Abstract
BackgroundIn this paper we apply two novel quantitative trait linkage statistics based on the posterior probability of linkage (PPL) to chromosome 4 from the GAW 14 COGA dataset. Our approaches are advantageous since they use the full likelihood, use full phenotypic information, do not assume normality at the population level or require population/sample parameter estimates; and like other forms of the PPL, they are specifically tailored to accumulate linkage evidence, either for or against linkage, across multiple sets of heterogeneous data.ResultsThe first statistic uses all quantitative trait (QT) information from the pedigree (QT-posterior probability of linkage, PPL); we applied the QT-PPL to the trait ecb21 (resting electroencephalogram). The second statistic allows simultaneous incorporation of dichotomous trait data into the QT analysis via a threshold model (QTT-PPL); we applied the QTT-PPL to combined data on ecb21 and ALDX1. We obtained a QT-PPL of 96% at GABRB1 and a QT-PPL of 18% at FABP2 while the QTT-PPL was 4% and 2% at the same two loci, respectively. By comparison, the variance-components (VC) method, as implemented in SOLAR, yielded multipoint VC LOD scores of 2.05 and 2.21 at GABRB1 and FABP2, respectively; no other VC LODs were greater than 2.ConclusionThe QTT-PPL was only 4% at GABARB1, which might suggest that the underlying ecb21 gene does not also cause ALDX1, although features of the data complicate interpretation of this result.
Highlights
In this paper we apply two novel quantitative trait linkage statistics based on the posterior probability of linkage (PPL) to chromosome 4 from the GAW 14 Collaborative Study on the Genetics of Alcoholism (COGA) dataset
We have developed two new methods for quantitative trait (QT) linkage analysis based on the posterior probability of linkage (PPL) framework [1], which directly measures the probability that a disease gene is linked to a genetic marker or genomic location
These methods were applied to chromosome 4 of the Collaborative Study on the Genetics of Alcoholism (COGA) data using the quantitative ecb21 phenotype, and the dichotomous phenotype ALDX1. ecb21 was chosen because it had yielded a variance components (VC) LOD score of 5.01 near GABRB1 in an analysis using the full set of COGA families [2]
Summary
In this paper we apply two novel quantitative trait linkage statistics based on the posterior probability of linkage (PPL) to chromosome 4 from the GAW 14 COGA dataset. Our approaches are advantageous since they use the full likelihood, use full phenotypic information, do not assume normality at the population level or require population/sample parameter estimates; and like other forms of the PPL, they are tailored to accumulate linkage evidence, either for or against linkage, across multiple sets of heterogeneous data. The single-locus quantitative trait likelihood as implemented in LIPED is used for analysis, with the trait parameters (allele frequency, genotypic means, and variances) integrated out This framework has several advantages over pair-wise identity-by-descent (IBD) sharing-based methods: it is based on the full likelihood, uses full phenotypic information, is applicable to pedigrees of arbitrary size and complexity, does not assume normality at the population level or require population/sample parameter estimates; and like other forms of the PPL, it is tailored to accumulate evidence, either for or against linkage, across multiple sets of heterogeneous data. These methods were applied to chromosome 4 of the Collaborative Study on the Genetics of Alcoholism (COGA) data using the quantitative ecb phenotype, and the dichotomous phenotype ALDX1. ecb was chosen because it had yielded a variance components (VC) LOD score of 5.01 near GABRB1 in an analysis using the full set of COGA families [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
