Polybrominated diphenyl ethers (PBDEs) concentrations in soil and plants around municipal dumpsites in Abuja, Nigeria.

Polybrominated diphenyl ethers (PBDEs) are listed as persistent organic pollutants (POPs) in the Stockholm Convention. It has been established that PBDEs may be released into the environment during improper handling and disposal of e-waste and other products containing PBDEs that is prevalent in developing countries. This research work assessed the status of PBDE contamination at dumpsites in Nigeria. Soil and edible plant samples were collected from the dumpsites and control sites for analysis. The concentrations of ∑7PBDE in the topsoils around the dumpsites at 0-15 cm depth ranged from 112 to 366 ng/g dry weight (dw) while that of the topsoil of the control site 500 m from the dumpsite ranged from 26.8 to 39.7 ng/g dw. These high concentrations stem likely from open burning of waste including electronic waste on the landfills. Plant samples (bentgrass, spinach, tomatoes, pumpkin and sweet potatoes) around the dumpsites were found to be contaminated by PBDEs with levels ranging from 25.0 to 60.5 ng/g dw in plant roots and from 8.45 to 32.2 ng/g dw in plant shoots for ∑7PBDE. This suggests that consumption of vegetables by humans and ingestion of contaminated soils and feed by chickens and cows can transfer PBDEs into the human food chain around the dumpsites. The comparison of PBDE levels in soils and the PBDE levels in chicken eggs from the former study indicate that PBDE levels in the soils are sufficient to explain the levels in the chicken eggs with a reasonable carry-over rate for PBDEs of 0.28 on average. The PBDE contamination in the soil was sufficient to result in a relevant exposure of humans via accumulation in eggs. The study shows that a better management of end-of-life products containing PBDEs is needed to reduce PBDE exposure risk in Africa.

Reviewing the relevance of dioxin and PCB sources for food from animal origin and the need for their inventory, control and management.

BACKGROUND: In the past, cases of PCDD/F and PCB contamination exceeding limits in food from animal origin (eggs, meat or milk) were mainly caused by industrially produced feed. But in the last decade, exceedances of EU limit values were discovered more frequently for PCDD/Fs or dioxin-like(dl)-PCBs from free range chicken, sheep, and beef, often in the absence of any known contamination source. RESULTS: The German Environment Agency initiated a project to elucidate the entry of PCBs and PCDD/Fs in food related to environmental contamination. This paper summarizes the most important findings. Food products from farm animals sensitive to dioxin/PCB exposure-suckling calves and laying hens housed outdoor-can exceed EU maximum levels at soil concentrations that have previously been considered as safe. Maximum permitted levels can already be exceeded in beef/veal when soil is contaminated around 5 ng PCB-TEQ/kg dry matter (dm). For eggs/broiler, this can occur at a concentration of PCDD/Fs in soil below 5 ng PCDD/F-PCB-TEQ/kg dm. Egg consumers-especially young children-can easily exceed health-based guidance values (TDI). The soil-chicken egg exposure pathway is probably the most sensitive route for human exposure to both dl-PCBs and PCDD/Fs from soil and needs to be considered for soil guidelines. The study also found that calves from suckler cow herds are most prone to the impacts of dl-PCB contamination due to the excretion/accumulation via milk. PCB (and PCDD/F) intake for free-range cattle stems from feed and soil. Daily dl-PCB intake for suckler cow herds must in average be less than 2 ng PCB-TEQ/day. This translates to a maximum concentration in grass of 0.2 ng PCB-TEQ/kg dm which is less than 1/6 of the current EU maximum permitted level. This review compiles sources for PCDD/Fs and PCBs relevant to environmental contamination in respect to food safety. It also includes considerations on assessment of emerging POPs. CONCLUSIONS: The major sources of PCDD/F and dl-PCB contamination of food of animal origin in Germany are (1) soils contaminated from past PCB and PCDD/F releases; (2) PCBs emitted from buildings and constructions; (3) PCBs present at farms. Impacted areas need to be assessed with respect to potential contamination of food-producing animals. Livestock management techniques can reduce exposure to PCDD/Fs and PCBs. Further research and regulatory action are needed to overcome gaps. Control and reduction measures are recommended for emission sources and new listed and emerging POPs to ensure food safety.

Life cycle of PCBs and contamination of the environment and of food products from animal origin.

This report gives a summary of the historic use, former management and current release of polychlorinated biphenyls (PCBs) in Germany and assesses the impact of the life cycle of PCBs on the contamination of the environment and of food products of animal origin. In Germany 60,000 t of PCBs were used in transformers, capacitors or as hydraulic oils. The use of PCB oils in these "closed applications", has been banned in Germany in 2000. Thirty to 50% of these PCBs were not appropriately managed. In West Germany, 24,000 t of PCBs were used in open applications, mainly as additive (plasticiser, flame retardant) in sealants and paints in buildings and other construction. The continued use in open applications has not been banned, and in 2013, an estimated more than 12,000 t of PCBs were still present in buildings and other constructions. These open PCB applications continuously emit PCBs into the environment with an estimated release of 7-12 t per year. This amount is in agreement with deposition measurements (estimated to 18 t) and emission estimates for Switzerland. The atmospheric PCB releases still have an relevant impact on vegetation and livestock feed. In addition, PCBs in open applications on farms are still a sources of contamination for farmed animals. Furthermore, the historic production, use, recycling and disposal of PCBs have contaminated soils along the lifecycle. This legacy of contaminated soils and contaminated feed, individually or collectively, can lead to exceedance of maximum levels in food products from animals. In beef and chicken, soil levels of 5 ng PCB-TEQ/kg and for chicken with high soil exposure even 2 ng PCB-TEQ/kg can lead to exceedance of EU limits in meat and eggs. Areas at and around industries having produced or used or managed PCBs, or facilities and areas where PCBs were disposed need to be assessed in respect to potential contamination of food-producing animals. For a large share of impacted land, management measures applicable on farm level might be sufficient to continue with food production. Open PCB applications need to be inventoried and better managed. Other persistent and toxic chemicals used as alternatives to PCBs, e.g. short chain chlorinated paraffins (SCCPs), should be assessed in the life cycle for exposure of food-producing animals and humans.

Biomonitoring of polychlorinated biphenyls in Bavaria/Germany-long-term observations and standardization.

In the 1980s, it was demonstrated that semi-volatile organic compounds (SVOCs) like polychlorinated biphenyls (PCBs) accumulate in plant leaves. Plants are at the base of the food chain, and therefore a starting point for transfer of PCBs to animals and related human exposure. For two decades, the Environment Agency of the German federal state of Bavaria (LfU) has been operating long-term monitoring stations to measure the impact of organic air pollutants. Standardized ryegrass, curly kale, and spruce needles are used as bioindicators for the atmospheric entries of PCBs into vegetation. From the end of 1990s to 2009, there was a marked decline in the concentrations of indicator PCBs (i-PCBs) and a minor decline in PCB-TEQ levels. After 2009, the concentrations leveled off. In rural areas, the median concentrations of Σ6 i-PCB in ryegrass and curly kale were about 3 and 4 µg/kg dm in 2000, and have been about 0.5 and 1 µg/kg dm since 2009, respectively. Concentrations in spruce needles fell from 0.9 to 0.4 µg/kg dm. Median PCB-TEQ concentrations in the bioindicator plants ranged from 0.05 to 0.23 ng/kg dm between 2002 and 2009 and from 0.15 to 0.05 ng/kg dm after 2009. Indicator PCB and PCB-TEQ concentrations were several times higher at the urban station in Munich than at the rural areas, reflecting the emissions from in-use PCB stocks in the building sector. The likely reason of the slower decrease of PCB-TEQ compared to i-PCBs is the formation of PCB-126 by dechlorination of industrial PCBs in open applications.

Single nucleotide polymorphisms in the angiogenic and lymphangiogenic pathways are associated with lymphedema caused by Wuchereria bancrofti.

BACKGROUND: Lymphedema (LE) is a chronic clinical manifestation of filarial nematode infections characterized by lymphatic dysfunction and subsequent accumulation of protein-rich fluid in the interstitial space-lymphatic filariasis. A number of studies have identified single nucleotide polymorphisms (SNPs) associated with primary and secondary LE. To assess SNPs associated with LE caused by lymphatic filariasis, a cross-sectional study of unrelated Ghanaian volunteers was designed to genotype SNPs in 285 LE patients as cases and 682 infected patients without pathology as controls. One hundred thirty-one SNPs in 64 genes were genotyped. The genes were selected based on their roles in inflammatory processes, angiogenesis/lymphangiogenesis, and cell differentiation during tumorigenesis. RESULTS: Genetic associations with nominal significance were identified for five SNPs in three genes: vascular endothelial growth factor receptor-3 (VEGFR-3) rs75614493, two SNPs in matrix metalloprotease-2 (MMP-2) rs1030868 and rs2241145, and two SNPs in carcinoembryonic antigen-related cell adhesion molecule-1 (CEACAM-1) rs8110904 and rs8111171. Pathway analysis revealed an interplay of genes in the angiogenic/lymphangiogenic pathways. Plasma levels of both MMP-2 and CEACAM-1 were significantly higher in LE cases compared to controls. Functional characterization of the associated SNPs identified genotype GG of CEACAM-1 as the variant influencing the expression of plasma concentration, a novel finding observed in this study. CONCLUSION: The SNP associations found in the MMP-2, CEACAM-1, and VEGFR-3 genes indicate that angiogenic/lymphangiogenic pathways are important in LE clinical development.

