Redefining Genomic Privacy: Trust and Empowerment

Fulfilling the promise of the genetic revolution requires the analysis of large datasets containing information from thousands to millions of participants. However, sharing human genomic data requires protecting subjects from potential harm. Current models rely on de-identification techniques in which privacy versus data utility becomes a zero-sum game. Instead, we propose the use of trust-enabling techniques to create a solution in which researchers and participants both win. To do so we introduce three principles that facilitate trust in genetic research and outline one possible framework built upon those principles. Our hope is that such trust-centric frameworks provide a sustainable solution that reconciles genetic privacy with data sharing and facilitates genetic research.     

Robust joint analysis allowing for model uncertainty in two-stage genetic association studies

Background  

The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate.     

Understanding type 1 diabetes through genetics: advances and prospects

Starting with early crucial discoveries of the role of the major histocompatibility complex, genetic studies have long had a role in understanding the biology of type 1 diabetes (T1D), which is one of the most heritable common diseases. Recent genome-wide association studies (GWASs) have given us a clearer picture of the allelic architecture of genetic susceptibility to T1D. Fine mapping and functional studies are gradually revealing the complex mechanisms whereby immune self-tolerance is lost, involving multiple aspects of adaptive immunity. The triggering of these events by dysregulation of the innate immune system has also been implicated by genetic evidence. Finally, genetic prediction of T1D risk is showing promise of use for preventive strategies.     

Cross-Tissue and Tissue-Specific eQTLs: Partitioning the Heritability of a Complex Trait

Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (∼10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ≥ 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.     

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.     

Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies

Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their “shared pathogenetics”. The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases.     

Genetic-based signatures of the latitudinal differences in chronotype

The natural cycles of night and day, and their length, remain stable in near-equatorial African regions but they vary with latitude and season in Eurasia. This new environmental factor might shape the adaptation of circadian rhythms of Eurasians after the out-of-African dispersal of their African ancestors. To identify the genetic-based signatures of this adaptation, geographic variation in allele frequencies of more than 2300 genetic variants was analyzed using data from 5 African and 11 Eurasian populations of the 1000 Genomes Project. The genetic signatures of latitude-dependent polygenic selection were found more frequently within non-coding DNA regions associated with morningness–eveningness in genome-wide association studies (GWASs) than among polymorphisms hinted by GWASs of other traits/diseases and among polymorphisms sampled from pseudogenes and from protein-coding regions in either circadian clock genes or reference genes. Some of such variants were located within the introgressions of the Neanderthal’s genome into the genomes of Eurasians.     

Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies

Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.     

Genetic basis of rheumatoid arthritis: A current review

Rheumatoid arthritis (RA) is one of the most common autoimmune diseases. As with other complex traits, genome-wide association studies (GWASs) have tremendously enhanced our understanding of the complex etiology of RA. In this review, we describe the genetic architecture of RA as determined through GWASs and meta-analyses. In addition, we discuss the pathologic mechanism of the disease by examining the combined findings of genetic and functional studies of individual RA-associated genes, including HLA-DRB1, PADI4, PTPN22, TNFAIP3, STAT4, and CCR6. Moreover, we briefly examine the potential use of genetic data in clinical practice in RA treatment, which represents a challenge in medical genetics in the post-GWAS era.     

基于高通量测序的全基因组关联研究策略

全基因组关联研究（Genome-wide association study, GWAS）是人类复杂疾病研究的重要组成部分之一,在群体水平检测全基因组范围的遗传变异与可观测性状间的遗传关联。传统的GWAS是以芯片（Array）技术获得高密度的遗传变异,尽管硕果累累,但也存在不少问题。如：所谓的“缺失的遗传力”,即利用关联分析检测达到全基因组水平显著的遗传变异位点只能解释小部分遗传力;在某些性状上不同研究的结果一致性较弱;显著关联的遗传变异位点的功能较难解释等。高通量测序技术,也称第二代测序（Next-generation sequencing, NGS）技术,可以快速、准确地产出高通量的变异位点数据,为解决以上问题提供了可行的方案。基于NGS技术的GWAS方法（NGS-GWAS）,可在一定程度上弥补传统GWAS的不足。文章对NGS-GWAS策略和方法进行了系统性调研,提出了目前较为可行的NGS-GWAS的实施策略和方法,并对NGS-GWAS如何应用于个体化医疗（Personalized medicine, PM）进行了展望。     

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

Background  

Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.     

