Characterisation of genome-wide association epistasis signals for serum uric acid in human population isolates

Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations.     

肺结核全基因组关联研究进展

肺结核是由结核分枝杆菌感染引起的一类古老但仍对人类造成巨大影响的传染性疾病。到目前为止, 肺结核依然是由单一病原菌导致死亡人数最多的疾病, 并且随着耐药菌株的出现而呈现死灰复燃之势。近几年, 肺结核全基因组关联研究在世界范围内取得了阶段性成果, 发现了与肺结核相关联的遗传易感位点和区域, 使肺结核的遗传学研究进入了一个崭新的阶段, 为后续肺结核的早期和综合防治提供了重要线索。然而, 由于人群遗传结构差异和宿主/病原体相互作用, 与其他复杂疾病相比, 肺结核全基因组关联研究依旧面临重重困难, 进展缓慢。文章对不同人群肺结核全基因组关联研究及其验证进行综述, 并系统阐述了目前研究中存在的困难及可能的应对策略。     

全基因组关联研究现状

在过去的5年中, 全基因组关联研究(Genome-wide association study, GWAS)方法已被证明是研究复杂疾病和性状遗传易感变异的一种有效手段。目前, 各国科学家在多种复杂疾病和性状中开展了大量的GWAS, 对肿瘤、糖尿病、心脏病、神经精神疾病、自身免疫及免疫相关疾病等复杂疾病以及一些常见性状(如身高、体重、血脂、色素等)的遗传易感基因研究取得了重大成果。截止到2010年9月11日, 运用GWAS开展了对近200种复杂疾病/性状的研究, 发现了3 000多个疾病相关的遗传变异。文章就GWAS的发展及其在复杂疾病/性状中的应用做一综述。     

Towards practical privacy-preserving genome-wide association study

Background

The deployment of Genome-wide association studies (GWASs) requires genomic information of a large population to produce reliable results. This raises significant privacy concerns, making people hesitate to contribute their genetic information to such studies.

Results

We propose two provably secure solutions to address this challenge: (1) a somewhat homomorphic encryption (HE) approach, and (2) a secure multiparty computation (MPC) approach. Unlike previous work, our approach does not rely on adding noise to the input data, nor does it reveal any information about the patients. Our protocols aim to prevent data breaches by calculating the χ² statistic in a privacy-preserving manner, without revealing any information other than whether the statistic is significant or not. Specifically, our protocols compute the χ² statistic, but only return a yes/no answer, indicating significance. By not revealing the statistic value itself but only the significance, our approach thwarts attacks exploiting statistic values. We significantly increased the efficiency of our HE protocols by introducing a new masking technique to perform the secure comparison that is necessary for determining significance.

Conclusions

We show that full-scale privacy-preserving GWAS is practical, as long as the statistics can be computed by low degree polynomials. Our implementations demonstrated that both approaches are efficient. The secure multiparty computation technique completes its execution in approximately 2 ms for data contributed by one million subjects.

     

A genome-wide association study for quantitative traits in schizophrenia in China

Few genome-wide association studies (GWAS) of schizophrenia have included Chinese populations, and verification of positive genetic findings from other ethnic groups is rare in Chinese groups. We used fluid intelligence as the quantitative trait reflecting schizophrenia dysfunction in Chinese populations, and determined the impact of genetic variation on fluid intelligence phenotypic patterns to identify genetic influences in schizophrenia. The study sample comprised 98 patients with schizophrenia and 60 healthy controls. The general fluid intelligence of participants was assessed with Cattell's Culture-Free Intelligence Test (CCFIT). Subjects were genotyped using the Illumina HumanHap 660 beadchip. We identified the methionine sulfoxide reductase A (MSRA) gene on chromosome 8 as having an association with fluid intelligence. However, only CCFIT subtest 1 (series score) demonstrated a significant result for the interaction term using the criteria of the quantitative trait (QT) analysis of 10(-5) for at least three SNPs. There were 15 haplotype blocks of MSRA gene SNPs identified using Haploview 4.2 with solid spine D' > 0.80. The strongest QT interaction was noted in Block 3, with the most common haplotypes being AAACAGCAG and CGCAGAAGA. In conclusion, we report data from a GWAS with quantitative traits design from Chinese first-episode schizophrenia patients and matched controls. Although the gene identified requires confirmation in an independent sample, the MSRA gene located on chromosome 8 was found to be associated with the phenotype of schizophrenia.     

