Genome‐wide association studies identify susceptibility loci affecting respiratory disease in Chinese Erhualian pigs under natural conditions

Prevalence of swine respiratory disease causes poor growth performance in and serious economic losses to the swine industry. In this study, a categorical trait of enzootic pneumonia‐like (EPL) score representing the infection gradient of a respiratory disease, more likely enzootic pneumonia, was recorded in a herd of 332 Chinese Erhualian pigs. According to their EPL scores and the disease effect on weight gains, these pigs were grouped into controls (EPL score ≤ 1) and cases (EPL score > 1). The weight gain of the case group reduced significantly at days 180, 210, 240 and 300 as compared to the control group. The heritability of EPL score was estimated to be 0.24 based on the pedigree information using a linear mixed model. All 332 Erhualian pigs and their nine sire parents were genotyped with Illumina Porcine 60K SNP chips. Two genome‐wide association studies were performed under a generalized linear mixed model and a case–control model respectively. In total, five loci surpassed the suggestive significance level (P = 2.98 × 10^?5) on chromosomes 2, 8, 12 and 14. CXCL6, CXCL8, KIT and CTBP2 were highlighted as candidate genes that might play important roles in determining resistance/susceptibility to swine EP‐like respiratory disease. The findings advance understanding of the genetic basis of resistance/susceptibility to respiratory disease in pigs.     

复杂疾病的遗传易感基因区域的精细定位

以单核苷酸多态性(Single-nucleotide polymorphism, SNP)为遗传标记, 采用全基因组关联研究(Genome-wide association studies, GWAS)的策略, 已经在660多种疾病(或性状)中发现了3800多个遗传易感基因区域。但是, 其中最显著关联的遗传变异或致病性的遗传变异位点及其生物学功能并不完全清楚。这些位点的鉴定有助于阐明复杂疾病的生物学机制, 以及发现新的疾病标记物。后GWAS时代的主要任务之一就是通过精细定位研究找到复杂疾病易感基因区域内最显著关联的易感位点或致病性的易感位点并阐明其生物学功能。针对常见变异, 可通过推断或重测序增加SNP密度, 寻找最显著关联的SNP位点, 并通过功能元件分析、表达数量性状位点(Expression quantitative trait locus, eQTL)分析和单体型分析等方法寻找功能性的SNP位点和易感基因。针对罕见变异, 则可采用重测序、罕见单体型分析、家系分析和负荷检验等方法进行精细定位。文章对这些策略和所面临的问题进行了综述。     

A genome-wide association analysis for susceptibility of pigs to enterotoxigenic Escherichia coli F41

Enterotoxigenic Escherichia coli (ETEC) is a type of pathogenic bacteria that cause diarrhea in piglets through colonizing pig small intestine epithelial cells by their surface fimbriae. Different fimbriae type of ETEC including F4, F18, K99 and F41 have been isolated from diarrheal pigs. In this study, we performed a genome-wide association study to map the loci associated with the susceptibility of pigs to ETEC F41 using 39454 single nucleotide polymorphisms (SNPs) in 667 F₂ pigs from a White Duroc×Erhualian F₂ cross. The most significant SNP (ALGA0022658, P=5.59×10⁻¹³) located at 6.95 Mb on chromosome 4. ALGA0022658 was in high linkage disequilibrium (r²>0.5) with surrounding SNPs that span a 1.21 Mb interval. Within this 1.21 Mb region, we investigated ZFAT as a positional candidate gene. We re-sequenced cDNA of ZFAT in four pigs with different susceptibility phenotypes, and identified seven coding variants. We genotyped these seven variants in 287 unrelated pigs from 15 diverse breeds that were measured with ETEC F41 susceptibility phenotype. Five variants showed nominal significant association (P<0.05) with ETEC F41 susceptibility phenotype in International commercial pigs. This study provided refined region associated with susceptibility of pigs to ETEC F41 than that reported previously. Further works are needed to uncover the underlying causal mutation(s).     

