Supplementing high-density SNP microarrays for additional coverage of disease-related genes: addiction as a paradigm.
Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.
Saccone
Scott F
SF
Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA. ssaccone@wustl.edu
Bierut
Laura J
LJ
Chesler
Elissa J
EJ
Kalivas
Peter W
PW
Lerman
Caryn
C
Saccone
Nancy L
NL
Uhl
George R
GR
Li
Chuan-Yun
CY
Philip
Vivek M
VM
Edenberg
Howard J
HJ
Sherry
Stephen T
ST
Feolo
Michael
M
Moyzis
Robert K
RK
Rutter
Joni L
JL
D000818:Animals; D016739:Behavior, Addictive / Q000235:genetics*; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci