Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor.
BACKGROUND
In candidate-gene association studies of single nucleotide polymorphisms (SNPs), multilocus analyses are frequently of high dimensionality when considering haplotypes or haplotype pairs (diplotypes) and differing modes of expression. Often, while candidate genes are selected based on their biological involvement in a given pathway, little is known about the functionality of SNPs to guide association studies. Investigators face the challenge of exploring multiple SNP models to elucidate which variants, independently or in combination, might be associated with a disease of interest. A data mining module, hapConstructor (freely-available in Genie software) performs systematic construction and association testing of multilocus genotype data in a Monte Carlo framework. Our objective was to assess its utility to guide statistical analyses of haplotypes within a candidate region (or combined genotypes across candidate genes) beyond that offered by a standard logistic regression approach.


METHODS
We applied the hapConstructor method to a multilocus investigation of candidate genes involved in pro-inflammatory cytokine IL6 production, IKBKB, IL6, and NFKB1 (16 SNPs total) hypothesized to operate together to alter colorectal cancer risk. Data come from two U.S. multicenter studies, one of colon cancer (1,556 cases and 1,956 matched controls) and one of rectal cancer (754 cases and 959 matched controls).


RESULTS
hapConstructor enabled us to identify important associations that were further analyzed in logistic regression models to simultaneously adjust for confounders. The most significant finding (nominal P = 0.0004; false discovery rate q = 0.037) was a combined genotype association across IKBKB SNP rs5029748 (1 or 2 variant alleles), IL6 rs1800797 (1 or 2 variant alleles), and NFKB1 rs4648110 (2 variant alleles) which conferred an ~80% decreased risk of colon cancer.


CONCLUSIONS
Strengths of hapConstructor were: systematic identification of multiple loci within and across genes important in CRC risk; false discovery rate assessment; and efficient guidance of subsequent logistic regression analyses.
Epidemiology, Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, Utah, USA. karen.curtin@hsc.utah.edu
Karen
K
Curtin
Roger K
RK
Wolff
Jennifer S
JS
Herrick
Ryan
R
Abo
Martha L
ML
Slattery
D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D003110:Colonic Neoplasms / Q000235:genetics*; D005260:Female; D056726:Genetic Association Studies; D056426:Genetic Loci; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D051550:I-kappa B Kinase / Q000235:genetics / Q000378:metabolism; D007249:Inflammation / Q000235:genetics*; D015850:Interleukin-6 / Q000235:genetics / Q000378:metabolism; D007407:Interviews as Topic; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D052002:NF-kappa B p50 Subunit / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D012004:Rectal Neoplasms / Q000235:genetics*; D012307:Risk Factors; D012984:Software
D015850:Interleukin-6; D052002:NF-kappa B p50 Subunit; C489548:NFKB1 protein, human; D051550:I-kappa B Kinase