A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing

Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distribution for discrete multivariate SNP data. We propose a composite likelihood approach for parameter estimation. A direct application of this latent variable model is to association testing with multiple SNPs in a candidate gene or region. In contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete SNP data, we can exclusively focus on testing the mean and covariance parameters of the latent Gaussian distributions for cases and controls. Our simulation results demonstrate potential power gains of the proposed approach over some existing methods.     

Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci

Psoriasis (PS) and Crohn disease (CD) have been shown to be epidemiologically, pathologically, and therapeutically connected, but little is known about their shared genetic causes. We performed meta-analyses of five published genome-wide association studies on PS (2,529 cases and 4,955 controls) and CD (2,142 cases and 5,505 controls), followed up 20 loci that showed strongest evidence for shared disease association and, furthermore, tested cross-disease associations for previously reported PS and CD risk alleles in additional 6,115 PS cases, 4,073 CD cases, and 10,100 controls. We identified seven susceptibility loci outside the human leukocyte antigen region (9p24 near JAK2, 10q22 at ZMIZ1, 11q13 near PRDX5, 16p13 near SOCS1, 17q21 at STAT3, 19p13 near FUT2, and 22q11 at YDJC) shared between PS and CD with genome-wide significance (p < 5 × 10⁻⁸) and confirmed four already established PS and CD risk loci (IL23R, IL12B, REL, and TYK2). Three of the shared loci are also genome-wide significantly associated with PS alone (10q22 at ZMIZ1, p_rs1250544 = 3.53 × 10⁻⁸, 11q13 near PRDX5, p_rs694739 = 3.71 × 10⁻⁰⁹, 22q11 at YDJC, p_rs181359 = 8.02 × 10⁻¹⁰). In addition, we identified one susceptibility locus for CD (16p13 near SOCS1, p_rs4780355 = 4.99 × 10⁻⁸). Refinement of association signals identified shared genome-wide significant associations for exonic SNPs at 10q22 (ZMIZ1) and in silico expression quantitative trait locus analyses revealed that the associations at ZMIZ1 and near SOCS1 have a potential functional effect on gene expression. Our results show the usefulness of joint analyses of clinically distinct immune-mediated diseases and enlarge the map of shared genetic risk loci.     

Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.     

Meta-analysis of genetic association of chromosome 9p21 with early-onset coronary artery disease

Purpose

A number of studies reported on associations of single nucleotide polymorphisms (SNPs) present in chromosome 9p21 with early-onset coronary artery disease (CAD). The present study was then undertaken to perform a meta-analysis of all the results published to date.

Methods

All studies of the 9p21 association with early-onset CAD that were published between 2007 and 2012 were retrieved from the PubMed database. RevMan 5.0 software was used to perform meta-analysis of the data that fulfilled the criteria for our meta-analysis. The effect size of four SNPs in the 9p21 region on early-onset CAD risk was assessed based on the odds ratios (ORs) with calculation of 95% confidence interval (CI).

Results

A total of 7123 subjects from 7 case–control studies were genotyped. Meta-analysis demonstrated disease association for rs2383207 (OR = 0.79, 95% CI 0.71–0.88, P < 0.0001), rs2383206 (OR = 1.17, 95% CI 1.10–1.25, P < 0.00001), rs10757278 (OR = 1.28, 95% CI 1.15–1.42, P < 0.00001), and rs10757274 (OR = 1.17, 95% CI 1.08–1.33, P = 0.02).

Conclusion

Genetic variation in the chromosome 9p21 region may contribute to the etiology of early-onset CAD although their effect size is rather small.     

