Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3:186–191
PubMed
CrossRef
Google Scholar
Lambert JC, Ibrahim-Verbaas CA, Harold D et al (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45:1452–1458
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:17
Google Scholar
Miller JA, Horvath S, Geschwind DH (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A 107:12698–12703
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Miller JA, Oldham MC, Geschwind DH (2008) A systems level analysis of transcriptional changes in Alzheimer’s disease and normal aging. J Neurosci 28:1410–1420
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Miller JA, Woltjer RL, Goodenbour JM et al (2013) Genes and pathways underlying regional and cell type changes in Alzheimer’s disease. Genome Med 5:48
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Rhinn H, Fujita R, Qiang L et al (2013) Integrative genomics identifies APOE epsilon4 effectors in Alzheimer’s disease. Nature 500:45–50
CAS
PubMed
CrossRef
Google Scholar
Zhang B, Gaiteri C, Bodea LG et al (2013) Integrated systems approach identifies genetic nodes and networks in late-onset alzheimer’s disease. Cell 153:707–720
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Emilsson V, Thorleifsson G, Zhang B et al (2008) Genetics of gene expression and its effect on disease. Nature 452:423–428
CAS
PubMed
CrossRef
Google Scholar
Storey JD, Tibshirani R (2003) Statistical methods for identifying differentially expressed genes in DNA microarrays. Methods Mol Biol 224:149–157
CAS
PubMed
Google Scholar
Langfelder P, Zhang B, Horvath S (2008) Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24:719–720
CAS
PubMed
CrossRef
Google Scholar
Schadt EE, Sachs A, Friend S (2005) Embracing complexity, inching closer to reality. Sci STKE 2005:pe40
PubMed
Google Scholar
Yang X, Deignan JL, Qi H et al (2009) Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat Genet 41:415–423
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Zhu J, Zhang B, Schadt EE (2008) A systems biology approach to drug discovery. Adv Genet 60:603–635
CAS
PubMed
CrossRef
Google Scholar
Zhu J, Zhang B, Smith EN et al (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40:854–861
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Zhu J, Sova P, Xu Q et al (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol 10:e1001301
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Schadt EE, Lamb J, Yang X et al (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37:710–717
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Zhu J, Wiener MC, Zhang C et al (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3:e69
PubMed Central
PubMed
CrossRef
Google Scholar
Zhu J, Lum PY, Lamb J et al (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105:363–374
CAS
PubMed
CrossRef
Google Scholar
Chen Y, Zhu J, Lum PY et al (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452:429–435
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Schadt EE, Molony C, Chudin E et al (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6:e107
PubMed Central
PubMed
CrossRef
Google Scholar
Zhu J, Chen Y, Leonardson AS et al (2010) Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput Biol 6:e1000671
PubMed Central
PubMed
CrossRef
Google Scholar
Tran LM, Zhang B, Zhang Z et al (2011) Inferring causal genomic alterations in breast cancer using gene expression data. BMC Syst Biol 5:121
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Wang I-M, Zhang B, Yang X et al (2012) Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers. Mol Syst Biol 8:594
PubMed Central
PubMed
CrossRef
Google Scholar
Sharan R, Ulitsky I, Shamir R (2007) Network-based prediction of protein function. Mol Syst Biol 3:88
PubMed Central
PubMed
CrossRef
Google Scholar
Yang X, Zhang B, Molony C et al (2010) Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Res 20:1020–1036
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Kondo T, Takahashi K, Kohara N et al (2002) Heterogeneity of presenile dementia with bone cysts (Nasu-Hakola disease): three genetic forms. Neurology 59:1105–1107
CAS
PubMed
CrossRef
Google Scholar
Lanier LL, Bakker ABH (2000) The ITAM-bearing transmembrane adaptor DAP12 in lymphoid and myeloid cell function. Immunol Today 21:611–614
CAS
PubMed
CrossRef
Google Scholar
Paloneva J, Kestilä M, Wu J et al (2000) Loss-of-function mutations in TYROBP (DAP12) result in a presenile dementia with bone cysts. Nat Genet 25:357–361
CAS
PubMed
CrossRef
Google Scholar
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York
Google Scholar
Bertram L, McQueen MB, Mullin K et al (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet 39:17–23
CAS
PubMed
CrossRef
Google Scholar
ADNI. http://ida.loni.ucla.edu/
BrainSpan. http://www.brainspan.org/
GTEx. http://commonfund.nih.gov/GTEx/
Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS One 5:e12776
PubMed Central
PubMed
CrossRef
Google Scholar
Marbach D, Costello JC, Küffner R et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9:796–804
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Brennand KJ, Gage FH (2011) Concise review: the promise of human induced pluripotent stem cell-based studies of schizophrenia. Stem Cells 29:1915–1922
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Brennand KJ, Landek-Salgado MA, Sawa A (2014) Modeling heterogeneous patients with a clinical diagnosis of schizophrenia with induced pluripotent stem cells. Biol Psychiatry 75:936–944
PubMed Central
PubMed
CrossRef
Google Scholar
Brennand KJ, Simone A, Jou J et al (2011) Modelling schizophrenia using human induced pluripotent stem cells. Nature 473:221–225
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Qiang L, Fujita R, Abeliovich A (2013) Remodeling neurodegeneration: somatic cell reprogramming-based models of adult neurological disorders. Neuron 78:957–969
CAS
PubMed
CrossRef
Google Scholar
Qiang L, Fujita R, Yamashita T et al (2011) Directed conversion of Alzheimer’s disease patient skin fibroblasts into functional neurons. Cell 146:359–371
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Tran NN, Ladran IG, Brennand KJ (2013) Modeling schizophrenia using induced pluripotent stem cell-derived and fibroblast-induced neurons. Schizophr Bull 39:4–10
PubMed Central
PubMed
CrossRef
Google Scholar
Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868
CAS
PubMed Central
PubMed
CrossRef
Google Scholar
Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 5:418–429
Google Scholar
Gill R, Datta S, Datta S (2010) A statistical framework for differential network analysis from microarray data. BMC Bioinformatics 11:95
PubMed Central
PubMed
CrossRef
Google Scholar