Tools for genome-wide strain design and construction

Advances in DNA sequencing and synthesis technologies concurrent with the development of new recombinant DNA approaches have enabled the extension of directed evolution algorithms to the genome-scale. It is now possible to simultaneously map the effect of mutation(s) in each and every gene in the genome onto almost any screenable or selectable phenotype in less than a week. Such maps can be used to direct the design and construction of libraries containing billions of rationally designed combinatorial mutations. Such combinatorial libraries can now also be created and evaluated in less than a week. The review presents and discusses these new technologies within the context of directed evolution and inverse metabolic engineering.     

Adiponectin concentrations: a genome-wide association study

Adiponectin is associated with obesity and insulin resistance. To date, there has been no genome-wide association study (GWAS) of adiponectin levels in Asians. Here we present a GWAS of a cohort of Korean volunteers. A total of 4,001 subjects were genotyped by using a genome-wide marker panel in a two-stage design (979 subjects initially and 3,022 in a second stage). Another 2,304 subjects were used for follow-up replication studies with selected markers. In the discovery phase, the top SNP associated with mean log adiponectin was rs3865188 in CDH13 on chromosome 16 (p = 1.69 × 10(-15) in the initial sample, p = 6.58 × 10(-39) in the second genome-wide sample, and p = 2.12 × 10(-32) in the replication sample). The meta-analysis p value for rs3865188 in all 6,305 individuals was 2.82 × 10(-83). The association of rs3865188 with high-molecular-weight adiponectin (p = 7.36 × 10(-58)) was even stronger in the third sample. A reporter assay that evaluated the effects of a CDH13 promoter SNP in complete linkage disequilibrium with rs3865188 revealed that the major allele increased expression 2.2-fold. This study clearly shows that genetic variants in CDH13 influence adiponectin levels in Korean adults.     

An efficient algorithm to explore liquid association on a genome-wide scale

Background

The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Liquid association (LA) describes the extent to which coexpression of two genes may vary based on the expression level of a third gene (the controller gene). However, genome-wide application has been difficult and resource-intensive. We propose a new screening algorithm for more efficient processing of LA estimation on a genome-wide scale and apply its use to a Saccharomyces cerevisiae data set.

Results

On a test subset of the data, the fast screening algorithm achieved >99.8% agreement with the exhaustive search of LA values, while reduced run time by 81–93 %. Using a well-known yeast cell-cycle data set with 6,178 genes, we identified triplet combinations with significantly large LA values. In an exploratory gene set enrichment analysis, the top terms for the controller genes in these triplets with large LA values are involved in some of the most fundamental processes in yeast such as energy regulation, transportation, and sporulation.

Conclusion

In summary, in this paper we propose a novel, efficient algorithm to explore LA on a genome-wide scale and identified triplets of interest in cell cycle pathways using the proposed method in a yeast data set. A software package named fastLiquidAssociation for implementing the algorithm is available through http://www.bioconductor.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0371-5) contains supplementary material, which is available to authorized users.     

全基因组关联研究的深度分析策略

2005年至今,全基因组关联研究(Genome-wide association study,GWAS)发现了大量复杂疾病/性状相关变异。近来,科学家们关注的焦点又集中在了如何利用GWAS数据进行深入分析,期待发现更多复杂疾病/性状的易感基因。一些新的策略和方法已经被尝试应用到复杂疾病/性状GWAS的后续研究中,例如深入分析GWAS数据;鉴定新的复杂疾病/性状易感基因/位点;国际合作和Meta分析;易感区域精细定位及测序;多种疾病共同易感基因研究;以及基因型填补,基于通路的关联分析,基因-基因、基因-环境交互作用和上位研究等。这些策略和方法的应用弥补了经典GWAS的一些不足之处,进一步推动了人类对复杂疾病/性状遗传机制的认识。文章对上述研究的策略、方法以及所面临的问题和挑战进行了综述,为读者描绘了GWAS后期工作的一个简要框架。     