中国汉族高原肺水肿易感基因的全基因组关联研究

高原肺水肿(High-altitude pulmonary edema, HAPE)是一种特发于高原低氧环境的肺水肿, 是遗传和环境因素共同作用的结果。为了寻找与中国汉族高原肺水肿相关的单核苷酸多态性(Single nucleotide polymorphism, SNP)位点及易感基因, 文章利用Affymetrix SNP Array 6.0芯片, 对2010年5月至2012年7月在青海省玉树地区执行援建任务时来自平原地区的40例HAPE患者和33例健康对照进行全基因组SNP分型, 通过PLINK软件对芯片结果进行全基因组关联分析(Genome-wide association study, GWAS), 筛选出在病例组和对照组中间有显著差异(P < 10E-7)的SNP位点57个, 通过对57个SNP位点附近74个基因进行GO与Pathway富集分析, 发现这些基因与“前列腺素代谢”、“四烯酸代谢”、“氮代谢”显著相关(adjust P < 0.05), 以上代谢过程与HAPE病理生理机制相关。结果表明, 高原肺水肿受遗传多态性影响, 与多个基因以及位点相关。     

Coeliac disease patients carry conserved HLA-DR3-DQ2 haplotypes revealed by association of TNF alleles

Certain HLA-DQ alleles are known to contribute to predisposition to coeliac disease (CD). The existence of additional independent risk-modifying loci in the HLA complex is still being debated. The DR3-DQ2 haplotype has been studied most, but the evidence is conflicting. The discrepancies may stem from the absence of such an effect, insufficient statistical power to detect an effect (i.e. small studies) and/or incomplete control of linkage disequilibrium (LD) to the neighbouring DQ-loci, known to elicit a strong effect. In the present study, we aimed to undertake a statistically high-powered family-based analysis, fully controlling effects of LD between the major DQ-risk haplotypes and neighbouring candidate loci. We investigated five markers on DR3-DQ2, DR5-DQ7 and DR7-DQ2 haplotypes in 327 Norwegian and Swedish families. Our primary finding was that TNF-308A (TNF2) was significantly associated on the DR3-DQ2 haplotype [stratum specific odds ratio (OR)=2.40 (1.25–4.48), Pc=0.009, where P_c=Pn and n=number of tests performed]. Furthermore, we confirmed earlier indications that LD between TNF2 and DQA1*05-DQB1*02 on the DR3 haplotype is more strongly maintained in family-based cases than family-based controls. In conclusion, we confirmed in this study, the largest of its kind, that additional CD risk factors independent of DQ2 alleles do exist on the DR3 haplotype.     

利用SNP进行遗传病致病基因搜寻的策略

SNP是一类基于单碱基变异引起的DNA多态性,被遗传学界称为第三代遗传标记。由于SNP的诸多优点,如位点丰富和与DNA芯片等技术上的结合,它将对人类致病基因的搜寻工作起到革命性的作用。本文综合了目前SNP领域的一些进展,对这一新的标记系统在人类遗传病研究中的应用策略进行了初步概括。     

Functional polymorphisms of HSPA5: possible association with bipolar disorder

Altered endoplasmic reticulum stress (ER) response signaling is suggested in bipolar disorder. Previously, we preliminarily reported the genetic association of HSPA5 (GRP78/BiP) with bipolar disorder. Here, we extended our analysis by increasing the number of Japanese case-control samples and NIMH Genetics Initiative bipolar trio samples (NIMH trios), and also analyzed schizophrenia samples. In Japanese, nominally significant association of one haplotype was observed in extended samples of bipolar disorder but not in schizophrenia. In NIMH trios, no association was found in total samples. However, an exploratory analysis suggested that the other haplotype was significantly over-transmitted to probands only from the paternal side. The associated haplotype in Japanese or NIMH pedigrees shared three common polymorphisms in the promotor, which was found to alter promotor activity. These findings suggested promotor polymorphisms of HSPA5 may affect the interindividual variability of ER stress response and may confer a genetic risk factor for bipolar disorder.     

