Genotype-Tissue Expression Project Research Articles

First genome-wide association study on rocuronium dose requirements shows association with SLCO1A2

BackgroundRocuronium, a common neuromuscular blocking agent, is mainly excreted unchanged in urine (10–25%) and bile (>70%). Age, sex, liver blood flow, smoking, medical conditions, and ethnic background can affect its pharmacological actions. However, reasons for the wide variation in rocuronium requirements are mostly unknown. We hypothesised that pharmacogenetic factors might explain part of the variation. MethodsOne thousand women undergoing surgery for breast cancer were studied. Anaesthesia was maintained with propofol (50–100 μg kg−1 min−1) and remifentanil (0.05–0.25 μg kg−1 min−1). Neuromuscular block was maintained with rocuronium to keep the train-of-four ratio at 0–10%. DNA was extracted from peripheral blood and genotyped with a next-generation genotyping array. The genome-wide association study (GWAS) was conducted using an additive linear regression model with PLINK software. The FINEMAP tool and data from the Genotype-Tissue Expression project v8 were utilised to study the locus further. ResultsThe final patient population comprised 918 individuals. Of the clinical variables tested, age, BMI, ASA physical status, and total dose of propofol correlated significantly (all P<0.001) with the rocuronium dose in a linear regression model. The GWAS highlighted one genome-wide significant locus in chromosome 12. The single-nucleotide polymorphisms (SNPs) with the most significant evidence of association were located in or near SLCO1A2. The two top SNPs, rs7967354 (P=5.3e−11) and rs11045995 (P=1.4e−10), and the clinical variables accounted for 41% of the variability in rocuronium dosage. ConclusionsGenetic variation in the gene SLCO1A2, encoding OATP1A2, an uptake transporter, accounted for 4% of the variability in rocuronium consumption. The underlying mechanism remains unknown.

CCmed: cross-condition mediation analysis for identifying replicable trans-associations mediated by cis-gene expression.

Trans-acting expression quantitative trait loci (eQTLs) collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are often individually weak. A large proportion of genetic effects on distal genes are mediated through cis-gene expression. Cis-association (between SNP and cis-gene) and gene-gene correlation conditional on SNP genotype could establish trans-association (between SNP and trans-gene). Both cis-association and gene-gene conditional correlation have effects shared across relevant tissues and conditions, and trans-associations mediated by cis-gene expression also have effects shared across relevant conditions. We proposed a Cross-Condition Mediation analysis method (CCmed) for detecting cis-mediated trans-associations with replicable effects in relevant conditions/studies. CCmed integrates cis-association and gene-gene conditional correlation statistics from multiple tissues/studies. Motivated by the bimodal effect-sharing patterns of eQTLs, we proposed two variations of CCmed, CCmedmost and CCmedspec for detecting cross-tissue and tissue-specific trans-associations, respectively. We analyzed data of 13 brain tissues from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-mediated trans-associations across brain tissues, many of which showed evidence of trans-association in two replication studies. We also identified trans-genes associated with schizophrenia loci in at least two brain tissues. CCmed software is available at http://github.com/kjgleason/CCmed. Supplementary data are available at Bioinformatics online.

Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing

AbstractGenome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3.

Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10-8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82-0.93; former smokers 1.00, 95% CI, 0.91-1.07; current smokers 1.25, 95% CI 1.12-1.40, P interaction = 3.08 × 10-9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r 2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. SIGNIFICANCE: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.

Multi-trait association studies discover pleiotropic loci between Alzheimer\u2019s disease and cardiometabolic traits

BackgroundIdentification of genetic risk factors that are shared between Alzheimer’s disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Previous epidemiological correlations observed between cardiometabolic traits and AD led us to assess the pleiotropy between these traits.MethodsWe performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. For each of these loci, we performed colocalization with Genotype-Tissue Expression (GTEx) project expression quantitative trait loci (eQTL) to identify candidate causal genes.ResultsWe identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the gene DOCK4 and is potentially implicated in its alternative splicing. Colocalization with GTEx eQTL data identified additional candidate genes for the loci we detected, including ACE, the target of the hypertensive drug class of ACE inhibitors. We found that the allele associated with decreased ACE expression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic.ConclusionOur results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.

