Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13
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The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.Keywords:
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Abstract Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome‐wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome‐wide significant linkage ( P SR = 0.00034, P OR = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes. © 2008 Wiley‐Liss, Inc.
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A consideration of the currently established autosomal dominant linkage relationships to ophthalmologic disorders was utilized to review the principles of linkage. If cross-over does not occur, as few as 6 informative matings can be utilized to identify probable linkage, and as few as 11 informative matings can be utilized to establish linkage. The potential implications of linkage in diagnosis, prevention, and treatment of heritable disorders was discussed.
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The possible linkage groups JK-JGK, MNS-GC, GPT-ESD and GPT-HP have been analysed in families of middle-European origin. Linkage of JK-JGK and MNS-GC could be confirmed, the group GPT-ESD needs further data and for GPT-HP no evidence of linkage was revealed.
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It was shown that sz and J are closely linked and that the recombination frequency between these two genes is 7·41 ± 2·91. It was also shown that Jis not linked with markers in linkage groups land III, and that sz is not linked with a marker in linkage group II. Therefore, linkage between sz and J is a new linkage group. Since only three linkage groups have so far been established in the Syrian hamster, linkage groups I, II and III, this new linkage constitutes linkage group IV.
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Our goal was to determine the degree to which joint segregation and linkage analysis leads to increased efficiency for estimating the recombination fraction and to greater power for detecting linkage, compared to separate analyses. We concentrated on the quantitative phenotype Q2 and analyzed linkage with a tightly linked marker, a loosely linked marker, and eight unlinked markers, the latter chosen to evaluate false positive rates. We considered both nuclear-family and extended-pedigree data, using the 200 replicates of each provided to GAW participants. We found joint analysis to be consistently more efficient, with relative efficiencies for the tightly linked marker of 1.16 and 1.06 in extended pedigrees and nuclear families, respectively. These relative efficiencies translated into modest but consistent gains in power to detect linkage. Both methods appear to produce unbiased parameter estimates and similar false positive rates.
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Nineteen further polymorphic loci were typed on the DogMap reference panel. Five new linkage groups were identified. Additionally, five markers were added to earlier defined linkage groups. Three of the new linkage groups contain markers mapped earlier to specific dog chromosomes by physical mapping. These results make a further contribution to the canine genome map and provides more linkage groups physically assigned to known chromosomes.
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Abstract This unit provides an introduction to human genetic linkage analysis for cases in which the genetic model is known (parametric or lod score linkage analysis). The LINKAGE computer package is used for computer analysis because it remains a training standard. The concepts presented for the LINKAGE programs, however, translate directly to other commonly used programs including the FASTLINK and VITESSE programs. This unit includes seven examples selected to illustrate many of the key concepts involved in performing linkage analysis. In Examples 1 through 4, a large pedigree segregating an autosomal dominant disease with reduced penetrance is studied for linkage to various markers. In Example 5, the effects of misspecification of the marker allele frequency on linkage analysis are demonstrated.
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Summary Linkage between the Pi (alpha 1 ‐antitrypsin) and Gm (immunoglobulin heavy chain) loci was studied in thirty‐four families including forty‐one informative parents and 142 children. In females, the results did not provide evidence for linkage (posterior probability of non ‐linkage 0.98). In contrast, in males, there was strong evidence for linkage (peak lod 3.9 at 0 = 0.18, posterior probability of linkage 0.98). The two populations appeared to be significantly different (0.001 < P < 0.01) with respect to the heterogeneity criterion of Morton. In addition, the effect of the possession of the S allele (associated with significantly decreased serum alpha 1 ‐antitrypsin levels) was studied in fifteen informative parents and fifty‐three children of the same group. No evidence for or against linkage was found in females, but in males close linkage between Pi S and Gm was demonstrated (peak lod 7.7 at 0 = 0.05, posterior probability of linkage 0.9999). These data indicate significant linkage between Pi and Gm in males but not females and close linkage between the Pi S and Gm markers in males.
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Evidence for significant linkage in complexly inherited disorders usually necessitates independent, replicative studies. This study investigates the implications of including in the replicative studies families already used to suggest linkage in initial linkage analysis. We generated 1,000 unlinked replicates of 100 nuclear families with a complexly inherited disease but with no linkage to the markers analyzed. We then used a standard nonparametric linkage method to analyze these data. From the original 1,000 replicates of the original data set, one set was chosen as it yielded suggestive, but falsely positive, linkage results (LOD score = 3.4). Variable numbers of randomly selected families from this positive replicate (n = 100 families) were used to replace families in replicates of the original (unlinked) data set, and linkage analysis repeated. Overlap of families from the “positive data set” did increase the LOD scores for “unlinked data sets.” While a small amount of overlap (replacement) between a positive linkage result and the replication sample is unlikely in practice to alter results, our study suggests that steps should be taken to ensure that overlap is minimized. The implications of this overlapping recruitment on replicative linkage studies are discussed. © 2001 Wiley-Liss. Inc.
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