David Ballard

Absolute pitch (AP) and synesthesia are two uncommon cognitive traits that reflect increased neuronal connectivity and have been anecdotally reported to occur together in an individual. Here we systematically evaluate the occurrence of synesthesia in a population of 768 subjects with documented AP. Out of these 768 subjects, 151 (20.1%) reported synesthesia, most commonly with color. These self-reports of synesthesia were validated in a subset of 21 study subjects, using an established methodology. We further carried out combined linkage analysis of 53 multiplex families with AP and 36 multiplex families with synesthesia. We observed a peak NPL LOD = 4.68 on chromosome 6q, as well as evidence of linkage on chromosome 2, using a dominant model. These data establish the close phenotypic and genetic relationship between AP and synesthesia. The chromosome 6 linkage region contains 73 genes; several leading candidate genes involved in neurodevelopment were investigated by exon resequencing. However, further studies will be required to definitively establish the identity of the causative gene(s) in the region.

Peter Gregersen and colleagues identify a regulatory variant in CSK, coding for an intracellular kinase that physically interacts with Lyp (PTPN22), associated with systemic lupus erythematosus (SLE). Their work suggests that the Lyp-Csk complex influences susceptibility to SLE through regulation of B-cell signaling, maturation and activation. The c-Src tyrosine kinase, Csk, physically interacts with the intracellular phosphatase Lyp (encoded by PTPN22) and can modify the activation state of downstream Src kinases, such as Lyn, in lymphocytes. We identified an association of CSK with systemic lupus erythematosus (SLE) and refined its location to the intronic polymorphism rs34933034 (odds ratio (OR) = 1.32; P = 1.04 × 10−9). The risk allele at this SNP is associated with increased CSK expression and augments inhibitory phosphorylation of Lyn. In carriers of the risk allele, there is increased B-cell receptor (BCR)-mediated activation of mature B cells, as well as higher concentrations of plasma immunoglobulin M (IgM), relative to individuals with the non-risk haplotype. Moreover, the fraction of transitional B cells is doubled in the cord blood of carriers of the risk allele, due to an expansion of late transitional cells in a stage targeted by selection mechanisms. This suggests that the Lyp-Csk complex increases susceptibility to lupus at multiple maturation and activation points in B cells.

To pinpoint true positive single-nucleotide polymorphism (SNP) associations in a genome-wide association study (GWAS) of rheumatoid arthritis (RA), we categorize genetic loci by external knowledge. We test both the ‘enrichment of associated loci’ in a locus category and the ‘combined association’ of a locus category. The former is quantified by the odds ratio for the presence of SNP associations at the loci of a category, whereas the latter is quantified by the number of loci in a category that have SNP associations. These measures are compared with their expected values as obtained from the permutation of the affection status. To account for linkage disequilibrium (LD) among SNPs, we view each LD block as a genetic locus. Positional candidates were defined as loci implicated by earlier GWAS results, whereas functional candidates were defined by annotations regarding the molecular roles of genes, such as gene ontology categories. As expected, immune-related categories show the largest enrichment signal, although it is not very strong. The intersection of positional and functional candidate information predicts novel RA loci near the genes TEC/TXK, MBL2 and PIK3R1/CD180. Notably, a combined association signal is not only produced by immune-related categories, but also by most other categories and even randomly defined categories. The unspecific quality of these signals limits the possible conclusions from combined association tests. It also reduces the magnitude of enrichment test results. These unspecific signals might result from common variants of small effect and hardly concentrated in candidate categories, or an inflated size of associated regions from weak LD with infrequent mutations.

The use of biological annotation such as genes and pathways in the analysis of gene expression data has aided the identification of genes for follow-up studies and suggested functional information to uncharacterized genes. Several studies have applied similar methods to genome wide association studies and identified a number of disease related pathways. However, many questions remain on how to best approach this problem, such as whether there is a need to obtain a score to summarize association evidence at the gene level, and whether a pathway, dominated by just a few highly significant genes, is of interest. We evaluated the performance of two pathway-based methods (Random Set, and Binomial approximation to the hypergeometric test) based on their applications to three data sets of Crohn's disease. We consider both the disease status as a phenotype as well as the residuals after conditioning on IL23R, a known Crohn's related gene, as a phenotype. Our results show that Random Set method has the most power to identify disease related pathways. We confirm previously reported disease related pathways and provide evidence for IL-2 Receptor Beta Chain in T cell Activation and IL-9 signaling as Crohn's disease associated pathways. Our results highlight the need to apply powerful gene score methods prior to pathway enrichment tests, and that controlling for genes that attain genome wide significance enable further biological insight.

We propose to use the rough set theory to identify genes affecting rheumatoid arthritis risk from the data collected by the North American Rheumatoid Arthritis Consortium. For each gene, we employ generalized dynamic reducts in the rough set theory to select a subset of single-nucleotide polymorphisms (SNPs) to represent the genetic information from this gene. We then group the study subjects into different clusters based on their genotype similarity at the selected markers. Statistical association between disease status and cluster membership is then studied to identify genes associated with rheumatoid arthritis. Based on our proposed approach, we are able to identify a number of statistically significant genes associated with rheumatoid arthritis. Aside from genes on chromosome 6, our identified genes include known disease-associated genes such as PTPN22 and TRAF1. In addition, our list contains other biologically plausible genes, such as ADAM15 and AGPAT2. Our findings suggest that ADAM15 and AGPAT2 may contribute to a genetic predisposition through abnormal angiogenesis and adipose tissue.

The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study.

The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.

The joint use of information from multiple markers may be more effective to reveal association between a genomic region and a trait than single marker analysis. In this article, we compare the performance of seven multi‐marker methods. These methods include (1) single marker analysis (either the best‐scoring single nucleotide polymorphism in a candidate region or a combined test based on Fisher's method); (2) fixed effects regression models where the predictors are either the observed genotypes in the region, principal components that explain a proportion of the genetic variation, or predictors based on Fourier transformation for the genotypes; and (3) variance components analysis. In our simulation studies, we consider genetic models where the association is due to one, two, or three markers, and the disease‐causing markers have varying allele frequencies. We use information from either all the markers in a region or information only from tagging markers. Our simulation results suggest that when there is one disease‐causing variant, the best‐scoring marker method is preferred whereas the variance components method and the principal components method work well for more common disease‐causing variants. When there is more than one disease‐causing variant, the principal components method seems to perform well over all the scenarios studied. When these methods are applied to analyze associations between all the markers in or near a gene and disease status for an inflammatory bowel disease data set, the analysis based on the principal components method leads to biologically more consistent discoveries than other methods. Genet. Epidemiol. 34: 201–124, 2010.