Toward owner governance in genomic data privacy with Governome

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. Consequently, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). GWAS analyze genome sequence variations in order to identify genetic risk factors for diseases. These studies often require pooling data from different sources together in order to unravel statistical patterns, and relationships between genetic variants and diseases. Here, the primary challenge is to fulfill one major objective: accessing multiple genomic data repositories for collaborative research in a privacy-preserving manner. Due to the privacy concerns regarding the genomic data, multi-jurisdictional laws and policies of cross-border genomic data sharing are enforced among different countries. In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions to ensure high efficiency and privacy at the same time. Different experimental settings show the efficacy and applicability of such hybrid framework in secure conduction of GWAS. To the best of our knowledge, this hybrid use of homomorphic encryption along with Intel SGX is not proposed to this date. SAFETY is up to 4.82 times faster than the best existing secure computation technique.

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson’s disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson’s disease.Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways.Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3.Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson’s disease.

Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P-value 3.70 × 10–6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.

The quest for genes associated with different phenotypes and diseases is a central theme of research in human genetics. Several human phenotypes/diseases including height, myopia, diabetes, and others show complex pattern of inheritance where many genes together with environmental factors influence the intensity of a phenotype or disease. The genome-wide association study (GWAS) has been successful in mapping loci associated to variety of human traits and diseases. GWAS test for association of single nucleotide polymorphism (SNP) markers with a phenotype of interest at the genome-wide scale, this hence requires a severe multiple testing correction for statistical significance (p-value < 5 × 10-8). A single marker association approach has limited power in finding disease susceptibility genes especially in situations where the risk architecture of a gene is defined by many SNPs rather than one or two. This issue can be addressed through gene-based association approaches where the combined (or mean) statistics of a number of markers in a gene is tested for association with the trait of interest. Gene-based association approaches are established as a complementary approach to GWAS. It is well established that genes work in concert as molecular networks and cellular pathways to perform biological processes. Individual tests for association of a gene (or a SNP in a gene) with phenotype may have limited power in identifying interacting genes with small effects. SNP or gene-based tests are limited in how much they can reveal of the biological mechanism behind phenotypic variation. Pathway-based association approaches, where gene-based tests statistics are combined into gene sets and tested for association, have been developed to overcome these limitations. Each gene set represents a biological pathway or a process, based on prior biological knowledge. Various techniques have been proposed to perform pathway-based association tests on GWAS data, including ALIGATOR, MAGENTA, and INRICH. These packages show several limitations, which can be addressed by the development of a new pathway-based association approach. The aim of this thesis is to demonstrate the analytical approaches (GWAS and post-GWAS) to identify genes and pathways associated with complex ophthalmology traits and report methods for gene- and pathways-based analysis of GWAS summary data. Chapter 1 introduces the basic principles and strategies involved in GWAS analysis. Further, it extensively reviews the popular methods available for gene-based and pathway-based association tests. Chapter 2 describes an initial GWAS on corneal curvature, a phenotype of interest to diseases such as myopia and keratoconus, in Australians and further meta-analysis of two cohorts comprising of 1788 Australian twins and their families and 1013 individuals from a birth cohort from Western Australia. It also reports on replication in Australians of Northern European ancestry of the previously reported association of SNPs in PDGFRA and FRAP1 genes with corneal curvature in an Asian population. Chapter 3 is a demonstration of GWAS and pathway-based analysis. It reports the first GWAS and meta-analysis for corneal astigmatism in two Australian cohort studies, total sample size ~2,700. This chapter describes the application of Pathway-VEGAS, an extension of the gene-based analysis tool Versatile Gene-based Association Study (VEGAS) program for pathway-based analysis. Chapter 4 reports the successful application of VEGAS and Pathway-VEGAS to GWAS meta-analysis data. As part of the international glaucoma genetics consortium (IGGC), I participated in genetic analysis of the vertical cup disc ratio (VCDR) phenotype. This manuscript describes the meta-analysis of GWAS on a discovery sample of 21,094 individuals from ten cohorts of northern European ancestry and a replication sample of 6,784 individuals from four Asian cohorts. Gene-based and pathway-based tests were performed using the VEGAS and Pathway-VEGAS programs separately for northern European ancestry samples and Asian ancestry samples, and respective results were meta-analysed. This chapter demonstrates the advantages of using different approaches such as single marker GWAS, gene-based association approach and pathway based association approach. Chapter 5 presents the VEGAS2 software. VEGAS is one of the most popular gene-based association software. VEGAS however has some limitations such as the inability to perform a gene-based tests on the X chromosome, dependence on HapMap2 data to model the correlation between SNPs and inflexibility in the selection of gene boundary. The VEGAS2 software is an extension of VEGAS, redeveloped and upgraded to overcome these limitations. Chapter 6 presents the VEGAS2Pathway approach for pathway analysis of GWAS summary data. This chapter describes the shortcomings of many popular pathway-based association approaches, and how VEGAS2-Pathway overcomes these limitations. Further it demonstrates the application of VEGAS2Pathway on the endometriosis GWAS summary data. The last chapter provides the contributions this thesis makes to the literature and discusses its implications, limitations and future directions. In conclusion, my thesis provides the methodological and analytical approaches to further our knowledge in human genetics.

