Volume 6 Supplement 1
Genetic Analysis Workshop 14: Microsatellite and singlenucleotide polymorphism
Genetic imprinting analysis for alcoholism genes using variance components approach
 Sanjay Shete^{1}Email author and
 Robert Yu^{1}
DOI: 10.1186/147121566S1S161
© Shete and Yu; licensee BioMed Central Ltd 2005
Published: 30 December 2005
Abstract
Genomic imprinting, which is also known as the parentoforigin effect, is a mechanism that only expresses one copy of a gene pair depending upon the parental origin. Although many chromosomal regions in the human genome are likely to be imprinted, imprinting is not accounted for in the usual linkage analysis. In this study, using a variancecomponents approach with a quantitative phenotype ttthFP1, we found significant evidence of imprinting at two loci, D7S1790 and D1S1631, on chromosome 1 and chromosome 7, respectively. Our results suggest that allowing for the possibility of imprinting can increase the power to detect linkage for localizing genes for alcoholism.
Background
Genomic imprinting (also known as the parentoforigin effect) is a mechanism by which only one copy of a gene pair is expressed, and this expression is determined by the parental origin of the copy. The deregulation of imprinted genes has been implicated in a number of human diseases. Expression of imprinted genes is regulated by allelespecific epigenetic modifications of DNA and chromatin. These modifications affect central regulatory elements that control the allelespecific expression of neighboring genes. Although many chromosomal regions in the human genome are likely to be imprinted, particularly those involved in developmental disorders, imprinting is not accounted for in the usual linkage analysis [1–8]. In this summary, we analyzed the ttthFP1 (far frontal left side channel), a quantitative measure of alcohol dependence, using the families provided by a multicenter consortium of the Collaborative Study on the Genetics of Alcoholism (COGA) [9, 10].
Alcoholism is a complex disorder with involvement of genetic and environmental risk factors. Several studies have shown familial aggregation, segregation, and linkages to several regions [11]. Therefore, the purpose of our study was to evaluate the possibility of genomic imprinting in the regions that show some evidence of linkage using a recently developed method. Several regions on chromosomes 1 and 7 have been localized using parametric and nonparametric methods of linkage and association methods that do not allow for the possibility of genomic imprinting.
Methods
Variance components approach
Quantitative variation in a trait often occurs because of the underlying variation in genetic factors. We recently developed a method to analyze quantitative traits using the variance components approach and allowing for imprinting as described by Shete and Amos [3] and Shete et al. [4]. Let X_{i} be the phenotypic value for the i^{th} individual in a pedigree:
where μ is the overall mean, g_{ i }is the majorgene effect, G_{ i }is the polygenic effect, β_{k} values are covariate effects that are assumed to be uncorrelated with genetic and environmental factors, and e_{ i }is the environmental effect. The major gene effect has a mean value of a when individual's genotype is BB, d_{ 1 }when the genotype is Bb, d_{ 2 }when the genotype bB, and a when the genotype bb. Here, we assumed that the first allele is derived from the father and the second allele is derived from the mother. Let d be the dominance effect and I be the imprinting effect. Then, d = (d_{1} + d_{2})/2 and I = (d_{1}  d_{2})/2. When d_{1} = d_{2}, there is no imprinting. Shete and Amos [3] decomposed genetic variance at this locus into three parts: an additive component due to the paternally derived allele, σ^{2}_{af}; an additive component due to the maternally derived allele, σ^{2}_{am}; and the usual dominance component, σ^{2}_{d}. These parentspecific additive components are: where p and q are the frequencies of alleles B and b, respectively. Also, σ^{2}_{af}+σ^{2}_{am} = σ^{2}_{a}.
When the imprinting coefficient I = 0, σ^{2}_{af} and σ^{2}_{am} are equal to σ^{2}_{a}/2; and, when σ^{2}_{af} and σ^{2}_{am} are equal, I = 0. Hence, Shete and Amos [3] proposed that a test for the equality of these two parentspecific additive variances is a test for imprinting. In an extended pedigree, one must consider an allele that is shared IBD (identical by descent) by a pair of relatives in which one of the relatives received the copy from his/her father and the other received the copy from his/her mother. So, we define "parentspecific IBD sharing between a pair of relatives i and j" asfollows:
We define π_{mf,ij} and π_{mm,ij} similarly. Then, the phenotypic covariance is given by [4]
From the above equation, it can be seen that the coefficients of π_{ff,ij},π_{mm,ij}, and (π_{fm,ij}+ π_{mf,ij}) are equal if and only if σ^{2}_{af} and σ^{2}_{am} are equal, and σ^{2}_{af} and σ^{2}_{am} are equal if and only if the imprinting parameter I = 0 (i.e., there is no parental imprinting). Hence, the likelihood ratio test (LRT) for equality of these coefficients is a valid test for the null hypothesis of no imprinting. We do not estimate the parameters p, q, or I separately in the above equation, rather we estimate three parameters σ^{2}_{af}, σ^{2}_{am}, and (σ^{2}_{ a }/2  2pqI^{2}). Ordinarily, in a genome scan, one will test the joint null hypotheses of no linkage and no imprinting by testing σ^{2}_{af} = σ^{2}_{am} = 0.
