, Read C, Moore J: Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Hum Genet. 2009, 125 (1): 63-79. 10.1007/s00439-008-0600-y.
CAS
PubMed
Google Scholar
Luo L, Peng G, Zhu Y, Dong H, Amos CI, Xiong M: Genome-wide gene and pathway analysis. Eur J Hum Genet. 2010, 18 (9): 1045-1053. 10.1038/ejhg.2010.62.
CAS
PubMed
PubMed Central
Google Scholar
Peng G, Luo L, Siu H, Zhu Y, Hu P, Hong S, Zhao J, Zhou X, Reveille JD, Jin L, Amos CI, Xiong M: Gene and pathway-based second-wave analysis of genome-wide association studies. Eur J Hum Genet. 2010, 18 (1): 111-117. 10.1038/ejhg.2009.115.
PubMed
Google Scholar
Lee E, Chuang HY, Kim JW, Ideker T, Lee D: Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008, 4 (11): e1000217. 10.1371/journal.pcbi.1000217.
PubMed
PubMed Central
Google Scholar
Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, Coin L, Levin M: Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases. PLoS One. 2009, 4 (11): e8068. 10.1371/journal.pone.0008068.
PubMed
PubMed Central
Google Scholar
Braun R, Buetow K: Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genet. 2011, 7 (6): e1002101. 10.1371/journal.pgen.1002101.
CAS
PubMed
PubMed Central
Google Scholar
Bebek G, Koyutürk M, Price ND, Chance MR: Network biology methods integrating biological data for translational science. Brief Bioinform. 2012, 13 (4): 446-459. 10.1093/bib/bbr075.
PubMed
PubMed Central
Google Scholar
McKinney BA, Crowe JE, Guo J, Tian D: Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet. 2009, 5 (3): e1000432. 10.1371/journal.pgen.1000432.
PubMed
PubMed Central
Google Scholar
Lavender NA, Rogers EN, Yeyeodu S, Rudd J, Hu T, Zhang J, Brock GN, Kimbro KS, Moore JH, Hein DW, Kidd LC: Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer. BMC Med Genomics. 2012, 5: 11. 10.1186/1755-8794-5-11.
CAS
PubMed
PubMed Central
Google Scholar
Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH: Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics. 2011, 12: 364. 10.1186/1471-2105-12-364.
CAS
PubMed
PubMed Central
Google Scholar
Phillips PC: Epistasis: the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet. 2008, 9 (11): 855-867. 10.1038/nrg2452.
CAS
PubMed
PubMed Central
Google Scholar
Schadt EE: Molecular networks as sensors and drivers of common human diseases. Nature. 2009, 461 (7261): 218-223. 10.1038/nature08454.
CAS
PubMed
Google Scholar
Ideker T, Dutkowski J, Hood L: Boosting signal-to-noise in complex biology: prior knowledge is power. Cell. 2011, 144 (6): 860-863. 10.1016/j.cell.2011.03.007.
CAS
PubMed
PubMed Central
Google Scholar
Vidal M, Cusick ME, Barabási AL: Interactome networks and human disease. Cell. 2011, 144 (6): 986-998. 10.1016/j.cell.2011.02.016.
CAS
PubMed
PubMed Central
Google Scholar
Barabási AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011, 12 (1): 56-68. 10.1038/nrg2918.
PubMed
PubMed Central
Google Scholar
Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007, 3: 140-
PubMed
PubMed Central
Google Scholar
Winter C, Kristiansen G, Kersting S, Roy J, Aust D, Knösel T, Rümmele P, Jahnke B, Hentrich V, Rückert F, Niedergethmann M, Weichert W, Bahra M, Schlitt HJ, Settmacher U, Friess H, Büchler M, Saeger HD, Schroeder M, Pilarsky C, Grützmann R: Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. PLoS Comput Biol. 2012, 8 (5): e1002511. 10.1371/journal.pcbi.1002511.
CAS
PubMed
PubMed Central
Google Scholar
Lavi O, Dror G, Shamir R: Network-induced classification kernels for gene expression profile analysis. J Comput Biol. 2012, 19 (6): 694-709. 10.1089/cmb.2012.0065.
CAS
PubMed
PubMed Central
Google Scholar
Feldman I, Rzhetsky A, Vitkup D: Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci U S A. 2008, 105 (11): 4323-4328. 10.1073/pnas.0701722105.
CAS
PubMed
PubMed Central
Google Scholar
Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BM, Kappos L, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR, GeneMSA Consortium: Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet. 2009, 18 (11): 2078-2090. 10.1093/hmg/ddp120.
CAS
PubMed
PubMed Central
Google Scholar
McKinney BA, Pajewski NM: Six Degrees of Epistasis: Statistical Network Models for GWAS. Front Genet. 2012, 2: 109-
PubMed
PubMed Central
Google Scholar
Mooney M, Wilmot B, McWeeney S, The Bipolar Genome Study: The GA and the GWAS: Using Genetic Algorithms to Search for Multi-locus Associations. IEEE/ACM Trans Comput Biol Bioinform. 2012, 9 (3): 899-910.
