Volume 6 Supplement 1
Multilocus and interaction-based genome scan for alcoholism risk factors in Caucasian Americans: the COGA study
© Williams et al; licensee BioMed Central Ltd 2005
Published: 30 December 2005
In this paper, we applied the nonparametric linkage regression approach to the Caucasian genome scan data from the Collaborative Study on the Genetics of Alcoholism to search for regions of the genome that exhibit evidence for linkage to putative alcoholism-predisposing genes. The multipoint single-locus model identified four regions of the genome with LOD scores greater than one. These regions were on 7p near D7S1790 (LOD = 1.31), two regions on 7q near D7S1870 (LOD = 1.15) and D7S1799 (LOD = 1.13) and 21q near D21S1440 and D21S1446 (LOD = 1.78). Jointly modeling these loci provided stronger evidence for linkage in each of these regions (LOD = 1.58 on 7q11, LOD = 1.61 on 11q23, and LOD = 1.95 on 21q22). The evidence for linkage tended to increase among pedigrees with earlier mean age of onset at 8q23 (p = 0.0016), 14q21 (p = 0.0079), and 18p12 (p = 0.0021) and with later mean age of onset at 4q35 (p = 0.0067) and 9p22 (p = 0.0008).
The Collaborative Study on the Genetics of Alcoholism (COGA) is a study designed to identify the genetic influences of alcoholism. Although alcoholism itself and the corresponding risk factors are heritable, they are strongly believed to be complex genetic traits. Thus, in the search for genes that influence these traits we expect significant genetic heterogeneity, gene × gene, and gene × environment interactions. Statistical methods that have the flexibility to simultaneously consider multiple loci and environmental factors are potentially valuable tools in the search for putative disease-predisposing loci. The purpose of this paper is to examine the evidence for linkage using multilocus nonparametric linkage regression modeling and explore whether the evidence for linkage varies by the age of onset of alcoholism [1, 2].
The genotyped sample provided by COGA to the Genetics Analysis Workshop 14 consists of 102 Caucasian pedigrees (1,078 individuals) and 41 non-Caucasian pedigrees (526 individuals). Given the limited number of pedigrees of non-Caucasian ethnicity, this paper focuses on the self-reported Caucasian sample genotyped on 315 microsatellite markers and 15,406 autosomal single-nucleotide polymorphisms (SNPs). The alcohol dependence diagnosis required that an individual have DSM-III-R alcohol dependence and Feighner alc definite. This yielded four affection status classifications: 1) unaffected, 2) never drank, 3) unaffected with some symptoms, and 4) affected. The primary focus of these analyses will use affection status 4 only.
The initial genome scan linkage analyses were computed using the nonparametric linkage (NPL) (pairs) and NPL (all) statistics under 1) the exponential allele-sharing model implemented in GENEHUNTER PLUS  and 2) a conditional logistic regression parameterization denoted NPL regression [1, 2]. This regression-based approach provides a one degree of freedom test of the evidence for linkage conditional on the evidence for linkage at the other loci in the model. Model building was performed using step-wise regression techniques. To test for an interaction between two loci, we included the two loci and their statistical interaction in the model and computed the one degree of freedom test of the interaction coefficient. In addition, we tested for interactions between the degree of sharing (identity by descent (IBD)) at a locus and the pedigree-specific mean age at alcoholism diagnosis. The p-value should be interpreted as a point-wise p-value and was not adjusted for the number of comparisons across the genome. All analyses are based on multipoint IBD estimates.
Ordered subset analyses (OSAs)  were computed to investigate the influence of a pedigree's mean age at alcoholism diagnosis on the evidence for linkage. Analyses were conducted ranking the mean family age of onset in ascending, and then in descending order. Linkage analyses were computed on contiguous subsets of pedigrees based on the mean age of onset ranking. The statistical significance of the change in the LOD score was evaluated by a permutation test under the null hypothesis that the ranking of the covariate is independent of the LOD score of the family on the target chromosome. Thus, the families were randomly permuted with respect to the covariate ranking and an analysis proceeded as above for each permutation of these data. The resulting empirical distribution of the change in the LOD score yielded a chromosome-wide p-value (Δp). MERLIN  was also used to perform a genome scan and was subsequently used on the SNP data. Due to computation time only the chromosomes that showed linkage with the microsatellites were run through MERLIN for linkage analysis. Cox et al.  examined the decay of linkage disequilibrium (LD) across the genome and found little evidence that adjacent markers exhibited significant LD, thus validating the use of the SNP data for linkage analysis. More specifically, in the absence of parental genotype data LD between markers can inflate the type 1 error rate in linkage analysis. The allele frequencies in the MERLIN analyses were computed in MERLIN using founders. As above, multipoint IBD estimates were computed and the NPL regression analysis was computed based on the NPL (pairs) and NPL (all) statistics.
Using the strictest criteria of affection status, there were 643 affected individuals from 102 families. Among these families there were 656 relative pairs including: 404 full-sib pairs, 9 half-sib pairs, 8 grandparent-grandchild pairs, 178 avuncular pairs and 19 other relative pairs. The families consisted of pedigrees with two (n = 5), three (n = 30), four (n = 32), five (n = 21), six (n = 8) and seven or greater (n = 6) individuals diagnosed with alcoholism.
Single- and multiple-locus NPL regression results
Single locus model
7p21 (17 cM)
7q11 (112 cM)
7q22 (145 cM)
21q22 (58 cM)
D21S1440 / D21S1446
Linkage and age of onset interaction analysis
NPL regression interaction analyses for age at diagnosis
Mean ± SD (n)
4q35 (216 cM)
24.08 ± 5.26 (43)
21.63 ± 3.69 (59)
8q23 (139 cM)
21.26 ± 2.90 (34)
23.37 ± 5.08 (68)
9p22 (34 cM)
24.52 ± 5.40 (39)
21.52 ± 5.55 (63)
14q21 (49 cM)
21.32 ± 3.98 (39)
23.50 ± 4.73 (63)
18q12 (57 cM)
21.44 ± 3.90 (39)
23.43 ± 4.80 (63)
Upon reviewing two previously published genome scans of alcohol dependence, two of our regions were identified in these published studies. The strongest evidence of linkage when looking across both the microsatellites and SNPs in this set of COGA data was on chromosome 7. The region on 7p was also identified in an American Indian population  within 10 cM of our peak with a nominal regression p-value of 0.009. The 7q peak is a series of peaks from about 100 cM to 160 cM; this region was found in the original COGA analyses  as well. The 11q peak was also replicated in the American Indian population  within 10 cM of our peak with a nominal regression p-value of 0.02. The 21q22 result showed up in both the ASM analysis and the NPL regression analysis, but completely disappeared when the SNP analysis was done.
Alcoholism is a genetically complex disease and therefore requires sophisticated consideration of multigenic and phenotypic influences. In this study methods that consider genetic heterogeneity, gene × gene interactions, gene × age-of-onset interactions, and joint modeling of multiple loci increased the evidence for linkage at three chromosomal locations, two of which had been previously identified as being associated with alcohol dependence. These methods reduced the linkage support interval at all three loci. In addition, testing for a dependence of the evidence for linkage on age of onset identified five additional regions of interest. These results suggest the potential utility of incorporating characteristics of complex genetic traits in the analysis.
Collaborative Study on the Genetics of Alcoholism
Identity by descent
Ordered subset analysis
The authors acknowledge Stephanie R. Beck and Joel K. Campbell for their assistance in preparing the data.
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