“Good Enough Solutions” and the Genetics of Complex Diseases

Details

• Alternative Title:
  Circ Res
• Personal Author:
  Weiss, James N. ; Karma, Alain ; MacLellan, W. Robb ; Deng, Mario ; Rau, Christoph ; Rees, Colin M. ; Wang, Jessica ; Wisniewski, Nicholas ; Eskin, Eleazar ; Horvath, Steve ; Qu, Zhilin ; Wang, Yibin ; Lusis, Aldons J.
• Description:
  Rationale and Objective
  
  In this Emerging Science Review, we discuss a systems genetics strategy, which we call Gene Module Association Study (GMAS), as a novel approach complementing Genome Wide Association Studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly.
  
  Methods
  
  The first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high throughput) data from the population to construct gene co-expression networks. Co-expression analysis typically groups 20,000 genes into 20–30 modules containing 10’s to 100’s of genes, whose aggregate behavior can be represented by the module’s “eigengene.” The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of SNPs.
  
  Results and Conclusions
  
  The goal of the GMAS approach is to identify groups of co-regulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the “good enough solution” concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, i.e. a “good enough solution” to accomplish normal biological functions. However, when faced with environmental stresses, some “good enough solutions” adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations.
  
  
• Subjects:
  Animals Article Computational Biology Databases, Genetic Evolution, Molecular Gene Expression Profiling Gene Expression Regulation Gene Regulatory Networks Genetic Predisposition To Disease Genetics Of Complex Diseases Genetic Variation Genome-Wide Association Study Genomics Humans Hybrid Mouse Diversity Panel Inheritance Patterns Oligonucleotide Array Sequence Analysis Phenotype Reproducibility Of Results Scale-free Networks Systems Biology Systems Genetics

  More +
• Source:
  Circ Res. 111(4):493-504
• Pubmed ID:
  22859671
• Pubmed Central ID:
  PMC3428228
• Document Type:
  Journal Article
• Funding:
  R01 HL094322/HL/NHLBI NIH HHSUnited States/ ; P01 HL080111/HL/NHLBI NIH HHSUnited States/ ; HHSN268201000035C/HL/NHLBI NIH HHSUnited States/ ; 1DP3 D094311/DP/NCCDPHP CDC HHSUnited States/ ; R01 HL101228/HL/NHLBI NIH HHSUnited States/ ; P01 HL30568/HL/NHLBI NIH HHSUnited States/ ; R21 HL110667-01/HL/NHLBI NIH HHSUnited States/ ; P01 HL030568/HL/NHLBI NIH HHSUnited States/ ; T32 HL069766/HL/NHLBI NIH HHSUnited States/ ; R01 GM095656/GM/NIGMS NIH HHSUnited States/ ; R21 HL110667/HL/NHLBI NIH HHSUnited States/ ; P01 HL28481/HL/NHLBI NIH HHSUnited States/ ; K25 HL080079/HL/NHLBI NIH HHSUnited States/ ; P01 HL078931/HL/NHLBI NIH HHSUnited States/ ; P01 HL028481/HL/NHLBI NIH HHSUnited States/ ; UL1 TR000124/TR/NCATS NIH HHSUnited States/ ; T32HL69766/HL/NHLBI NIH HHSUnited States/ ; R01 HL070748/HL/NHLBI NIH HHSUnited States/
• Volume:
  111
• Issue:
  4
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:f3da1e1d0afc0a27570fda8a135d3dc44ea3658d256f82e5f38d6dee7d218ec6
• Download URL:
  https://stacks.cdc.gov/view/cdc/33422/cdc_33422_DS1.pdf
• File Type:
  [PDF - 3.38 MB ]