Lack of association of the CIITA -168A→G promoter SNP with myasthenia gravis and its role in autoimmunity

<p>Abstract</p> <p>Background</p> <p>The major histocompatibility complex class II transactivator (CIITA) regulates MHC class II gene expression. A promoter SNP -168A→G (rs3087456) has previously been shown to be associated with susceptibility to several immune mediated disorders, including rheumatoid arthritis (RA), multiple sclerosis (MS) and myocardial infarction (MI). Myasthenia gravis (MG) is an autoimmune disorder which has previously been shown to be associated with polymorphisms of several autoimmune predisposing genes, including <it>IL-1</it>, <it>PTPN22</it>, <it>TNF-α </it>and the <it>MHC</it>. In order to determine if allelic variants of rs3087456 increase predisposition to MG, we analyzed this SNP in our Swedish cohort of 446 MG patients and 1866 controls.</p> <p>Results</p> <p>No significant association of the SNP with MG was detected, neither in the patient group as a whole, nor in any clinical subgroup. The vast majority of previous replication studies have also not found an association of the SNP with autoimmune disorders.</p> <p>Conclusions</p> <p>We thus conclude that previous findings with regard to the role of the <it>CIITA </it>-168A→G SNP in autoimmunity may have to be reconsidered.</p

A genome-wide association scan on estrogen receptor-negative breast cancer

Introduction: Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk.Methods: We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases.Results: Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer share

Population based allele frequencies of disease associated polymorphisms in the Personalized Medicine Research Project

<p>Abstract</p> <p>Background</p> <p>There is a lack of knowledge regarding the frequency of disease associated polymorphisms in populations and population attributable risk for many populations remains unknown. Factors that could affect the association of the allele with disease, either positively or negatively, such as race, ethnicity, and gender, may not be possible to determine without population based allele frequencies.</p> <p>Here we used a panel of 51 polymorphisms previously associated with at least one disease and determined the allele frequencies within the entire Personalized Medicine Research Project population based cohort. We compared these allele frequencies to those in dbSNP and other data sources stratified by race. Differences in allele frequencies between self reported race, region of origin, and sex were determined.</p> <p>Results</p> <p>There were 19544 individuals who self reported a single racial category, 19027 or (97.4%) self reported white Caucasian, and 11205 (57.3%) individuals were female. Of the 11,208 (57%) individuals with an identifiable region of origin 8337 or (74.4%) were German.</p> <p>41 polymorphisms were significantly different between self reported race at the 0.05 level. Stratification of our Caucasian population by self reported region of origin revealed 19 polymorphisms that were significantly different (p = 0.05) between individuals of different origins. Further stratification of the population by gender revealed few significant differences in allele frequencies between the genders.</p> <p>Conclusions</p> <p>This represents one of the largest population based allele frequency studies to date. Stratification by self reported race and region of origin revealed wide differences in allele frequencies not only by race but also by region of origin within a single racial group. We report allele frequencies for our Asian/Hmong and American Indian populations; these two minority groups are not typically selected for population allele frequency detection. Population wide allele frequencies are important for the design and implementation of studies and for determining the relevance of a disease associated polymorphism for a given population.</p

Empirical Distributions of F-ST from Large-Scale Human Polymorphism Data

Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright’s FST that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-FST may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically FST analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global FST distribution closely follows an exponential distribution. Third, although the overall FST distribution is similarly shaped (inverse J), FST distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-FST of these groups is linear in allele frequency. These results suggest that investigating the extremes of the FST distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection

Genetic Variation and Population Substructure in Outbred CD-1 Mice: Implications for Genome-Wide Association Studies

Outbred laboratory mouse populations are widely used in biomedical research. Since little is known about the degree of genetic variation present in these populations, they are not widely used for genetic studies. Commercially available outbred CD-1 mice are drawn from an extremely large breeding population that has accumulated many recombination events, which is desirable for genome-wide association studies. We therefore examined the degree of genome-wide variation within CD-1 mice to investigate their suitability for genetic studies. The CD-1 mouse genome displays patterns of linkage disequilibrium and heterogeneity similar to wild-caught mice. Population substructure and phenotypic differences were observed among CD-1 mice obtained from different breeding facilities. Differences in genetic variation among CD-1 mice from distinct facilities were similar to genetic differences detected between closely related human populations, consistent with a founder effect. This first large-scale genetic analysis of the outbred CD-1 mouse strain provides important considerations for the design and analysis of genetic studies in CD-1 mice

Integrative Approach to Pain Genetics Identifies Pain Sensitivity Loci across Diseases

Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10−10) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10−4, 1.8×10−4, and 2.2×10−4 respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates

Coexpression Network Analysis in Abdominal and Gluteal Adipose Tissue Reveals Regulatory Genetic Loci for Metabolic Syndrome and Related Phenotypes

Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (DABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10−4). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10−4); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10−4) and BMI–adjusted waist-to-hip ratio (P = 2.4×10−4). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations

Phenylketonuria screening registry as a resource for population genetic studies

Background: Neonatal screening for metabolic diseases, involving samples stored on filter paper (Guthrie spots), provides a potential resource for genetic epidemiological studies. Objective: To develop a method to make these dried blood spots available for large scale genetic epidemiology. Methods: DNA from untraceable Guthrie spots was extracted using a saponin and chelex-100 based method and preamplified by improved primer preamplification. Analyses were done on 38 samples each of fresh, 10, and 25 year old Guthrie spots and the success rate determined for PCR amplification for five amplicon lengths. Results: The method was applicable even on 25 year old samples. The success rate was 100% for 100 bp amplicons and 80% for 396 bp amplicons. Ninety four Guthrie samples were genotyped, including carriers of two different PKU mutations; all carriers were found (six R158Q, four R252W), with no false positives. Finally, 2132 anonymous samples from the Swedish PKU registry were extracted and preamplified and the allele frequencies of APOε4, PPARγ Pro12Ala, and the CCR5 32 bp deletion determined. Local variations in allele frequencies suggested subpopulation structuring. There was a significant difference (p<0.01) in regional allele frequencies for the CCR5 32 bp deletion in the Swedish population. Conclusion: Whole genome amplification makes it feasible to conduct large genetic epidemiological studies using PKU screening registries