Collaborative Genome-wide Association Studies Research Articles

A functional schizophrenia-associated genetic variant near the TSNARE1 and ADGRB1 genes

Recent collaborative genome wide association studies (GWAS) have identified >200 independent loci contributing to risk for schizophrenia (SCZ). The genes closest to these loci have diverse functions, supporting the potential involvement of multiple relevant biological processes; yet there is no direct evidence that individual variants are functional or directly linked to specific genes. Nevertheless, overlap with certain epigenetic marks suggest that most GWAS-implicated variants are regulatory. Based on the strength of association with SCZ and the presence of regulatory epigenetic marks, we chose one such variant near TSNARE1 and ADGRB1, rs4129585, to test for functional potential and assay differences that may drive the pathogenicity of the risk allele. We observed that the variant-containing sequence drives reporter expression in relevant neuronal populations in zebrafish. Next, we introduced each allele into human induced pluripotent cells and differentiated 4 isogenic clones homozygous for the risk allele and 5 clones homozygous for the non-risk allele into neural precursor cells. Employing RNA-seq, we found that the two alleles yield significant transcriptional differences in the expression of 109 genes at FDR <0.05 and 259 genes at FDR <0.1. We demonstrate that these genes are highly interconnected in pathways enriched for synaptic proteins, axon guidance, and regulation of synapse assembly. Exploration of genes near rs4129585 suggests that this variant does not regulate TSNARE1 transcripts, as previously thought, but may regulate the neighboring ADGRB1, a regulator of synaptogenesis. Our results suggest that rs4129585 is a functional common variant that functions in specific pathways likely involved in SCZ risk.

Gender-Specific Genetic Predisposition to Breast Cancer: BRCA Genes and Beyond

Among neoplastic diseases, breast cancer (BC) is one of the most influenced by gender. Despite common misconceptions associating BC as a women-only disease, BC can also occur in men. Additionally, transgender individuals may also experience BC. Genetic risk factors play a relevant role in BC predisposition, with important implications in precision prevention and treatment. The genetic architecture of BC susceptibility is similar in women and men, with high-, moderate-, and low-penetrance risk variants; however, some sex-specific features have emerged. Inherited high-penetrance pathogenic variants (PVs) in BRCA1 and BRCA2 genes are the strongest BC genetic risk factor. BRCA1 and BRCA2 PVs are more commonly associated with increased risk of female and male BC, respectively. Notably, BRCA-associated BCs are characterized by sex-specific pathologic features. Recently, next-generation sequencing technologies have helped to provide more insights on the role of moderate-penetrance BC risk variants, particularly in PALB2, CHEK2, and ATM genes, while international collaborative genome-wide association studies have contributed evidence on common low-penetrance BC risk variants, on their combined effect in polygenic models, and on their role as risk modulators in BRCA1/2 PV carriers. Overall, all these studies suggested that the genetic basis of male BC, although similar, may differ from female BC. Evaluating the genetic component of male BC as a distinct entity from female BC is the first step to improve both personalized risk assessment and therapeutic choices of patients of both sexes in order to reach gender equality in BC care. In this review, we summarize the latest research in the field of BC genetic predisposition with a particular focus on similarities and differences in male and female BC, and we also discuss the implications, challenges, and open issues that surround the establishment of a gender-oriented clinical management for BC.

Gelişmiş Güvenlik ve Besin Değeri için Makarnalık Buğday Islahı: Alüminyum Alımıyla Mücadelede Bir GWAS Yaklaşımı

The importance of producing safe and high-quality food is on the rise, and developing durum wheat varieties with low aluminum content is crucial in meeting this demand. Breeders can achieve this goal by developing new varieties that are more resistant to aluminum uptake. To reach this purpose, aluminum levels in a diverse collection of durum wheat genotypes were evaluated, including Turkish-released cultivars and local landraces, by using inductively coupled plasma mass spectrometry was used. The results revealed that genotypes ranged from 0.9 to 24.6 mg kg-1, with an average of 3.31 mg kg-1, while 93.1% of them had a low content of ≤ 5 mg kg-1. A genome-wide association study is a robust method for uncovering genetic variations linked to specific traits. In this study, two marker-trait associations were identified on chromosomes 2A and 3A, which explained a phenotypic variation of 14 and 71%. These findings highlight the need for continued monitoring to ensure safe and healthy food for consumers and suggest that collaborative genome-wide association studies and marker-assisted selection can accelerate the development of new durum wheat varieties with reduced aluminum levels. However, further research is necessary to confirm and validate the genetic factors contributing to aluminum content variation among different durum wheat genotypes, although the study's methodology was robust.

