Correlation of age-of-onset of Atopic Dermatitis with Filaggrin loss-of-function variant status.

The genetic background of Atopic Dermatitis (AD) with chronic pruritus is complex. Filaggrin (FLG) is an essential gene in the epidermal barrier formation s. Loss-of-function (LOF) variants in FLG associated with skin barrier dysfunction constitute the most well-known genetic risk factor for AD. In this study, we focused on the frequency and effect of FLG loss-of-function variants in association with self-reported age-of-onset of AD. The dataset consisted of 386 whole-genome sequencing (WGS) samples. We observe a significant association between FLG LOF status and age-of-onset, with earlier age of onset of AD observed in the FLG LOF carrier group (p-value 0.0003, Wilcoxon two-sample test). We first tested this on the two most prevalent FLG variants. Interestingly, the effect is even stronger when considering all detected FLG LOF variants. Having two or more FLG LOF variants associates with the onset of AD at 2 years of age. In this study, we have shown enrichment of rare variants in the EDC region in cases compared with controls. Age-of-onset analysis shows not only the effect of the FLG and likely EDC variants in terms of the heightened risk of AD, but foremost enables to predict early-onset, lending further credence to the penetrance and causative effect of the identified variants. Understanding the genetic background and risk of early-onset is suggestive of skin barrier dysfunction etiology of AD with chronic pruritus.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes.

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci.

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

The effect of phenotype variation on detection of linkage in the COGA data.

Error in phenotypic measurement can significantly compromise ability to detect linkage. We assessed the impact of introducing phenotypic measurement error on our ability to detect a quantitative trait locus in the Collaborative Study on the Genetics of Alcoholism (COGA) data. The impact of introducing three different types of errors was evaluated: 1) errors generated by sampling from a normal distribution; 2) errors generated by permuting phenotype values between subjects; and 3) errors generated by sampling from a uniform error distribution.

Identifying influential individuals in linkage analysis: application to a quantitative trait locus detected in the COGA data.

Once linkage is detected to a quantitative trait locus (QTL), the next step towards localizing the gene involved may be to identify those families, or individuals, in whom the putative mutations are segregating. In this paper, we describe a jackknife procedure for identifying individuals (and families) who contribute disproportionately to the linkage. Following initial detection of linkage to a QTL, the strategy involves sequentially removing each individual (or each family) from the analysis and recomputing the lod score associated with the linked region using data from all remaining subjects (or families). This procedure can be used to determine if particular observations have substantial impact on evidence for linkage. Identification of such observations may provide insights for further efforts to localize the QTL.

Application of an ordered subset analysis approach to the genetics of alcoholism.

For complex diseases, underlying etiologic heterogeneity may reduce power to detect linkage. Thus, methods to identify more homogeneous subgroups within a given sample in a linkage study may improve detection of putative susceptibility loci. In this study we describe an ordered subsetting approach that utilizes disease-related quantitative trait data to complement traditional linkage analysis. This approach uses family-based lod scores derived from the initial genome screen and a family-based descriptor of the trait of interest. The goal of the approach is to identify more homogeneous subgroups of the data by ranking families based on their quantitative trait data. Permutation testing is used to assess statistical significance. This approach can be adapted to a variety of linkage methods and may provide a means to dissect some of the underlying heterogeneity in complex disease genetics.

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis.

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 x 10(-9)). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 x 10(-8)). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases.

Type 2 diabetes: evidence for linkage on chromosome 20 in 716 Finnish affected sib pairs.

We are conducting a genome scan at an average resolution of 10 centimorgans (cM) for type 2 diabetes susceptibility genes in 716 affected sib pairs from 477 Finnish families. To date, our best evidence for linkage is on chromosome 20 with potentially separable peaks located on both the long and short arms. The unweighted multipoint maximum logarithm of odds score (MLS) was 3.08 on 20p (location, chi = 19.5 cM) under an additive model, whereas the weighted MLS was 2.06 on 20q (chi = 57 cM, recurrence risk,lambda(s) = 1. 25, P = 0.009). Weighted logarithm of odds scores of 2.00 (chi = 69.5 cM, P = 0.010) and 1.92 (chi = 18.5 cM, P = 0.013) were also observed. Ordered subset analyses based on sibships with extreme mean values of diabetes-related quantitative traits yielded sets of families who contributed disproportionately to the peaks. Two-hour glucose levels in offspring of diabetic individuals gave a MLS of 2. 12 (P = 0.0018) at 9.5 cM. Evidence from this and other studies suggests at least two diabetes-susceptibility genes on chromosome 20. We have also screened the gene for maturity-onset diabetes of the young 1, hepatic nuclear factor 4-a (HNF-4alpha) in 64 affected sibships with evidence for high chromosomal sharing at its location on chromosome 20q. We found no evidence that sequence changes in this gene accounted for the linkage results we observed.

Power of variance component linkage analysis to detect epistasis.

Variance component methods are now being used in linkage analysis to detect genes influencing complex diseases. These methods are easily extended to allow for simultaneous estimation of both the additive effects of multiple loci on phenotypic variation (conditional oligogenic analysis) and the additive interaction (epistatic) effects among loci. We performed linkage analyses on 200 of the simulated replicates in order to evaluate the power to detect the main effects of MG1 and MG2 on Q1 as well as their interaction effects. The power to detect the main effect of MG1 was moderately good, although the power to detect MG2 and the MG1 x MG2 interaction was poor.