PubMed
Google Scholar
Deisboeck TS: Personalizing medicine: a systems biology perspective. Mol Syst Biol. 2009, 5: 249-
PubMed
PubMed Central
Google Scholar
Reynolds KS: Achieving the promise of personalized medicine. Clin Pharmacol Ther. 2012, 92 (4): 401-405. 10.1038/clpt.2012.147.
CAS
PubMed
Google Scholar
Hopkins AL: Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008, 4: 682-690. 10.1038/nchembio.118.
CAS
PubMed
Google Scholar
Jelier R, Semple JI, Garcia-Verdugo R, Lehner B: Predicting phenotypic variation in yeast from individual genome sequences. Nat Genet. 2011, 43 (12): 1270-1274. 10.1038/ng.1007.
CAS
PubMed
Google Scholar
Burga A, Casanueva MO, Lehner B: Predicting mutation outcome from early stochastic variation in genetic interaction partners. Nature. 2011, 480 (7376): 250-253. 10.1038/nature10665.
CAS
PubMed
Google Scholar
Huang W, Richards S, Carbone MA, Zhu D, Anholt RR, Ayroles JF, Duncan L, Jordan KW, Lawrence F, Magwire MM, Warner CB, Blankenburg K, Han Y, Javaid M, Jayaseelan J, Jhangiani SN, Muzny D, Ongeri F, Perales L, Wu YQ, Zhang Y, Zou X, Stone EA, Gibbs RA, Mackay TF: Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc Natl Acad Sci USA. 2012, 109 (39): 15553-15559. 10.1073/pnas.1213423109.
CAS
PubMed
PubMed Central
Google Scholar
Corander J, Aittokallio T, Ripatti S, Kaski S: The rocky road to personalized medicine: computational and statistical challenges. Personalized Med. 2012, 9 (2): 109-114. 10.2217/pme.12.1.
Google Scholar
Surakka I, Kristiansson K, Anttila V, Inouye M, Barnes C, Moutsianas L, Salomaa V, Daly M, Palotie A, Peltonen L, Ripatti S: Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging. Genome Res. 2010, 20 (10): 1344-1351. 10.1101/gr.106534.110.
CAS
PubMed
PubMed Central
Google Scholar
Holm H, Gudbjartsson DF, Sulem P, Masson G, Helgadottir HT, Zanon C, Magnusson OT, Helgason A, Saemundsdottir J, Gylfason A, Stefansdottir H, Gretarsdottir S, Matthiasson SE, Thorgeirsson GM, Jonasdottir A, Sigurdsson A, Stefansson H, Werge T, Rafnar T, Kiemeney LA, Parvez B, Muhammad R, Roden DM, Darbar D, Thorleifsson G, Walters GB, Kong A, Thorsteinsdottir U, Arnar DO, Stefansson K: A rare variant in MYH6 is associated with high risk of sick sinus syndrome. Nat Genet. 2011, 43 (4): 316-320. 10.1038/ng.781.
CAS
PubMed
PubMed Central
Google Scholar
Marko NF, Weil RJ: Mathematical modeling of molecular data in translational medicine: theoretical considerations. Sci Transl Med. 2010, 2 (56): 56rv4. 10.1126/scitranslmed.3001207.
PubMed
Google Scholar
Peltola T, Marttinen P, Jula A, Salomaa V, Perola M, Vehtari A: Bayesian variable selection in searching for additive and dominant effects in genome-wide data. PLoS One. 2012, 7 (1): e29115. 10.1371/journal.pone.0029115.
CAS
PubMed
PubMed Central
Google Scholar
Sebastiani P, Solovieff N, Dewan AT, Walsh KM, Puca A, Hartley SW, Melista E, Andersen S, Dworkis DA, Wilk JB, Myers RH, Steinberg MH, Montano M, Baldwin CT, Hoh J, Perls TT: Genetic signatures of exceptional longevity in humans. PLoS One. 2012, 7 (1): e29848. 10.1371/journal.pone.0029848.
CAS
PubMed
PubMed Central
Google Scholar
Ober U, Ayroles JF, Stone EA, Richards S, Zhu D, Gibbs RA, Stricker C, Gianola D, Schlather M, Mackay TF, Simianer H: Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLoS Genet. 2012, 8 (5): e1002685. 10.1371/journal.pgen.1002685.
CAS
PubMed
PubMed Central
Google Scholar
Sillanpää MJ: Detecting interactions in association studies by using simple allele recoding. Hum Hered. 2009, 67 (1): 69-75. 10.1159/000164401.