From Grain to Genome: Investigating Arsenic Levels in Triticum turgidum ssp durum Desf. Using GWAS

Producing safe and high-quality food is increasingly important, and developing durum wheat varieties with low toxicity is crucial to meeting this demand. Durum wheat breeders can achieve this goal by developing new varieties that are either more resistant to arsenic uptake or better adapted to grow in areas with high arsenic levels. High levels of arsenic can pose serious health hazards, which makes it critical to evaluate the arsenic levels. Therefore, this study evaluated the arsenic levels in diverse durum wheat genotypes, including Turkish-released cultivars and local landraces. The results showed that all genotypes had significantly low and non-toxic levels of arsenic, with an average concentration of 5.24 μg/kg. These concentrations were much lower than the minimum reported in numerous published research studies and well below the risky international standard limits for durum wheat grain (0.1 mg/kg). The study also identified two significant marker-trait associations linked to arsenic contents located on chromosomes 4A and 7B, which explained 11-17% of the phenotypic variation. These findings provide valuable insights into the arsenic levels in durum wheat genotypes and highlight the need for ongoing monitoring to ensure safe and healthy food for consumers. By conducting collaborative genome-wide association studies and employing marker-assisted selection, durum wheat breeders can accelerate the creation of new varieties that have reduced arsenic levels by identifying alleles linked to arsenic content. This study emphasizes the importance of developing low-toxicity durum wheat varieties to ensure the safety and quality of our food supply. The findings can inform breeding programs to develop such varieties and contribute to sustainable agriculture. While the study’s methodology was robust, further research is necessary to confirm and validate the genetic factors contributing to variation in arsenic content among different durum wheat genotypes.

Federated generalized linear mixed models for collaborative genome-wide association studies

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to correct for population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of dMEGA are demonstrated through simulated and real datasets. dMEGA is publicly available at https://github.com/Li-Wentao/dMEGA.

Genetic Susceptibility in Understanding of Pancreatic Ductal Adenocarcinoma Risk: A Decade-Long Effort of the PANDORA Consortium.

Pancreatic cancer, a complex disease, emerges as a severe health problem worldwide and it exhibits a poor prognosis and high mortality. Risk factors associated with sporadic pancreatic cancer remain poorly understood, even less is known about disease prognosis due to its rapid progression. The PANcreatic Disease ReseArch (PANDoRA) consortium, of which the authors are members, was established to coordinate the efforts of different research groups to uncover new genetic factors for pancreatic cancer risk, response to treatment, and patient survival. PANDoRA consortium has contributed to the identification of several low-penetrance risk loci for the disease both by candidate variants approach and genome-wide association studies, including those in cell-cycle and DNA damage response, telomere homeostasis, SCL and ABC transporters, ABO locus variability, mitochondrial metabolism and it participated on collaborative genome-wide association study approach and implementation of a search for functional-based pancreatic cancer risk loci and long noncoding RNAs. Complex studies covering genetic, environmental and microenvironmental factors in the pancreatic cancer onset, progression and its prognosis are warranted.

Federated Generalized Linear Mixed Models for Collaborative Genome-Wide Association Studies

Federated Generalized Linear Mixed Models for Collaborative Genome-Wide Association Studies

Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

Abstract Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

Privacy-preserving construction of generalized linear mixed model for biomedical computation.

MotivationThe generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation–Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects.ResultsOur collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package.Availability and implementationThe software is released in open source at https://github.com/huthvincent/cGLMM.Supplementary information Supplementary data are available at Bioinformatics online.

Sketching algorithms for genomic data analysis and querying in a secure enclave.

Genome-wide association studies (GWAS), especially on rare diseases, may necessitate exchange of sensitive genomic data between multiple institutions. Since genomic data sharing is often infeasible due to privacy concerns, cryptographic methods, such as secure multiparty computation (SMC) protocols, have been developed with the aim of offering privacy-preserving collaborative GWAS. Unfortunately, the computational overhead of these methods remain prohibitive for human-genome-scale data. Here we introduce SkSES (https://github.com/ndokmai/sgx-genome-variants-search), a hardware-software hybrid approach for privacy-preserving collaborative GWAS, which improves the running time of the most advanced cryptographic protocols by two orders of magnitude. The SkSES approach is based on trusted execution environments (TEEs) offered by current-generation microprocessors-in particular, Intel's SGX. To overcome the severe memory limitation of the TEEs, SkSES employs novel 'sketching' algorithms that maintain essential statistical information on genomic variants in input VCF files. By additionally incorporating efficient data compression and population stratification reduction methods, SkSES identifies the top k genomic variants in a cohort quickly, accurately and in a privacy-preserving manner.

