TY - JOUR
T1 - Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data
AU - Wen, Sheron
AU - Wang, Chenguang
AU - Berg, Arthur
AU - Li, Yao
AU - Chang, Myron M.
AU - Fillingim, Roger B.
AU - Wallace, Margaret R.
AU - Staud, Roland
AU - Kaplan, Lee
AU - Wu, Rongling
N1 - Funding Information:
This work is supported by Joint grant DMS/NIGMS-0540745 and NIH RO1 grant NS41670.
PY - 2009/8/11
Y1 - 2009/8/11
N2 - Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.
AB - Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.
UR - http://www.scopus.com/inward/record.url?scp=71049135170&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71049135170&partnerID=8YFLogxK
U2 - 10.1186/1748-7188-4-11
DO - 10.1186/1748-7188-4-11
M3 - Article
C2 - 19671182
AN - SCOPUS:71049135170
SN - 1748-7188
VL - 4
SP - 11
JO - Algorithms for Molecular Biology
JF - Algorithms for Molecular Biology
IS - 1
M1 - 1748
ER -