Pathway analysis of genome-wide association study for bone mineral density

The aim of this study was to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to bone mineral density (BMD) and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. We used hip BMD GWAS data of the genotypes of 301,019 SNPs in 5,715 Europeans. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the BMD GWAS dataset. The first stage involved the pre-selection of candidate causal SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified seven candidate SNPs, eight candidate pathways, and seven hypothetical biological mechanisms. Eight pathways are as follows; gamma-hexachlorocyclohexane degradation (nominal p-value < 0.001, false discovery rate (FDR) <0.001), regulation of the smoothened signaling pathway (nominal p-value < 0.001, FDR = 0.016), TACI and BCMA stimulation of B cell immune response (nominal p-value < 0.001, FDR = 0.021), endonuclease activity (nominal p-value = 0.001, FDR = 0,026), regulation of defense response to virus (nominal p-value = 0.001, FDR = 0.028), serine_type_endopeptidase_inhibitor_activity (nominal p-value = 0.001, FDR = 0.044), endoribonuclease activity (nominal p-value = 0.002, FDR = 0.045), and myeloid leukocyte differentiation (nominal p-value = 0.001, FDR = 0.050). The most significant causal pathway was gamma-hexachlorocyclohexane degradation. CYP3A5, PON2, PON3, CMBL, PON1, ALPL, CYP3A43, CYP3A7, ACP6, ACPP, and ALPI (p < 0.05) are involved in the pathway of gamma-hexachlorocyclohexane degradation. Further examination of the gene contents revealed that DBR1, DICER1, EXO1, FEN1, POP1, POP4, RPP30, and RPP38 were involved in 2 of the 8 pathways (p < 0.05). By applying ICSNPathway analysis to BMD GWAS data, we identified seven candidate SNPs and eight pathways involving gamma-hexachlorocyclohexane degradation, which may contribute to low BMD.     

冠心病全基因组关联研究进展

近年来全基因组关联研究在世界范围内发展迅猛,研究者应用全基因组关联研究策略发现了一系列疾病的相关基因或变异,将疾病的基因组研究推向一个新的阶段。冠心病是一种由环境因素和遗传因素共同作用导致的复杂疾病,且是世界范围内死亡和致残的首要原因之一,世界各地的研究者应用此策略发现了候选基因关联研究未曾发现的多个冠心病相关易感区域。文章对近年来世界范围内针对冠心病的全基因组关联研究取得的重要进展进行简要总结,然后就现阶段全基因组关联研究所面临的挑战以及对未来研究的发展趋势进行分析阐述,为进一步探究冠心病的遗传机制提供指导。     

Pathway analysis of a genome-wide association study in schizophrenia

Objective

The aim of this study was to identify the candidate single nucleotide polymorphisms (SNPs) and candidate mechanisms that contribute to schizophrenia susceptibility and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach.

Methods

We used schizophrenia GWAS data of the genotypes of 660,259 SNPs in 1378 controls and 1351 cases of European descent after quality control filtering. ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis was applied to the schizophrenia GWAS dataset. The first stage involved the pre-selection of candidate SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate SNPs using improved-gene set enrichment analysis.

Results

ICSNPathway analysis identified fifteen candidate SNPs, ten candidate pathways, and nine hypothetical biological mechanisms. The most strongly associated potential pathways were as follows. First, rs1644731 and rs1644730 to RDH8 to estrogen biosynthetic process (p < 0.001, FDR < 0.001). The genes involved in this pathway are RDH8 and HSD3B1 (p < 0.05). All-trans-retinol dehydrogenase (RDH8) is a visual cycle enzyme that reduces all-trans-retinal to all-trans-retinol in the presence of NADPH. The chemical reactions and pathways involved result in the formation of estrogens, which are C18 steroid hormones that can stimulate the development of female sexual characteristics. Second, rs1146031 to ACVR1 to mesoderm formation and activin binding (p < 0.001, FDR = 0.032, 0.034). Two of 15 candidate genes are known genes associated with schizophrenia: KCNQ2 and APOL2. One of the 10 candidate pathways, estrogen biosynthetic process, is known to be associated with schizophrenia (p < 0.001, FDR < 0.001). However, 13 of candidate genes (RDH8, ACVR1, PSMD9, KCNAB1, SLC17A3, ARCN1, COG7, STAB2, LRPAP1, STAB1, CXCL16, COL4A4, EXOSC3) and 9 of candidate pathways were novel.

