Volume 6 Supplement 1
Genetic Analysis Workshop 14: Microsatellite and singlenucleotide polymorphism
Identification of genes involved in alcohol consumption and cigarettes smoking
 Mariza de Andrade^{1}Email author,
 Curtis L Olswold^{1},
 Joshua P Slusser^{1},
 Larry A Tordsen^{1},
 Elizabeth J Atkinson^{1},
 Kari G Rabe^{1} and
 Susan L Slager^{1}
DOI: 10.1186/147121566S1S112
© de Andrade et al; licensee BioMed Central Ltd 2005
Published: 30 December 2005
Abstract
We compared the results of quantitative linkage analysis using singlenucleotide polymorphisms and microsatellite markers and introduced a new screening test for multivariate quantitative linkage analysis using the Collaborative Study on the Genetics of Alcoholism data. We analyzed 115 extended nonHispanic White families and tested for linkage using two phenotypes: the maximum number of drinks in a 24hour period and the number of packs smoked per day for one year. Our results showed that the linkage signal increased using singlenucleotide polymorphisms compared with microsatellite markers and that the screening test gave similar results to that of the bivariate analysis, suggesting its potential use in reducing overall analysis time.
Background
The Collaborative Study on the Genetics of Alcoholism (COGA) is a multicenter research program to detect and map susceptibility genes for alcohol dependence and related phenotypes. Numerous behavior measures were collected, two of which we considered for our study. The first is the maximum number of drinks in a 24 hour period (drink24), which can be considered a surrogate to alcoholism diagnosis and provides a quantitative measure to grade nonalcoholic individuals [1]. The second measure is the number of packs smoked per day for one year (pakyrs). Since pakyrs is highly correlated with alcohol consumption [2], these two measures are good candidates for multivariate linkage analysis. The goals of our analysis were twofold. First, we investigated the performance of a genomewide scan using singlenucleotide polymorphisms (SNPs) relative to the microsatellite markers. Several studies have shown gains in information when SNPs are used for qualitative traits, but advantages and disadvantages of SNPs have not been explored with quantitative traits [3, 4]. Second, we evaluated a new screening test for multivariate quantitative linkage analysis using drink24 and pakyrs as two correlated behavioral measures. Previous linkage studies have investigated these measures individually [5, 6], but currently no study has considered them in a bivariate analysis. Bivariate quantitative linkage analyses have been shown to identify genes with small effects where these genes may be missed with univariate analyses. However, these multivariate linkage analyses are computationally intensive as the number of traits used in the analysis increases. The proposed screening test combines univariate linkage results to determine whether a bivariate linkage analysis might be beneficial.
Methods
Data description
The COGA data consisted of 143 extended families, a mixture of large and small families, with 1,350 family members with clinical and demographic data. Because these families consisted of different ethnicities, we analyzed the families that were white, nonHispanic (WNH). A family was considered WNH if 75% of the reported ethnicity in the family was WNH; thus, our analyses were performed on 115 extended families. The phenotypes selected for the analysis were drink24 and pakyrs. Because drink24 and pakyrs measures have skewed distributions, a square root transformation (sqrt) was applied in both measures to normalize the distribution.
Genetic markers
The microsatellite markers and the Illumina SNPs were each used for our analyses. For the Illumina SNPs, we removed SNPs that were in linkage disequilibrium (LD) with another SNP. We based our criteria for LD using r^{2}, and the cutoff value of 0.4, which from our experience removed the effects of LD without a great loss of information. After dropping the SNPs in LD, a total of 350, 258, and 161 SNPs on chromosomes 1, 4, and 9, respectively, were used in our analyses. Multipoint identitybydescent (MIBD) sharing among pairs of relatives was calculated for microsatellite and SNP markers using the SIMWALK2 software program [7]
Quantitative trait linkage analysis
For the quantitative linkage analysis, we used the locally developed SPLUS multic library. This is a new library based on the C++ multic program from ACT [8]. For the analysis, we performed univariate and bivariate quantitative linkage analysis using a variance components (VC) approach. The details about univariate and multivariate quantitative linkage analysis are described in Amos [9] and de Andrade and Amos [10]. Sqrt(pakyrs) and sqrt(drink24) were adjusted for age and sex in the linkage analyses.