Lifetime productivity is a trait of great importance to dairy cattle populations as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics data sets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the Restricted Maximum Likelihood (REML) method. Genome-wide association studies (GWAS) and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-seq data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE) while the genetic correlation with 305d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle and important candidate genes were identified by integrating genomic and transcriptomic data sets.

The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium as a Model of Collaborative Science

Background: The pharmacogenetic effect on cardiovascular disease reduction in response to statin treatment has only been assessed in small studies. In a pharmacogenetic genome wide association study (GWAS) analysis within the Genomic Investigation of Statin Therapy (GIST) consortium, we investigated whether genetic variation was associated with the response of statins on cardiovascular disease risk reduction. Methods: The investigated endpoint was incident myocardial infarction (MI) defined as coronary heart disease death and definite and suspect non-fatal MI. For imputed single nucleotide polymorphisms (SNPs), regression analysis was performed on expected allelic dosage and meta-analysed with a fixed-effects model, inverse variance weighted meta-analysis. All SNPs with p-values <5.0 × 10−4 in stage 1 GWAS meta-analysis were selected for further investigation in stage-2. As a secondary analysis, we extracted SNPs from the Stage-1 GWAS meta-analysis results based on predefined hypotheses to possibly modifying the effect of statin therapy on MI. Results: In stage-1 meta-analysis (eight studies, n = 10,769, 4,212 cases), we observed no genome-wide significant results (p < 5.0 × 10−8). A total of 144 genetic variants were followed-up in the second stage (three studies, n = 1,525, 180 cases). In the combined meta-analysis, no genome-wide significant hits were identified. Moreover, none of the look-ups of SNPs known to be associated with either CHD or with statin response to cholesterol levels reached Bonferroni level of significance within our stage-1 meta-analysis. Conclusion: This GWAS analysis did not provide evidence that genetic variation affects statin response on cardiovascular risk reduction. It does not appear likely that genetic testing for predicting effects of statins on clinical events will become a useful tool in clinical practice.

Assessing causal relationship between non-alcoholic fatty liver disease and risk of atrial fibrillation

High-throughput genotyping and sequencing has revolutionized animal breeding by providing access to vast amounts of genomic data to facilitate precise selection for desirable traits. This shift from traditional methods to genomic selection provides dense marker information for predicting genetic variants. However, the success of genomic selection heavily depends on the accuracy and quality of the genomic data. Inaccurate or low-quality data can lead to flawed predictions, compromising breeding programs and reducing genetic gains. Therefore, stringent quality control (QC) measures are essential at every stage of data processing. QC in genomic data involves managing single nucleotide polymorphism (SNP) quality, assessing call rates, and filtering based on minor allele frequency (MAF) and Hardy-Weinberg equilibrium (HWE). High-quality SNP data is crucial because genotyping errors can bias the estimates of breeding values. Cost-effective low-density genotyping platforms often require imputation to deduce missing genotypes. QC is vital for genomic selection, genome-wide association studies (GWAS), and population genetics analyses because it ensures data accuracy and reliability. This paper reviews QC strategies for genomic data and emphasizes their applications in animal breeding programs. By examining various QC tools and methods, this review highlights the importance of data integrity in achieving successful outcomes in genomic selection, GWAS, and population analyses. Furthermore, this review covers the critical role of robust QC measures in enhancing the reliability of genomic predictions and advancing animal breeding practices.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study's sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335-0.374, the accuracy after five-fold cross-verification was 0.154-0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59-7.79%.

Foundation parents (FPs) play an irreplaceable role in maize breeding practices. Maize white spot (MWS) is an important disease in Southwest China that always seriously reduces production. However, knowledge about the genetic mechanism of MWS resistance is limited. In this paper, a panel of 143 elite lines were collected and genotyped by using the MaizeSNP50 chip with approximately 60,000 single nucleotide polymorphisms (SNPs) and evaluated for resistance to MWS among 3 environments, and a genome-wide association study (GWAS) and transcriptome analysis were integrated to reveal the function of the identity-by-descent (IBD) segments for MWS. The results showed that (1) 225 IBD segments were identified only in the FP QB512, 192 were found only in the FP QR273 and 197 were found only in the FP HCL645. (2) The GWAS results showed that 15 common quantitative trait nucleotides (QTNs) were associated with MWS. Interestingly, SYN10137 and PZA00131.14 were in the IBD segments of QB512, and the SYN10137-PZA00131.14 region existed in more than 58% of QR273's descendants. (3) By integrating the GWAS and transcriptome analysis, Zm00001d031875 was found to located in the region of SYN10137-PZA00131.14. These results provide some new insights for the detection of MWS's genetic variation mechanisms.