Meta-analysis identifies novel risk loci and yields systematic insights into the biology of male-pattern baldness.

Male-pattern baldness (MPB) is a common and highly heritable trait characterized by androgen-dependent, progressive hair loss from the scalp. Here, we carry out the largest GWAS meta-analysis of MPB to date, comprising 10,846 early-onset cases and 11,672 controls from eight independent cohorts. We identify 63 MPB-associated loci (P<5 × 10-8, METAL) of which 23 have not been reported previously. The 63 loci explain ∼39% of the phenotypic variance in MPB and highlight several plausible candidate genes (FGF5, IRF4, DKK2) and pathways (melatonin signalling, adipogenesis) that are likely to be implicated in the key-pathophysiological features of MPB and may represent promising targets for the development of novel therapeutic options. The data provide molecular evidence that rather than being an isolated trait, MPB shares a substantial biological basis with numerous other human phenotypes and may deserve evaluation as an early prognostic marker, for example, for prostate cancer, sudden cardiac arrest and neurodegenerative disorders.

TREM2 rare variant p.R47H is not associated with Parkinson's disease.

Variant p.R47H of triggering receptor expressed on myeloid cells 2 (TREM2) has been associated with Parkinson's disease (PD). We screened this TREM2-variant in 821 PD patients including 261 demented PD patients (PDD) and in healthy controls (n = 919). Neither the entire PD nor the small PDD sample was associated with p.R47H.

Prediction of male-pattern baldness from genotypes.

The global demand for products that effectively prevent the development of male-pattern baldness (MPB) has drastically increased. However, there is currently no established genetic model for the estimation of MPB risk. We conducted a prediction analysis using single-nucleotide polymorphisms (SNPs) identified from previous GWASs of MPB in a total of 2725 German and Dutch males. A logistic regression model considering the genotypes of 25 SNPs from 12 genomic loci demonstrates that early-onset MPB risk is predictable at an accuracy level of 0.74 when 14 SNPs were included in the model, and measured using the area under the receiver-operating characteristic curves (AUC). Considering age as an additional predictor, the model can predict normal MPB status in middle-aged and elderly individuals at a slightly lower accuracy (AUC 0.69-0.71) when 6-11 SNPs were used. A variance partitioning analysis suggests that 55.8% of early-onset MPB genetic liability can be explained by common autosomal SNPs and 23.3% by X-chromosome SNPs. For normal MPB status in elderly individuals, the proportion of explainable variance is lower (42.4% for autosomal and 9.8% for X-chromosome SNPs). The gap between GWAS findings and the variance partitioning results could be explained by a large body of common DNA variants with small effects that will likely be identified in GWAS of increased sample sizes. Although the accuracy obtained here has not reached a clinically desired level, our model was highly informative for up to 19% of Europeans, thus may assist decision making on early MPB intervention actions and in forensic investigations.

Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families.

BACKGROUND: In family-based association analysis, each family is typically ascertained from a single proband, which renders the effects of ascertainment bias heterogeneous among family members. This is contrary to case-control studies, and may introduce sample or ascertainment bias. Statistical efficiency is affected by ascertainment bias, and careful adjustment can lead to substantial improvements in statistical power. However, genetic association analysis has often been conducted using family-based designs, without addressing the fact that each proband in a family has had a great influence on the probability for each family member to be affected. METHOD: We propose a powerful and efficient statistic for genetic association analysis that considered the heterogeneity of ascertainment bias among family members, under the assumption that both prevalence and heritability of disease are available. With extensive simulation studies, we showed that the proposed method performed better than the existing methods, particularly for diseases with large heritability. RESULTS: We applied the proposed method to the genome-wide association analysis of Alzheimer's disease. Four significant associations with the proposed method were found. CONCLUSION: Our significant findings illustrated the practical importance of this new analysis method.

Novel genetic matching methods for handling population stratification in genome-wide association studies.

BACKGROUND: A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure. RESULTS: We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer's disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach. CONCLUSIONS: Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.

METAINTER: meta-analysis of multiple regression models in genome-wide association studies.