Distribution of the LRT
The asymptotic distribution of the LRT is complex. For testing linkage without imprinting the LRT test is assumed to be a halfandhalf mixture of χ^{2} random variable with one and zero degrees of freedom. For joint testing of linkage and imprinting, we now have three parameters in the model. The two parameters σ^{2}_{af} and σ^{2}_{am} are independent; however, the third parameter (σ^{2}_{ a }/2  2pqI^{2}) is correlated with the first two parameters [4]. Because this parameter was used in our model, we used a mixture of χ^{2} distribution with 0, 1, 2, and 3 degrees of freedom with mixing parameters of 1/8, 3/8, 3/8, and 1/8 for joint testing of linkage and imprinting following the same rationale as in the standard linkage analyses using the approach of Self and Liang [12]. Similarly, for testing the linkage model without imprinting against the linkage model allowing for imprinting we used a mixture of χ^{2} distribution with 0, 1, and 2 degrees of freedom with mixing parameters of 4/8, 3/8, and 1/8. These asymptotic distributions can be used to obtain pointwise significance of the LRT test for testing linkage and/or imprinting.
Multipoint parentspecific IBD
Computation of multipoint parentspecific IBD is described by Shete et al. [4]. There are fouralleles at a singlelocus for the relativepair i and j. The two alleles for individual i are denoted by a vector (i_{ m },i_{ f }), where i_{ m }and i_{ f }are maternal and paternal alleles, respectively. Similarly, we define the vector(j_{ m }, j_{ f }) for individual j. There are 15 possible ordered states of IBD between these two individuals [13]. Of these15 states, only 7 are essential for computation of IBD sharing in outbred populations. Using the notations of SIMWALK2 [14, 15], we define probabilities of these states as S_{9} = (i_{ m },j_{ m })(i_{ f },j_{ f }), S_{10} = (i_{ m },j_{ m })(i_{ f })(j_{ f }), S_{11} = (i_{ m })(i_{ f },j_{ f })(j_{ m }),S_{12} = (i_{ m },j_{ f })(i_{ f },j_{ m }), S_{13} = (i_{ m },j_{ f })(i_{ f })(j_{ m }), S_{14} = (i_{ m })(i_{ f },j_{ m })(j_{ f }), and S_{15} = (i_{ m })(i_{ f })(j_{ m })(j_{ f }). In these states, the pairs of alleles inside the parentheses are IBD. We used SIMWALK2 to obtain these 15 detailed states of identity sharing.
Data
Results and Discussion
For each of the markers listed, we calculated multipoint parentspecific IBDs using the methods described. On chromosome 7, for the marker D7S1790, we obtained a negative log likelihood value of 140.35 for the model in which the major gene variance component was fixed to zero, the model without linkage. The same value under the linkage without imprinting model was found to be 135.84. Using the LRT discussed above, we obtained a suggestive significant pvalue of 0.00129 for linkage. Furthermore, we obtained a negative log likelihood value of 129.95 using the variance components approach that tests for linkage allowing for imprinting. Using the LRT, the significance for joint testing of linkage and imprinting at this marker is 0.00003. When we compared the log likelihoods with linkage but no imprinting model with joint linkage and imprinting model, we obtain a pvalue of 0.00057, which is significant evidence of imprinting. The evidence of imprinting is also evident from the lower pvalue obtained using the imprinting model.
Significance of likelihood ratio test for linkage and imprinting.
Marker  L(unlinked)  L(linked)  L(linkage and imprinting)  pValue (linkage)  pValue (linkage and imprinting)  pValue (imprinting) 

D7S1790  140.38  135.84  129.95  0.00129  0.00003  0.00057 
D1S1631  140.38  136.90  131.90  0.00417  0.00018  0.00143 
D1S532  140.38  137.35  134.66  0.00700  0.00270  0.01612 
Conclusion
Imprinting is not accounted for traditional linkage analyses. We found evidence of imprinting, even allowing for the multiple testing, on two loci. It may also be important to allow for other covariates of environmental exposures, such as smoking, in the model. In addition, the asymptotic distribution that we used may not be very accurate and recommend simulationbased pvalues at the significant loci to confirm evidence of linkage. In conclusion, our results suggest that allowing for imprinting in the linkage analyses can increase the power to detect genes responsible for the alcoholism.