PubMed
Google Scholar
Ober U, Erbe M, Long N, Porcu E, Schlather M, Simianer H: Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data. Genetics. 2011, 188 (3): 695-708. 10.1534/genetics.111.128694.
PubMed
PubMed Central
Google Scholar
Beltrao P, Cagney G, Krogan NJ: Quantitative genetic interactions reveal biological modularity. Cell. 2010, 141 (5): 739-745. 10.1016/j.cell.2010.05.019.
CAS
PubMed
PubMed Central
Google Scholar
Lindén RO, Eronen VP, Aittokallio T: Quantitative maps of genetic interactions in yeast - comparative evaluation and integrative analysis. BMC Syst Biol. 2011, 5: 45. 10.1186/1752-0509-5-45.
PubMed
PubMed Central
Google Scholar
Dixon SJ, Costanzo M, Baryshnikova A, Andrews B, Boone C: Systematic mapping of genetic interaction networks. Annu Rev Genet. 2009, 43: 601-625. 10.1146/annurev.genet.39.073003.114751.
CAS
PubMed
Google Scholar
Wang Z, Wang Y, Tan KL, Wong L, Agrawal D: eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study. Bioinformatics. 2011, 27 (8): 1045-1051. 10.1093/bioinformatics/btr091.
CAS
PubMed
Google Scholar
Chen GK: A scalable and portable framework for massively parallel variable selection in genetic association studies. Bioinformatics. 2012, 28 (5): 719-720. 10.1093/bioinformatics/bts015.
CAS
PubMed
PubMed Central
Google Scholar
Gyenesei A, Moody J, Laiho A, Semple CA, Haley CS, Wei WH: BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies. Nucleic Acids Res. 2012, 40 (Web Server issue): W628-W632.
CAS
PubMed
PubMed Central
Google Scholar
Schupbach T, Xenarios I, Bergmann S, Kapur K: FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics. 2010, 26 (11): 1468-1469. 10.1093/bioinformatics/btq147.
PubMed
PubMed Central
Google Scholar
Hannum G, Srivas R, Guénolé A, van Attikum H, Krogan NJ, Karp RM, Ideker T: Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet. 2009, 5 (12): e1000782. 10.1371/journal.pgen.1000782.
PubMed
PubMed Central
Google Scholar
Michaut M, Bader GD: Multiple genetic interaction experiments provide complementary information useful for gene function prediction. PLoS Comput Biol. 2012, 8 (6): e1002559. 10.1371/journal.pcbi.1002559.
CAS
PubMed
PubMed Central
Google Scholar
Hartley SW, Monti S, Liu CT, Steinberg MH, Sebastiani P: Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction. Front Genet. 2012, 3: 176-
PubMed
PubMed Central
Google Scholar
Tuikkala J, Vähämaa H, Salmela P, Nevalainen OS, Aittokallio T: A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization. BioData Min. 2012, 26 (5): 2-
Google Scholar
Ashworth A, Lord CJ, Reis-Filho JS: Genetic interactions in cancer progression and treatment. Cell. 2011, 145 (1): 30-38. 10.1016/j.cell.2011.03.020.
CAS
PubMed
Google Scholar
Urbach D, Lupien M, Karagas MR, Moore JH: Cancer heterogeneity: origins and implications for genetic association studies. Trends Genet. 2012, 28 (11): 538-543. 10.1016/j.tig.2012.07.001.
CAS
PubMed
PubMed Central
Google Scholar
Galvan A, Ioannidis JP, Dragani TA: Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer. Trends Genet. 2010, 26 (3): 132-141. 10.1016/j.tig.2009.12.008.
CAS
PubMed
PubMed Central
Google Scholar
Kaelin WG: The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer. 2005, 5 (9): 689-698. 10.1038/nrc1691.
CAS
PubMed
Google Scholar
Iglehart JD, Silver DP: Synthetic lethality-a new direction in cancer-drug development. N Engl J Med. 2009, 361 (2): 189-191. 10.1056/NEJMe0903044.
CAS
PubMed
Google Scholar
Heiskanen MA, Aittokallio T: Mining high-throughput screens for cancer drug targets—lessons from yeast chemical-genomic profiling and synthetic lethality. Wiley Interdisciplinary Rev: Data Min Knowl Discov. 2012, 2 (3): 263-272. 10.1002/widm.1055.
Google Scholar
Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protocol. 2009, 4 (1): 44-57.
CAS
Google Scholar
Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37 (1): 1-13. 10.1093/nar/gkn923.
Google Scholar
Smoot M, Ono K, Ruscheinski J, Wang P-L, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27 (3): 431-432. 10.1093/bioinformatics/btq675.
CAS
PubMed
Google Scholar
Merico D, Isserlin R, Stueker O, Emili A, Bader GD: Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLoS One. 2010, 5 (11): e13984. 10.1371/journal.pone.0013984.
PubMed
PubMed Central
Google Scholar