MOTIVATION: Meta-analysis of summary statistics is an essential approach to guarantee the success of genome-wide association studies (GWAS). Application of the fixed or random effects model to single-marker association tests is a standard practice. More complex methods of meta-analysis involving multiple parameters have not been used frequently, a gap that could be explained by the lack of a respective meta-analysis pipeline. Meta-analysis based on combining p-values can be applied to any association test. However, to be powerful, meta-analysis methods for high-dimensional models should incorporate additional information such as study-specific properties of parameter estimates, their effect directions, standard errors and covariance structure. RESULTS: We modified 'method for the synthesis of linear regression slopes' recently proposed in the educational sciences to the case of multiple logistic regression, and implemented it in a meta-analysis tool called METAINTER. The software handles models with an arbitrary number of parameters, and can directly be applied to analyze the results of single-SNP tests, global haplotype tests, tests for and under gene-gene or gene-environment interaction. Via simulations for two-single nucleotide polymorphisms (SNP) models we have shown that the proposed meta-analysis method has correct type I error rate. Moreover, power estimates come close to that of the joint analysis of the entire sample. We conducted a real data analysis of six GWAS of type 2 diabetes, available from dbGaP (http://www.ncbi.nlm.nih.gov/gap). For each study, a genome-wide interaction analysis of all SNP pairs was performed by logistic regression tests. The results were then meta-analyzed with METAINTER. AVAILABILITY: The software is freely available and distributed under the conditions specified on http://metainter.meb.uni-bonn.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rare variant testing of imputed data: an analysis pipeline typified.

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group ('bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of 'rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

Analysis of the progression of systolic blood pressure using imputation of missing phenotype values.

We present a genome-wide association study of a quantitative trait, "progression of systolic blood pressure in time," in which 142 unrelated individuals of the Genetic Analysis Workshop 18 real genotype data were analyzed. Information on systolic blood pressure and other phenotypic covariates was missing at certain time points for a considerable part of the sample. We observed that the dropout process causing missingness is not independent of the initial systolic blood pressure; that is, the data is not missing completely at random. However, after the adjustment for age, the impact of systolic blood pressure on dropouts was no longer significant. Therefore, we decided to impute missing phenotype values by using information from individuals with complete phenotypic data. Progression of systolic blood pressure (∆SBP/∆t) was defined based on the imputed phenotypes and analyzed in a genome-wide fashion. We also conducted an exhaustive genome-wide search for interaction between single-nucleotide polymorphisms (7.14 × 10(10) tests) under an allelic model. The suggested data imputation and the association analysis strategy proved to be valid in the sense that there was no evidence of genome-wide inflation or increased type I error in general. Furthermore, we detected 2 single-nucleotide polymorphisms (SNPs) that met the criterion for genome-wide significance (p≤5 × 10(-8)), which was also confirmed via Monte-Carlo simulation. In view of the rather small sample size, however, the results have to be followed-up in larger studies.

SUCLG2 identified as both a determinator of CSF Aß1-42 levels and an attenuator of cognitive decline in Alzheimer's disease.

Cerebrospinal fluid amyloid-beta 1-42 (Aß1-42) and phosphorylated Tau at position 181 (pTau181) are biomarkers of Alzheimer's disease (AD). We performed an analysis and meta-analysis of genome-wide association study data on Aß1-42 and pTau181 in AD dementia patients followed by independent replication. An association was found between Aß1-42 level and a single-nucleotide polymorphism in SUCLG2 (rs62256378) (P = 2.5×10(-12)). An interaction between APOE genotype and rs62256378 was detected (P = 9.5 × 10(-5)), with the strongest effect being observed in APOE-ε4 noncarriers. Clinically, rs62256378 was associated with rate of cognitive decline in AD dementia patients (P = 3.1 × 10(-3)). Functional microglia experiments showed that SUCLG2 was involved in clearance of Aß1-42.

A one-degree-of-freedom test for supra-multiplicativity of SNP effects.

Deviation from multiplicativity of genetic risk factors is biologically plausible and might explain why Genome-wide association studies (GWAS) so far could unravel only a portion of disease heritability. Still, evidence for SNP-SNP epistasis has rarely been reported, suggesting that 2-SNP models are overly simplistic. In this context, it was recently proposed that the genetic architecture of complex diseases could follow limiting pathway models. These models are defined by a critical risk allele load and imply multiple high-dimensional interactions. Here, we present a computationally efficient one-degree-of-freedom "supra-multiplicativity-test" (SMT) for SNP sets of size 2 to 500 that is designed to detect risk alleles whose joint effect is fortified when they occur together in the same individual. Via a simulation study we show that the SMT is powerful in the presence of threshold models, even when only about 30-45% of the model SNPs are available. In addition, we demonstrate that the SMT outperforms standard interaction analysis under recessive models involving just a few SNPs. We apply our test to 10 consensus Alzheimer's disease (AD) susceptibility SNPs that were previously identified by GWAS and obtain evidence for supra-multiplicativity ([Formula: see text]) that is not attributable to either two-way or three-way interaction.