In Retrospect: A decade of shared genomic associations.

A paper that analysed genetic variants in 14,000 people to identify disease-associated regions set the standard for collaborative genome-wide association studies and provided methodological advances whose effects are still felt today.

Genetics of diabetic nephropathy: a long road of discovery.

The global prevalence of diabetic nephropathy is rising in parallel with the increasing incidence of diabetes in most countries. Unfortunately, up to 40% of persons diagnosed with diabetes may develop kidney complications. Diabetic nephropathy is associated with substantially increased risks of cardiovascular disease and premature mortality. An inherited susceptibility to diabetic nephropathy exists, and progress is being made unravelling the genetic basis for nephropathy thanks to international research collaborations, shared biological resources and new analytical approaches. Multiple epidemiological studies have highlighted the clinical heterogeneity of nephropathy and the need for better phenotyping to help define important subgroups for analysis and increase the power of genetic studies. Collaborative genome-wide association studies for nephropathy have reported unique genes, highlighted novel biological pathways and suggested new disease mechanisms, but progress towards clinically relevant risk prediction models for diabetic nephropathy has been slow. This review summarises the current status, recent developments and ongoing challenges elucidating the genetics of diabetic nephropathy.

The current progress and future prospects of personalized radiogenomic cancer study.

During the last twenty years, mounting studies have supported the hypothesis that there is a genetic component that plays an important role in clinically observed variability in individual tissue/organ toxicity after radiotherapy. We propose the term “Personalized Radiogenomics” for the translational study of individual genetic variations that may associate with or contribute to the responses of tissues to radiation therapy used in the treatment of all types of cancer. The missions of personalized radiogenomic research are 1) to reveal the related genes, proteins, and biological pathways responsible for non-tumor or tumor tissue toxicity resulting from radiotherapy that could be targeted with radio-sensitizing and/or radio-protective agents, and 2) to identify specific genetic markers that can be used in risk prediction and evaluation models before and after clinical cancer surgery. For the members of the Terry Fox Cancer Research Lab in China Medical University and Hospital, the long-term goal is to develop SNP-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. Worldwide, the field has evolved over the last two decades in parallel with rapid advances in genetic and genomic technology, moving step by step from narrowly focused candidate gene studies to large-scale, collaborative genome-wide association studies. This article will summarize the candidate gene association studies published so far from the Terry Fox Cancer Research Lab as well as worldwide on the risk of radiation-related cancers and highlight some wholegenome association studies showing feasibility in fulfilling the dream of personalized radiogenomic cancer therapy.

Radiogenomics: the search for genetic predictors of radiotherapy response.

'Radiogenomics' is the study of genetic variation associated with response to radiotherapy. Radiogenomics aims to uncover the genes and biologic pathways responsible for radiotherapy toxicity that could be targeted with radioprotective agents and; identify genetic markers that can be used in risk prediction models in the clinic. The long-term goal of the field is to develop single nucleotide polymorphism-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. The field has evolved over the last two decades in parallel with advances in genomics, moving from narrowly focused candidate gene studies to large, collaborative genome-wide association studies. Several confirmed genetic variants have been identified and the field is making progress toward clinical translation.

Genetics of Schizophrenia and other Psychotic Disorders

Research into the genetic basis of schizophrenia is advancing rapidly. This review gives a broad overview of results from successive phases of studies in this field, linking these with recent findings and likely future research directions. Among recent findings, large-scale epidemiological studies based on Scandinavian population registers, have provided further evidence of substantial heritability and evidence that a wide range of psychotic and non-psychotic disorders partly share genetic risk factors with schizophrenia. In molecular genetics, large collaborative genomewide association studies (GWAS) are providing evidence of common risk variants, each of small effect, and many more variants are likely to be found as samples sizes increase further. A range of rarer chromosomal copy number variants (CNVs) have been associated with schizophrenia, and both GWAS and CNV studies have provided molecular evidence of genetic overlap between schizophrenia and other disorders. There is increasing interest in phenotypes beyond diagnosis, including further clinical variables and endophenotypes. Next-generation sequencing studies are beginning, with the potential for fast, inexpensive sequencing of the whole genome in large samples, and there is an increasing focus on the functional effects of the candidate risk variants that are being identified.

