Summary of Genetic Analysis Workshop 15: Group 9 linkage analysis of the CEPH expression data

Abstract

Group 9 participants carried out linkage analysis of the Centre d'Etude de Polymorphism Humain (CEPH) expression data, using strategies that ranged from focused investigation of a small number of traits to full genome scans of all available traits. Results from five key areas encompass the most important results within and across the 17 participating groups. First, both extensive genetic heterogeneity and poor predictability of mapping results based on heritability have key implications for study design. Second, choice of the map used for linkage analysis is influential, with the implication that meiotic maps are preferable to physical maps. Third, performance of different analytic methods was in general fairly consistent, with the exception of one variance-component method that uses marker allele sharing as the dependent rather than independent variable. Fourth, multivariate analysis approaches did not generally appear to provide advantages over univariate approaches for linkage detection. Finally, there were computational and analytic challenges in working with a large public data set, along with need for more data documentation.

Identification of Genetic Loci Underlying the Phenotypic Constructs of Autism Spectrum Disorders

“…For example, the compulsion/restricted interests factor accounted for the smallest proportion of the total common variance (3%) but had the highest heritability (65%) (Table 2). In addition, as has been reported before, 34 heritability estimates are also not reliable predictors of the results of linkage analysis, e.g. the most significant linkage signal in this study was found for the repetitive sensory-motor behaviour factor (with a heritability estimate of 0.54) rather than the compulsion/restricted interests factor which had the highest heritability estimate (0.65).…”

Genome Scan of a Nonword Repetition Phenotype in Families with Dyslexia: Evidence for Multiple Loci

“…In addition, recent studies using very large numbers of traits in a single sample indicate that multivariate approaches do not appear to provide advantages for linkage detection (Wijsman et al 2007). A situation in which a multivariate analysis might be warranted, namely evidence for linkage for multiple highly correlated traits at the same location, does not apply to our analyses of NWR because the peaks reported here do not coincide with linkage peaks we have reported for reading measures, and NWR is only moderately correlated with reading measures in our sample.…”

Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15

“…For example, Group 9 performed linkage analyses using CEPH data, which have been analyzed extensively [Morley et al, 2004;Murray et al, 2004]. However, nearly all Group 9 participants who evaluated the data for Mendelian inconsistencies identified genotyping errors on this ''cleaned'' dataset [Wijsman et al, 2007]. While genotyping errors are easier to detect in family-based studies, genotyping errors should also be minimized in population-based studies.…”