Androgenetic alopecia: identification of four genetic risk loci and evidence for the contribution of WNT signaling to its etiology.

The pathogenesis of androgenetic alopecia (AGA, male-pattern baldness) is driven by androgens, and genetic predisposition is the major prerequisite. Candidate gene and genome-wide association studies have reported that single-nucleotide polymorphisms (SNPs) at eight different genomic loci are associated with AGA development. However, a significant fraction of the overall heritable risk still awaits identification. Furthermore, the understanding of the pathophysiology of AGA is incomplete, and each newly associated locus may provide novel insights into contributing biological pathways. The aim of this study was to identify unknown AGA risk loci by replicating SNPs at the 12 genomic loci that showed suggestive association (5 × 10(-8)

Quick, "imputation-free" meta-analysis with proxy-SNPs.

BACKGROUND: Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming. RESULTS: Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127. CONCLUSIONS: YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.

Six novel susceptibility Loci for early-onset androgenetic alopecia and their unexpected association with common diseases.

Androgenetic alopecia (AGA) is a highly heritable condition and the most common form of hair loss in humans. Susceptibility loci have been described on the X chromosome and chromosome 20, but these loci explain a minority of its heritable variance. We conducted a large-scale meta-analysis of seven genome-wide association studies for early-onset AGA in 12,806 individuals of European ancestry. While replicating the two AGA loci on the X chromosome and chromosome 20, six novel susceptibility loci reached genome-wide significance (p = 2.62×10⁻9-1.01×10⁻¹²). Unexpectedly, we identified a risk allele at 17q21.31 that was recently associated with Parkinson's disease (PD) at a genome-wide significant level. We then tested the association between early-onset AGA and the risk of PD in a cross-sectional analysis of 568 PD cases and 7,664 controls. Early-onset AGA cases had significantly increased odds of subsequent PD (OR = 1.28, 95% confidence interval: 1.06-1.55, p = 8.9×10⁻³). Further, the AGA susceptibility alleles at the 17q21.31 locus are on the H1 haplotype, which is under negative selection in Europeans and has been linked to decreased fertility. Combining the risk alleles of six novel and two established susceptibility loci, we created a genotype risk score and tested its association with AGA in an additional sample. Individuals in the highest risk quartile of a genotype score had an approximately six-fold increased risk of early-onset AGA [odds ratio (OR) = 5.78, p = 1.4×10⁻88]. Our results highlight unexpected associations between early-onset AGA, Parkinson's disease, and decreased fertility, providing important insights into the pathophysiology of these conditions.

Follow-up study of the first genome-wide association scan in alopecia areata: IL13 and KIAA0350 as susceptibility loci supported with genome-wide significance.

Recently, the first genome-wide association study (GWAS) of alopecia areata (AA) was conducted in a North-American sample, and this identified eight susceptibility loci surpassing genome-wide significance. The aim of the present follow-up association analysis was to confirm five of these eight loci (single-nucleotide polymorphisms (SNPs) from the CTLA4, IL-2RA, and HLA regions were not included due to previous own findings) and test 12 other loci from the GWAS, which did not surpass the threshold for genome-wide significance. Twenty-three SNPs from the 17 loci were investigated using a sample of 1,702 Central European AA patients and 1,723 controls. Of the five loci with previously reported genome-wide significance, association was confirmed for all of these: ULBP3/ULBP6, PRDX5, IL-2/IL-21, STX17, and IKZF4/ERBB3 (P-value <0.05). To detect robust evidence for association among the 12 other loci, a meta-analysis of the present association data and the data of the recent GWAS was performed. Genome-wide significant association was found for rs20541 (P(comb)=7.52 × 10(-10); odds ratio (OR)=1.30 (1.23-1.38)) and rs998592 (P(comb)=1.11 × 10(-11); OR=1.28 (1.21-1.36)), thus establishing IL-13 and KIAA0350/CLEC16A as susceptibility loci for AA. Interestingly, IL-13 and KIAA0350/CLEC16A are susceptibility loci for other autoimmune diseases, supporting the hypothesis of shared pathways of autoimmune susceptibility.

Integrated genome-wide pathway association analysis with INTERSNP.

OBJECTIVES: Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. METHODS: We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual λ inflation. RESULTS: We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher's combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher's combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas.