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1.  Analysis pipeline for the epistasis search – statistical versus biological filtering 
Frontiers in Genetics  2014;5:106.
Gene–gene interactions may contribute to the genetic variation underlying complex traits but have not always been taken fully into account. Statistical analyses that consider gene–gene interaction may increase the power of detecting associations, especially for low-marginal-effect markers, and may explain in part the “missing heritability.” Detecting pair-wise and higher-order interactions genome-wide requires enormous computational power. Filtering pipelines increase the computational speed by limiting the number of tests performed. We summarize existing filtering approaches to detect epistasis, after distinguishing the purposes that lead us to search for epistasis. Statistical filtering includes quality control on the basis of single marker statistics to avoid the analysis of bad and least informative data, and limits the search space for finding interactions. Biological filtering includes targeting specific pathways, integrating various databases based on known biological and metabolic pathways, gene function ontology and protein–protein interactions. It is increasingly possible to target single-nucleotide polymorphisms that have defined functions on gene expression, though not belonging to protein-coding genes. Filtering can improve the power of an interaction association study, but also increases the chance of missing important findings.
PMCID: PMC4012196  PMID: 24817878
epistasis; genetic interaction; biological interaction; filtering pipeline; optimal search
2.  A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma 
We propose a two-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma.
First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N=281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N=74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are re-estimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis.
Using the best fitting segregation models in model-based multipoint linkage analysis, we identified two separate peaks on chromosome 17; the first agreed with a region identified by Shete et al. who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD).
Our approach has the advantage of not requiring markers to be in linkage equilibrium unless the minor allele frequency is small (markers which tend to be uninformative for linkage), and of using more of the available information for LOD-based linkage analysis.
PMCID: PMC3518573  PMID: 22962404
Glioma; model-based linkage; segregation; age of onset; prevalence constraint
3.  A Segregation Analysis of Barrett’s Esophagus and Associated Adenocarcinomas 
Familial aggregation of esophageal adenocarcinomas, esophagogastric junction adenocarcinomas, and their precursor Barrett’s esophagus has been termed Familial Barrett’s Esophagus (FBE). Numerous studies documenting increased familial risk for these diseases raise the hypothesis that there may be an inherited susceptibility to the development of BE and its associated cancers. In this study, using segregation analysis for a binary trait as implemented in S.A.G.E. 6.0.1, we analyzed data on 881singly ascertained pedigrees in order to determine whether FBE is caused by a common environmental or genetic agent and, if genetic, to identify the mode of inheritance of FBE. The inheritance models were compared by likelihood ratio tests and Akaike’s A Information Criterion. Results indicated that random environmental and/or multifactorial components were insufficient to fully explain the familial nature of FBE, but rather there is segregation of a major type transmitted from one generation to the next (p-value < 10−10). An incompletely dominant inheritance model together with a polygenic component fits the data best. For this dominant model, the estimated penetrance of the dominant allele is 0.1005 (95% confidence interval, CI: 0.0587 to 0.1667) and the sporadic rate is 0.0012 (95% CI: 0.0004 to 0.0042), corresponding to a relative risk of 82.53 (95% CI: 28.70 to 237.35), or odds ratio of 91.63 (95% CI: 32.01 to 262.29). This segregation analysis provides epidemiological evidence in support of one or more rare autosomally inherited dominant susceptibility allele(s) in FBE families, and hence motivates linkage analyses.
PMCID: PMC2838211  PMID: 20200424
familial esophageal adenocarcinomas; complex segregation analysis; dominant major gene inheritance; polygenic component; likelihood; AIC; unified model

Results 1-3 (3)