Genetic variants conferring susceptibility to Alzheimer’s disease in the general population; do they also predispose to dementia in Down’s syndrome

BackgroundDown’s syndrome (DS) is caused by either complete or partial triplication of chromosome 21, affecting approximately 1/1000 live births, and it is widely accepted that individuals with DS are more likely to develop dementia of Alzheimer’s disease (DAD) compared with the general population. Recent collaborative genome-wide association studies of large case control data sets of individuals with and without Alzhemier’s disease (AD) have revealed new risk variants for dementia, as well as confirming previously identified risk variants. In this study, nine AD-derived SNPs, near or within the CR1 (rs3818361), BIN1 (rs744373), CD2AP (rs9349407), EPHA1 (rs11767557), CLU (rs1532278), MS4A6A/4A (rs610932), PICALM (rs561655), ABCA7 (rs3764650) and CD33 (rs3865444) genes were genotyped in 295 individuals with DS.ResultsThere were no significant associations between these nine GWAS-derived SNPs and DAD in British Caucasian individuals with DS. Interestingly the CR1 rs3818361 variant appeared to be associated with mortality in our cohort, particularly in the subjects without dementia. To our knowledge, this is the first time that this variant has been implicated as a determinant of mortality and the finding warrants further investigation in other cohorts with DS.ConclusionsThis study shows negative associations of nine AD-derived SNPs with DAD in DS. This may be due to the modest size of our cohort, which may indicate that our study is insufficiently powered to pick up such associations. We cannot conclusively exclude a role for these SNPs in DAD in DS. Clearly, efforts to investigate genetic variants with small effects on disease risk require a much larger cohort of individuals with DS. In fact, we hypothesize that a sample size of 4465 individuals with DS would be needed to determine the role in DAD in DS of the nine AD-derived SNPs investigated in this study. We therefore recommend that all national and international clinics with access to individuals with DS should contribute DNA samples to form DS consortia.

Seven new loci associated with age-related macular degeneration.

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate understanding of AMD biology and help design new therapies, we executed a collaborative genomewide association study, examining >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 genomic loci associated with AMD with p<5×10−8 and enriched for genes involved in regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include 7 loci reaching p<5×10−8 for the first time, near the genes COL8A1/FILIP1L, IER3/DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9/MIR548A2, and B3GALTL. A genetic risk score combining SNPs from all loci displayed similar good ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

Genome-Wide Association Analysis of Blood Biomarkers in Chronic Obstructive Pulmonary Disease

A genome-wide association study (GWAS) for circulating chronic obstructive pulmonary disease (COPD) biomarkers could identify genetic determinants of biomarker levels and COPD susceptibility. To identify genetic variants of circulating protein biomarkers and novel genetic determinants of COPD. GWAS was performed for two pneumoproteins, Clara cell secretory protein (CC16) and surfactant protein D (SP-D), and five systemic inflammatory markers (C-reactive protein, fibrinogen, IL-6, IL-8, and tumor necrosis factor-α) in 1,951 subjects with COPD. For genome-wide significant single nucleotide polymorphisms (SNPs) (P < 1 × 10(-8)), association with COPD susceptibility was tested in 2,939 cases with COPD and 1,380 smoking control subjects. The association of candidate SNPs with mRNA expression in induced sputum was also elucidated. Genome-wide significant susceptibility loci affecting biomarker levels were found only for the two pneumoproteins. Two discrete loci affecting CC16, one region near the CC16 coding gene (SCGB1A1) on chromosome 11 and another locus approximately 25 Mb away from SCGB1A1, were identified, whereas multiple SNPs on chromosomes 6 and 16, in addition to SNPs near SFTPD, had genome-wide significant associations with SP-D levels. Several SNPs affecting circulating CC16 levels were significantly associated with sputum mRNA expression of SCGB1A1 (P = 0.009-0.03). Several SNPs highly associated with CC16 or SP-D levels were nominally associated with COPD in a collaborative GWAS (P = 0.001-0.049), although these COPD associations were not replicated in two additional cohorts. Distant genetic loci and biomarker-coding genes affect circulating levels of COPD-related pneumoproteins. A subset of these protein quantitative trait loci may influence their gene expression in the lung and/or COPD susceptibility. Clinical trial registered with www.clinicaltrials.gov (NCT 00292552).

Perspectives on the Use of Multiple Sclerosis Risk Genes for Prediction

ObjectiveA recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.Materials and MethodsGenotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.ResultsIndividuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02–3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.ConclusionAlthough new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.

Identification of FGF7 as a novel susceptibility locus for chronic obstructive pulmonary disease

RationaleTraditional genome-wide association studies (GWASs) of large cohorts of subjects with chronic obstructive pulmonary disease (COPD) have successfully identified novel candidate genes, but several other plausible loci do not meet...