Sortilin: an unusual suspect in cholesterol metabolism: from GWAS identification to in vivo biochemical analyses, sortilin has been identified as a novel mediator of human lipoprotein metabolism

The concentration of low-density lipoprotein (LDL) cholesterol (C) in plasma is a key determinant of cardiovascular disease risk and human genetic studies have long endeavoured to elucidate the pathways that regulate LDL metabolism. Massive genome-wide association studies (GWASs) of common genetic variation associated with LDL-C in the population have implicated SORT1 in LDL metabolism. Using experimental paradigms and standards appropriate for understanding the mechanisms by which common variants alter phenotypic expression, three recent publications have presented divergent and even contradictory findings. Interestingly, although these reports each linked SORT1 to LDL metabolism, they did not agree on a mechanism to explain the association. Here, we review recent mechanistic studies of SORT1 - the first gene identified by GWAS as a determinant of plasma LDL-C to be evaluated mechanistically.     

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.     

Public Opinion about the Importance of Privacy in Biobank Research

Concerns about privacy may deter people from participating in genetic research. Recruitment and retention of biobank participants requires understanding the nature and magnitude of these concerns. Potential participants in a proposed biobank were asked about their willingness to participate, their privacy concerns, informed consent, and data sharing. A representative survey of 4659 U.S. adults was conducted. Ninety percent of respondents would be concerned about privacy, 56% would be concerned about researchers having their information, and 37% would worry that study data could be used against them. However, 60% would participate in the biobank if asked. Nearly half (48%) would prefer to provide consent once for all research approved by an oversight panel, whereas 42% would prefer to provide consent for each project separately. Although 92% would allow academic researchers to use study data, 80% and 75%, respectively, would grant access to government and industry researchers. Concern about privacy was related to lower willingness to participate only when respondents were told that they would receive $50 for participation and would not receive individual research results back. Among respondents who were told that they would receive $200 or individual research results, privacy concerns were not related to willingness. Survey respondents valued both privacy and participation in biomedical research. Despite pervasive privacy concerns, 60% would participate in a biobank. Assuring research participants that their privacy will be protected to the best of researchers'' abilities may increase participants'' acceptance of consent for broad research uses of biobank data by a wide range of researchers.     

糖尿病肾病遗传学研究进展

糖尿病肾病是糖尿病最严重的慢性并发症之一, 不同种族的发病率分析和家族聚集性研究显示遗传因素是糖尿病肾病发生、发展的重要因素。文章从3个方面对糖尿病肾病的遗传学研究进展进行综述：“候选基因”的关联研究、连锁分析和全基因组关联研究。关联研究及荟萃分析显示一些候选基因与糖尿病肾病显著相关, 包括ACE、AGT和PPARG等基因; 连锁分析及全基因组连锁分析发现多个糖尿病肾病的易感染色体位点; 随着高通量测序技术和芯片技术的发展, 全基因组关联研究已成为糖尿病肾病遗传学研究的重要途径。虽然遗传因素在糖尿病肾病发病中占据重要的位置, 但还不能完全解释糖尿病肾病的发病原因, 因为糖尿病肾病的发生还受环境因素的影响, 然而糖尿病肾病的遗传学研究可为糖尿病肾病发病机制研究以及药物治疗靶点研究提供一定的理论依据。     

Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20,240 Sibling Pairs

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10⁻⁷) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10⁻¹⁰) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.     

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.     

Comparison of the DNA methylation profiles of human peripheral blood cells and transformed B-lymphocytes

Epidemiological studies of DNA methylation (DNAm) profiles may hold substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. Different cell types are likely to have different DNAm patterns. We investigate the DNAm differences between two types of biospecimens available in many genetic epidemiology studies. We compared DNAm patterns in two different DNA samples from each of 34 participants in the Genetic Epidemiology Network of Arteriopathy study (20 Caucasians and 14 African-Americans). One was extracted from peripheral blood cells (PBC) and the other from transformed B-lymphocytes (TBL). The genome-wide DNAm profiles were compared at over 27,000 genome-wide methylation sites. We found that 26 out of the 34 participants had correlation coefficients higher than 0.9 between methylation profiles of PBC and TBL. Although a high correlation was observed in the DNAm profile between PBC and TBL, we also observed variation across samples from different DNA resources and donors. Using principal component analysis of the DNAm profiles, the two sources of the DNA samples could be accurately predicted. We also identified 3,723 autosomal DNAm sites that had significantly different methylation statuses in PBC compared to TBL (Bonferroni corrected p value <0.05). Both PBC and TBL provide a rich resource for understanding the DNAm profiles in humans participating in epidemiologic studies. While the majority of DNAm findings in PBC and TBL may be consistent, caution must be used when interpreting results because of the possibility of cell type-specific methylation modification.