Identification of six novel susceptibility loci for dyslipidemia using longitudinal exome-wide association studies in a Japanese population

Recent genome-wide association studies have identified various dyslipidemia-related genetic variants. However, most studies were conducted in a cross-sectional manner. We thus performed longitudinal exome-wide association studies of dyslipidemia in a Japanese population. We used ~244,000 genetic variants and clinical data of 6022 Japanese individuals who had undergone annual health checkups for several years. After quality control, the association of dyslipidemia-related phenotypes with 24,691 single nucleotide polymorphisms (SNPs) was tested using the generalized estimating equation model. In total, 82 SNPs were significantly (P < 2.03 × 10^?6) associated with dyslipidemia phenotypes. Of these SNPs, four (rs74416240 of TCHP, rs925368 of GIT2, rs7969300 of ATXN2, and rs12231744 of NAA25) and two (rs34902660 of SLC17A3 and rs1042127 of CDSN) were identified as novel genetic determinants of hypo-HDL- and hyper-LDL-cholesterolemia, respectively. A replication study using the cross-sectional data of 8310 Japanese individuals showed the association of the six identified SNPs with dyslipidemia-related traits.     

Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models

Summary This article introduces new methods for performing classification of complex, high‐dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between‐function correlations. The methods include training and prediction steps. In the training steps we train the FMM model by treating class designation as one of the fixed effects, and in the prediction steps we classify the new objects using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the methods can be used in any FMM framework, we provide details for two specific Bayesian approaches: the Gaussian, wavelet‐based FMM (G‐WFMM) and the robust, wavelet‐based FMM (R‐WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R‐WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down‐weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with other recently developed functional classification methods.     

SNP analyses of growth factor genes EGF, TGFbeta-1, and HGF reveal haplotypic association of EGF with autism

Autism is a pervasive neurodevelopmental disorder diagnosed in early childhood. Growth factors have been found to play a key role in the cellular differentiation and proliferation of the central and peripheral nervous systems. Epidermal growth factor (EGF) is detected in several regions of the developing and adult brain, where, it enhances the differentiation, maturation, and survival of a variety of neurons. Transforming growth factor-beta (TGFbeta) isoforms play an important role in neuronal survival, and the hepatocyte growth factor (HGF) has been shown to exhibit neurotrophic activity. We examined the association of EGF, TGFbeta1, and HGF genes with autism, in a trio association study, using DNA samples from families recruited to the Autism Genetic Resource Exchange; 252 trios with a male offspring scored for autism were selected for the study. Transmission disequilibrium test revealed significant haplotypic association of EGF with autism. No significant SNP or haplotypic associations were observed for TGFbeta1 or HGF. Given the role of EGF in brain and neuronal development, we suggest a possible role of EGF in the pathogenesis of autism.     

SNP genotyping through the melting analysis of unlabelled oligonucleotide applied on dilute PCR amplicon

Adaptation of DNA melting analysis for polymorphic single nucleotides (SNPs) genotyping using an unlabeled oligonucleotide probe for polymorphic DNAs under the presence of fluorescent DNA binding dye necessitates a reaction condition where the probe efficiently associates with a target strand that is PCR amplified. We present experimental evidence that application of an unlabeled probe to a dilute PCR amplicon provides a condition such that the fluorescent signals gained subsequently by probe melting are sufficient to discriminate allelic identities. This approach is best exploited by adapting the multiplexing PCR technique in order to cover multiple SNPs for given samples. 3′-end modification of the probe is unnecessary as the amplicon dilution step provides a way of inactivating the polymerase through divalent cation chelation. With the use of low-cost reagents and ordinary laboratory equipment, this method offers a rapid, simple and cost-efficient way of SNP genotyping.     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.     

Identification of mitochondrial disease genes through integrative analysis of multiple datasets

Determining the genetic factors in a disease is crucial to elucidating its molecular basis. This task is challenging due to a lack of information on gene function. The integration of large-scale functional genomics data has proven to be an effective strategy to prioritize candidate disease genes. Mitochondrial disorders are a prevalent and heterogeneous class of diseases that are particularly amenable to this approach. Here we explain the application of integrative approaches to the identification of mitochondrial disease genes. We first examine various datasets that can be used to evaluate the involvement of each gene in mitochondrial function. The data integration methodology is then described, accompanied by examples of common implementations. Finally, we discuss how gene networks are constructed using integrative techniques and applied to candidate gene prioritization. Relevant public data resources are indicated. This report highlights the success and potential of data integration as well as its applicability to the search for mitochondrial disease genes.