Genome-Wide Linkage Analysis Reveals Novel Loci Modifying Plasma Von Willebrand Factor Undetected by Genome-Wide Association

ObjectiveDeveloped sequencing techniques are yielding large-scale genomic data at low cost. A genome-wide association study (GWAS) targeting genetic variations that are significantly associated with a particular disease offers great potential for medical improvement. However, subjects who volunteer their genomic data expose themselves to the risk of privacy invasion; these privacy concerns prevent efficient genomic data sharing. Our goal is to presents a cryptographic solution to this problem.MethodsTo maintain the privacy of subjects, we propose encryption of all genotype and phenotype data. To allow the cloud to perform meaningful computation in relation to the encrypted data, we use a fully homomorphic encryption scheme. Noting that we can evaluate typical statistics for GWAS from a frequency table, our solution evaluates frequency tables with encrypted genomic and clinical data as input. We propose to use a packing technique for efficient evaluation of these frequency tables.ResultsOur solution supports evaluation of the D′ measure of linkage disequilibrium, the Hardy-Weinberg Equilibrium, the χ2 test, etc. In this paper, we take χ2 test and linkage disequilibrium as examples and demonstrate how we can conduct these algorithms securely and efficiently in an outsourcing setting. We demonstrate with experimentation that secure outsourcing computation of one χ2 test with 10, 000 subjects requires about 35 ms and evaluation of one linkage disequilibrium with 10, 000 subjects requires about 80 ms.ConclusionsWith appropriate encoding and packing technique, cryptographic solutions based on fully homomorphic encryption for secure computations of GWAS can be practical.

Overview Advances in high-throughput sequencing, genotyping, and characterization of haplotype diversity are consistently generating vast amounts of genomic data. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation. In the recent years the number of known SNPs has been increasing exponentially; the last release of the NCBI’s dbSNP database contained more than 50 million human SNPs. SNPs are interesting as both markers of evolutionary history and in the context of their phenotypic manifestations (e.g. characteristic traits and diseases). For some diseases, e.g. sickle-cell anemia, the causative SNPs are well documented. In most other cases, the detection of disease-causing variants is still a problem. The genome-wide association studies (GWAS) provide insight into SNP-disease relationships. However, GWAS analysis is both experimentally and computationally expensive and fails to properly consider the rare variants, i.e. individual-specific SNPs that have yet to be documented on a population scale. This discrepancy between the deluge of SNP data and the lack of its interpretation spurs the development of the SNP impact annotation/prediction algorithms. In the near future, the study of genetic variation in disease and treatment options will be key for the development of the field of personalized medicine. In 2010, the first edition of the Critical Assessment of Genomic Interpretation (CAGI; Berkeley, California) was organized to evaluate the ability of available computational methods to predict the phenotypic impacts of genomic variation. Annotation of SNPs was also a hot topic in many other meetings, such as AIMM at ECCB 2010 (Ghent, Belgium), the HGVS 2010 meeting (Washington, DC) and PSB 2011 (Big Island of Hawaii, USA). In line with the increasing interest in the genetic variation analysis and annotation, on July 15, 2011 we organized the first SNP Special Interesting Group (SNP-SIG) meeting at ISMB/ECCB’2011 in Vienna, Austria (http://snps.uib.es/ snp-sig/2011). This meeting attempted to summarize the field’s research advances in the directions of “Annotation and prediction of structural/functional impacts of coding SNPs” and “SNPs and Personal Genomics: GWAS, populations and phylogenetic analysis”. Over 70 scientists actively working in the field and strongly interested in its development have officially registered for the SIG. On the date of the meeting, an even larger number of ISMB participants have gathered to discuss their work, the state of the art, and future perspectives. In all, 17 presentation proposals and 13 posters were submitted to the SIG and eight works where selected for an oral presentation at the meeting. Distinguished scientists were invited to share their visions of the field past, present, and future: Steven Brenner (University of California at Berkeley), Atul Butte (Stanford University), John Moult (University of Maryland, College Park), Burkhard Rost (Techinal University of Munich) and Mauno Vihinen (Lund University). A round table discussion on the most timely and important problems of SNP annotation was held, directed by Christopher Baker (University of New Brunswick), Maricel Kann (University of Maryland, Baltimore), Sean Mooney (Buck Institute), Pauline Ng (Genome Institute of Singapore) and Mauno Vihinen (Lund University). * Correspondence: YanaB@rci.rutgers.edu; emidio.capriotti@uab.es Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick 76 Lipman Drive, NJ 08901, USA Department of Mathematics and Computer Science, University of Balearic Islands, Ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain Full list of author information is available at the end of the article Bromberg and Capriotti BMC Genomics 2012, 13(Suppl 4):S1 http://www.biomedcentral.com/1471-2164/13/S4/S1