Abbreviations
 COGA:

Collaborative Study on the Genetics of Alcoholism
 LRT:

Likelihood ratio test
 IBD:

Identity by descent
Declarations
Authors’ Affiliations
References
 Strauch K, Fimmers R, Kurz T, Deichmann KA, Wienker TF, Baur MP: Parametric and nonparametric multipoint linkage analysis with imprinting and twolocustrait models: application to mite sensitization. Am J Hum Genet. 2000, 66: 19451957. 10.1086/302911.PubMed CentralView ArticlePubMedGoogle Scholar
 Hanson RL, Kobes S, Lindsay RS, Knowler WC: Assessment of parentoforigin effects in linkage analysis of quantitative traits. Am J Hum Genet. 2001, 68: 951962. 10.1086/319508.PubMed CentralView ArticlePubMedGoogle Scholar
 Shete S, Amos CI: Testing for genetic linkage in families by a variancecomponents approach in the presence of genomic imprinting. Am J Hum Genet. 2002, 70: 751757. 10.1086/338931.PubMed CentralView ArticlePubMedGoogle Scholar
 Shete S, Zhou X, Amos CI: Genomic imprinting and linkage test for quantitativetrait loci in extended pedigrees. Am J Hum Genet. 2003, 73: 933938. 10.1086/378592.PubMed CentralView ArticlePubMedGoogle Scholar
 Wu CC, Shete S, Amos CI: Linkage analysis of affected sib pairs allowing for parentoforigin effects. Ann Hum Genet. 2005, 69: 113126. 10.1046/j.15298817.2004.00139.x.View ArticlePubMedGoogle Scholar
 Knapp M, Strauch K: Affectedsibpair test for linkage based on constraints for identicalbydescent distributions corresponding to disease models with imprinting. Genet Epidemiol. 2004, 26: 273285. 10.1002/gepi.10320.View ArticlePubMedGoogle Scholar
 Mukhopadhyay N, Weeks DE: Linkage analysis of adult height with parentoforigin effects in the Framingham Heart Study. BMC Genet. 2003, 4 (Suppl 1): S7610.1186/147121564S1S76.PubMed CentralView ArticlePubMedGoogle Scholar
 Shete S, Zhou X: Parametric approach to genomic imprinting analysis with applications to Angelman's syndrome. Hum Hered. 2005, 59: 2633. 10.1159/000084734.View ArticlePubMedGoogle Scholar
 Reich T: A genomic survey of alcohol dependence and related phenotypes: results from the Collaborative Study on the Genetics of Alcoholism (COGA). Alcohol Clin Exp Res. 1996, 20: 133A137A. 10.1111/j.15300277.1996.tb01763.x.View ArticlePubMedGoogle Scholar
 Begleiter H, Reich T, Nurnberger J Jr, Li TK, Conneally PM, Edenberg H, Crowe R, Kuperman S, Schuckit M, Bloom F, Hesselbrock V, Porjesz B, Cloninger CR, Rice J, Goate A: Description of the Genetic Analysis Workshop 11 Collaborative Study on the Genetics of Alcoholism. Genet Epidemiol. 1999, 17 (Suppl 1): S25S30.View ArticlePubMedGoogle Scholar
 Almasy L, Borecki IB: Exploring genetic analysis of complex traits through the paradigm of alcohol dependence: summary of GAW11 contributions. Genet Epidemiol. 1999, 17 (Suppl 1): S124.View ArticlePubMedGoogle Scholar
 Self SG, Liang KY: Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J Am Stat Assoc. 1987, 82: 605610. 10.2307/2289471.View ArticleGoogle Scholar
 Lynch M, Walsh B: Genetics and Analysis of Quantitative Traits. 1997, Sunderland: Sinauer AssociatesGoogle Scholar
 Weeks DE, Sobel E, O'Connell JR, Lange K: Computer programs for multilocus haplotyping of general pedigrees. Am J Hum Genet. 1995, 56: 15061507.PubMed CentralPubMedGoogle Scholar
 Sobel E, Lange K: Descent graphs in pedigree analysis: applications to haplotyping, location scores, and markersharing statistics. Am J Hum Genet. 1996, 58: 13231337.PubMed CentralPubMedGoogle Scholar
 Shete S, Beasley TM, Etzel CJ, Fernandez JR, Chen J, Allison DB, Amos CI: Effect of winsorization on power and type 1 error of variance components and related methods of QTL detection. Behav Genet. 2004, 34: 153159. 10.1023/B:BEGE.0000013729.26354.da.View ArticlePubMedGoogle Scholar
 Etzel CJ, Shete S, Beasley TM, Fernandez JR, Allison DB, Amos CI: Effect of BoxCox transformation on power of HasemanElston and maximumlikelihood variance components tests to detect quantitative trait loci. Hum Hered. 2003, 55: 108116. 10.1159/000072315.View ArticlePubMedGoogle Scholar
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