Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6–7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160–167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287–294 cM, including D2), chromosome 5 (p-value < 0.001 for 0–7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7–14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage

Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including 'Sphingoid metabolic process,' 'Ubiquitin protein ligase activity,' 'Regulation of cytokine secretion,' and 'Ceramide metabolic process.' These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data. © 2013 Wang et al

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

An ordered subset approach to including covariates in the transmission disequilibrium test

Clinical heterogeneity of a disease may reflect an underlying genetic heterogeneity, which may hinder the detection of trait loci. Consequently, many statistical methods have been developed that allow for the detection of linkage and/or association signals in the presence of heterogeneity

Human Telomere Length Correlates to the Size of the Associated Chromosome Arm

The majority of human telomere length studies have focused on the overall length of telomeres within a cell. In fact, very few studies have examined telomere length for individual chromosome arms. The objective of this study was to examine the relationship between chromosome arm size and the relative length of the associated telomere. Quantitative Fluorescence In Situ Hybridization (Q-FISH) was used to measure the relative telomere length of each chromosome arm in metaphases from cultured lymphocytes of 17 individuals. A statistically significant positive correlation (r = 0.6) was found between telomere length and the size of the associated chromosome arm, which was estimated based on megabase pair measurements from http://www.ncbi.nlm.nih.gov/projects/mapview/

Cryptic Subtelomeric Rearrangements and X Chromosome Mosaicism: A Study of 565 Apparently Normal Individuals with Fluorescent In Situ Hybridization

Five percent of patients with unexplained mental retardation have been attributed to cryptic unbalanced subtelomeric rearrangements. Half of these affected individuals have inherited the rearrangement from a parent who is a carrier for a balanced translocation. However, the frequency of carriers for cryptic balanced translocations is unknown. To determine this frequency, 565 phenotypically normal unrelated individuals were examined for balanced subtelomeric rearrangements using Fluorescent In Situ hybridization (FISH) probes for all subtelomere regions. While no balanced subtelomeric rearrangements were identified, three females in this study were determined to be mosaic for the X chromosome. Mosaicism for XXX cell lines were observed in the lymphocyte cultures of 3 in 379 women (0.8%), which is a higher frequency than the 1 in 1000 (0.1%) reported for sex chromosome aneuploidies. Our findings suggest that numerical abnormalities of the X chromosome are more common in females than previously reported. Based on a review of the literature, the incidence of cryptic translocation carriers is estimated to be approximately 1/8,000, more than ten-fold higher than the frequency of visible reciprocal translocations

Heritability of face shape in twins: a preliminary study using 3D stereophotogrammetry and geometric morphometrics

Introduction: Previous research suggests that aspects of facial surface morphology are heritable.  Traditionally, heritability studies have used a limited set of linear distances to quantify facial morphology and often employ statistical methods poorly designed to deal with biological shape.  In this preliminary report, we use a combination of 3D photogrammetry and landmark-based morphometrics to explore which aspects of face shape show the strongest evidence of heritability in a sample of twins. Methods: 3D surface images were obtained from 21 twin pairs (10 monozygotic, 11 same-sex dizygotic).  Thirteen 3D landmarks were collected from each facial surface and their coordinates subjected to geometric morphometric analysis.  This involved superimposing the individual landmark configurations and then subjecting the resulting shape coordinates to a principal components analysis.  The resulting PC scores were then used to calculate rough narrow-sense heritability estimates. Results: Three principal components displayed evidence of moderate to high heritability and were associated with variation in the breadth of orbital and nasal structures, upper lip height and projection, and the vertical and forward projection of the root of the nose due to variation in the position of nasion. Conclusions: Aspects of facial shape, primarily related to variation in length and breadth of central midfacial structures, were shown to demonstrate evidence of strong heritability. An improved understanding of which facial features are under strong genetic control is an important step in the identification of specific genes that underlie normal facial variation

SNPs associated with testosterone levels influence human facial morphology

Many factors influence human facial morphology, including genetics, age, nutrition, biomechanical forces, and endocrine factors. Moreover, facial features clearly differ between males and females, and these differences are driven primarily by the influence of sex hormones during growth and development. Specific genetic variants are known to influence circulating sex hormone levels in humans, which we hypothesize, in turn, affect facial features. In this study, we investigated the effects of testosterone-related genetic variants on facial morphology. We tested 32 genetic variants across 22 candidate genes related to levels of testosterone, sex hormone-binding globulin (SHGB) and dehydroepiandrosterone sulfate (DHEAS) in three cohorts of healthy individuals for which 3D facial surface images were available (Pittsburgh 3DFN, Penn State and ALSPAC cohorts; total n = 7418). Facial shape was described using a recently developed extension of the dense-surface correspondence approach, in which the 3D facial surface was partitioned into a set of 63 hierarchically organized modules. Each variant was tested against each of the facial surface modules in a multivariate genetic association-testing framework and meta-analyzed. Additionally, the association between these candidate SNPs and five facial ratios was investigated in the Pittsburgh 3DFN cohort. Two significant associations involving intronic variants of SHBG were found: both rs12150660 (p = 1.07E-07) and rs1799941 (p = 6.15E-06) showed an effect on mandible shape. Rs8023580 (an intronic variant of NR2F2-AS1) showed an association with the total and upper facial width to height ratios (p = 9.61E-04 and p = 7.35E-04, respectively). These results indicate that testosterone-related genetic variants affect normal-range facial morphology, and in particular, facial features known to exhibit strong sexual dimorphism in humans