Conclusion

By applying ICSNPathway analysis to schizophrenia GWAS data, we identified candidate SNPs, genes like KCNQ2 and APOL2 and pathways involving the estrogen biosynthetic process may contribute to schizophrenia susceptibility. Further analyses are needed to validate the results of this analysis.     

全基因组关联研究中的交互作用研究现状

利用高密度单核苷酸多态(Single nucleotide polymorphism,SNP)标记在全基因组范围内检测影响复杂疾病/性状的染色体区段或基因,已经成为目前遗传学领域新的突破点之一。在全基因组关联研究(Genome-wide association study,GWAS)取得大量成果之后,研究者们对在全基因范围内研究交互作用产生了极大的热情。近几年,对交互作用的研究,无论是在方法的研发、实际的应用以及统计学上的交互向生物学上的交互转化,还是在信息组学的整合,都呈现快速发展的趋势。已有很多策略和方法被尝试用于进行全基因组交互作用分析,这些研究推动了对复杂疾病/性状遗传机制的进一步认识。基于目前全基因组交互分析所采用的各类数据处理方法的理论与算法的异同,文章拟对目前使用较为广泛的回归类方法、机器学习方法、贝叶斯模型法、SNP筛选类方法和基于并行程序的方法等5类方法加以评述,着重介绍了这些方法的算法原理、计算效率以及差别之处,以期能够为相关领域的研究者提供参考。     

全基因组关联研究通路分析方法现状

全基因组关联研究(genome-wide association study, GWAS)自2005年首次发表以来已不断增进人们对疾病遗传机制的认识,结合系统生物学并改进统计分析方法是对GWAS数据进行深度挖掘的重要途径。通路分析(pathway analysis)将GWAS所检测的遗传变异根据一定的生物学含义组合为集合进行分析,有利于发现对疾病单独效应小却在通路中相互关联的遗传变异,更有利于进行生物学解释。当前通路分析在GWAS数据上已有较为广泛的应用并取得初步成果。与此同时,通路分析的统计方法仍在不断发展。本文旨在介绍现有直接以SNP为对象的GWAS通路分析算法,根据方法中是否采用核函数分为非核算法和核算法两大类,其中非核算法主要包括基因功能富集分析(gene set enrichment analysis, GSEA)和分层贝叶斯优取(hierarchical Bayes prioritization, HBP),核算法包括线性核(linear kernel, LIN)、状态认证核(identity-by-status kernel, IBS)和尺度不变核(powered exponential kernel)。通过介绍这些方法的计算原理和优缺点,以期为新算法的构建提供更好的思路,为GWAS领域研究方法的选择提供参考。     

A genome-wide association study of BMI in American Indians

Numerous studies have been done to understand genetic contributors to BMI, but only a limited number of studies have been done in nonwhite groups such as American Indians. A genome-wide association study (GWAS) for BMI was therefore performed in Pima Indians. BMI measurements from a longitudinal study of 1,120 Pima Indians and 454,194 single-nucleotide polymorphisms (SNPs) from the 1 million Affymetrix SNP panel were used (35% of SNPs were excluded due to minor allele frequency <0.05). Data included BMI measured at multiple examinations collected from 1965 to 2004, as well as the maximum BMI at one of these visits. General and within-family tests were performed using a maximum-likelihood based mixed model procedure. No SNP reached a genome-wide significance level (estimated at P < 4.94 × 10(-7)). For repeated measures analyses, the strongest associations for general and within-family tests mapped to two different regions on chromosome 6 (rs9342220 (P = 1.39 × 10(-6)) and rs7758764 (P = 2.51 × 10(-6)), respectively). For maximum BMI, the strongest association for the general tests mapped to chromosome 4 (rs17612333; P = 1.98 × 10(-6)) and to chromosome 3 (rs11127958; P = 1.53 × 10(-6)) for the within-family tests. Further analysis is important because only a few of these regions have been previously implicated in a GWAS and genetic susceptibility may differ by ethnicity.