To test for genetic linkage, a likelihood ratio test (LRT) was applied. Under the null hypothesis, the linked gene parameter(s) is (are) restricted to equal 0. The distributions of the univariate and bivariate linkage tests are a mixture of 1/2 χ_{0}^{2} and 1/2 χ_{1}^{2}, and a mixture of 1/4 χ_{0}^{2}, 1/2 χ_{1}^{2}, and 1/4 χ_{3}^{2}, respectively [11]. In the univariate linkage analyses, we considered multipoint maximum LOD scores (MLS) ≥ 3.00 as statistically significant evidence of linkage, ≥ 2.00 as suggestive evidence, and ≥ 1.30 as tentative evidence of linkage [12]. These MLS thresholds correspond to pvalues of 0.0001, 0.001, and 0.007, respectively. To achieve levels of statistical significance in the bivariate linkage analysis comparable to the univariate thresholds, we calculated the threshold using a mixture of 1/4 χ_{0}^{2}, 1/2 χ_{1}^{2}, and 1/4 χ_{3}^{2}. This calculation provided MLS ≥ 4.00 as statistically significant evidence of linkage (i.e., p ≤ 0.0001), ≥ 2.87 as suggestive evidence (i.e., p ≤ 0.001), and ≥ 2.06 as tentative evidence of linkage (p ≤ 0.007). We inferred evidence of chromosomal regions with pleiotropic effects when the bivariate MLS met the criteria for at least tentative evidence of linkage and its nominal pvalue was less than the univariate maxima at the same location.
Screening test
Let us assume k quantitative traits are represented by Y_{1}, Y_{2}, ..., Y_{k}. For each trait a genomewide scanning linkage analysis is performed using the VC quantitative trait approach. For each trait i, and genomic position j, the quantitative trait locus (QTL) variance component estimate (σ^{2}_{ij}) is estimated with its standard error. Our hypothesis for the proposed screening test is: if there is a gene with pleiotropic effects, its QTL VC should be incremented in an additive manner using combinations of correlated traits by simply adding its respective univariate QTL VC. Let σ^{2}_{ijk} be the QTL VC for trait i, position j on chromosome k. The null hypothesis is that there is no pleiotropic effect at position j on chromosome k, i.e., H_{0}: = 0 ∀ i, j, k, which is equivalent to H_{0}: . The alternative hypothesis is H_{1}: σ^{2}_{ ijk }> 0 for any i, j, k. The test statistic will be , where is the maximum likelihood estimator (MLE) of σ^{2}_{ ijk }. Under H_{0}, E(σ^{2}_{ ijk }) = 0, ∀ i, j, k, S_{ ijk }~ 1/2 N (0, 1). By assuming the S_{ ijk }values are independent, , where T is the number of traits. Consequently by squaring and standardizing T_{ jk }, [11].
Results
Discussion
In our analyses using microsatellite markers, tentative and suggestive evidence of linkage were found on chromosomes 1, 2, 8, and 14 for sqrt(pakyrs) and on chromosomes 10 and 13 for sqrt(drink24). Bergen et al. identified several regions for sqrt(pakyrs) in the COGA sample, among chromosomes 2 (~10 cM) and 14 (~68 cM) [13]. Straub et al. identified several linkage regions for nicotine dependence in a sample from Christchurch, New Zealand within chromosome 2 (~150 cM, LOD = 1.5) [14]. Saccone et al. identified a susceptibility locus on chromosome 4 (~120 cM, LOD = 3.5) for drink24 [5]. In our analysis using SNP markers, we observed an increase in the LOD scores and suggestive evidence of linkage on chromosomes 1 and 4 for sqrt(drink24) that was not observed using microsatellite markers. No evidence of a pleiotropic effect was found between sqrt(pakyrs) and sqrt(drink24). Our screening test is a computationally timesaving approach that can be used to determine which regions should be analyzed using a multivariate approach. However, significant results of the screening test may be misleading because the results may be driven by only one trait rather than several traits. Thus, careful evaluation of the univariate linkage results and the screening test is necessary.
During our analyses several difficulties arose when SNPs were used in quantitative trait linkage analysis. First, the only software that could specifically handle pedigrees of large size was SIMWALK2 [8]; however, it was computationally intensive to estimate the MIBDs. Second, in order to calculate the MIBD for 350 SNPS on chromosome 1, we had to break the 350 SNPs into 10 groups of 35 SNPs and then combine the results of the linkage analyses.
Conclusion
We observed evidence of linkage on chromosome 4 for alcohol consumption using SNPs; this linked region was in the same region previously identified by Saccone et al. [5]. Furthermore, using SNPs, we also observed several suggestive regions for linkage to sqrt(pakyrs) and sqrt(drink24) not previously identified. The proposed screening test for multivariate quantitative trait linkage analysis also showed its potential application in this data. Our experience using large extended families and many SNPs suggest that software limitations are an issue when contemplating genomewide linkage scans.
Abbreviations
 COGA:

Collaborative Study on the Genetics of Alcoholism
 LD:

Linkage disequilibrium
 LRT:

Likelihood ratio test
 MIBD:

Multipoint identitybydescent
 MLE:

Maximum likelihood estimator
 MLS:

Maximum LOD score
 QTL:

Quantitative trait locus
 SNP:

Singlenucleotide polymorphism
 VC:

Variance components
 WNH:

White, nonHispanic
Declarations
Acknowledgements
This research was partially funded by NIH grants R01HL71917 and CA94919.
Authors’ Affiliations
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