Allele-specific alternative splicing and its functional genetic variants in human tissues.

Alternative splicing is an RNA processing mechanism that affects most genes in human, contributing to disease mechanisms and phenotypic diversity. The regulation of splicing involves an intricate network of cis-regulatory elements and trans-acting factors. Due to their high sequence specificity, cis-regulation of splicing can be altered by genetic variants, significantly affecting splicing outcomes. Recently, multiple methods have been applied to understanding the regulatory effects of genetic variants on splicing. However, it is still challenging to go beyond apparent association to pinpoint functional variants. To fill in this gap, we utilized large-scale data sets of the Genotype-Tissue Expression (GTEx) project to study genetically modulated alternative splicing (GMAS) via identification of allele-specific splicing events. We demonstrate that GMAS events are shared across tissues and individuals more often than expected by chance, consistent with their genetically driven nature. Moreover, although the allelic bias of GMAS exons varies across samples, the degree of variation is similar across tissues versus individuals. Thus, genetic background drives the GMAS pattern to a similar degree as tissue-specific splicing mechanisms. Leveraging the genetically driven nature of GMAS, we developed a new method to predict functional splicing-altering variants, built upon a genotype-phenotype concordance model across samples. Complemented by experimental validations, this method predicted >1000 functional variants, many of which may alter RNA-protein interactions. Lastly, 72% of GMAS-associated SNPs were in linkage disequilibrium with GWAS-reported SNPs, and such association was enriched in tissues of relevance for specific traits/diseases. Our study enables a comprehensive view of genetically driven splicing variations in human tissues.

Blood-derived mitochondrial DNA copy number is associated with gene expression across multiple tissues and is predictive for incident neurodegenerative disease.

Mitochondrial DNA copy number (mtDNA-CN) is a proxy for mitochondrial function and is associated with aging-related diseases. However, it is unclear how mtDNA-CN measured in blood can reflect diseases that primarily manifest in other tissues. Using the Genotype-Tissue Expression Project, we interrogated relationships between mtDNA-CN measured in whole blood and gene expression from whole blood and 47 additional tissues in 419 individuals. mtDNA-CN was significantly associated with expression of 700 genes in whole blood, including nuclear genes required for mtDNA replication. Significant enrichment was observed for splicing and ubiquitin-mediated proteolysis pathways, as well as target genes for the mitochondrial transcription factor NRF1. In nonblood tissues, there were more significantly associated genes than expected in 30 tissues, suggesting that global gene expression in those tissues is correlated with blood-derived mtDNA-CN. Neurodegenerative disease pathways were significantly associated in multiple tissues, and in an independent data set, the UK Biobank, we observed that higher mtDNA-CN was significantly associated with lower rates of both prevalent (OR = 0.89, CI = 0.83; 0.96) and incident neurodegenerative disease (HR = 0.95, 95% CI = 0.91;0.98). The observation that mtDNA-CN measured in blood is associated with gene expression in other tissues suggests that blood-derived mtDNA-CN can reflect metabolic health across multiple tissues. Identification of key pathways including splicing, RNA binding, and catalysis reinforces the importance of mitochondria in maintaining cellular homeostasis. Finally, validation of the role of mtDNA CN in neurodegenerative disease in a large independent cohort study solidifies the link between blood-derived mtDNA-CN, altered gene expression in multiple tissues, and aging-related disease.

Making a family decision to donate the brain for genomic research: lessons from the genotype-tissue expression project (GTEx).

This study sheds light on the attitudes and circumstances that influence decisions by families to donate the brain of a deceased family member for research. This study, a part of the Genotype-Tissue Expression (GTEx) project, interviewed families of patients who had authorized organ and/or tissue donation for transplantation. A total of 384 family decision makers (FDMs) who decided to donate organs and/or tissues for transplantation were also asked to donate to GTEx. Of these, 297 families were asked to donate their loved one's whole brain and 87 families responded to a hypothetical request for brain donation. The decision to donate the brain to GTEx, actually or hypothetically, was the major outcome measure. The majority of the FDMs would choose to donate the brain, 78%. Unwillingness to donate the brain was associated with four attitudes: (1) the FDM unwillingness to donate their own tissues for research (OR 1.91, 95% CI .67 to 2.96; p = .05), (2) concern with potential for-profit use of tissues (OR 2.12, 95% CI 1.2 to 3.7; p = .008), (3) reported squeamishness about tissue donation (OR 1.34, 95% CI 1.1 to 1.7; p = .006), and (4) belief that FDMs should have a say in how the donated tissues are used (OR 1.36, 95% CI 1.13 to 1.5; p = .01). Organ and tissue donors may present a plenteous source of brains for research. Family concerns about tissue use and collection should be addressed by requesters.

Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity.

Various characteristics of cancers exhibit tissue specificity, including lifetime cancer risk, onset age, and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels. Here, we study the role of synthetic lethality in cancer risk. Analyzing normal tissue transcriptomics data in the Genotype-Tissue Expression project, we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. Consistently, more cSL gene pairs become up-regulated in cells treated by carcinogens and throughout premalignant stages in vivo. We also show that the tissue specificity of numerous tumor suppressor genes is associated with the expression of their cSL partner genes across normal tissues. Overall, our findings support the possible role of synthetic lethality in tumorigenesis.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

ABSTRACT Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

Processing and Analysis of RNA-seq Data from Public Resources.

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

Integrating GWAS and eQTL to predict genes and pathways for non-syndromic cleft lip with or without palate.

To explore susceptibility genes and pathways for non-syndromic cleft lip with or without cleft palate (NSCL/P). Two genome-wide association studies (GWAS) datasets, including 858 NSCL/P cases and 1,248 controls, were integrated with expression quantitative trait loci (eQTL) dataset identified by Genotype-Tissue Expression (GTEx) project in whole-blood samples. The expression of the candidate genes in mouse orofacial development was inquired from FaceBase. Protein-protein interaction (PPI) network was visualized to identify protein functions. Go and KEGG pathway analyses were performed to explore the underlying risk pathways. A total of 233 eQTL single-nucleotide polymorphisms (SNPs) in 432 candidate genes were identified to be associated with the risk of NSCL/P. One hundred and eighty-three susceptible genes were expressed in mouse orofacial development according to FaceBase. PPI network analysis highlighted that these genes involved in ubiquitin-mediated proteolysis (KCTD7, ASB1, UBOX5, ANAPC4) and DNA synthesis (XRCC3, RFC3, KAT5, RHNO1) were associated with the risk of NSCL/P. GO and KEGG pathway analyses revealed that the fatty acid metabolism pathway (ACADL, HSD17B12, ACSL5, PPT1, MCAT) played an important role in the development of NSCL/P. Our results identified novel susceptibility genes and pathways associated with the development of NSCL/P.

Transcriptional Differences for COVID-19 Disease Map Genes between Males and Females Indicate a Different Basal Immunophenotype Relevant to the Disease.

Worldwide COVID-19 epidemiology data indicate differences in disease incidence amongst sex and gender demographic groups. Specifically, male patients are at a higher death risk than female patients, and the older population is significantly more affected than young individuals. Whether this difference is a consequence of a pre-existing differential response to the virus, has not been studied in detail. We created DeCovid, an R shiny app that combines gene expression (GE) data of different human tissue from the Genotype-Tissue Expression (GTEx) project along with the COVID-19 Disease Map and COVID-19 related pathways gene collections to explore basal GE differences across healthy demographic groups. We used this app to study differential gene expression of COVID-19 associated genes in different age and sex groups. We identified that healthy women show higher expression-levels of interferon genes. Conversely, healthy men exhibit higher levels of proinflammatory cytokines. Additionally, young people present a stronger complement system and maintain a high level of matrix metalloproteases than older adults. Our data suggest the existence of different basal immunophenotypes amongst different demographic groups, which are relevant to COVID-19 progression and may contribute to explaining sex and age biases in disease severity. The DeCovid app is an effective and easy to use tool for exploring the GE levels relevant to COVID-19 across demographic groups and tissues.

GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions.

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Multi-Omic Based Antigen Discovery for the Immunotherapy of Pediatric Acute T Cell Lymphoblastic Leukemia

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is a high-risk disease due to treatment related complications and poor prognosis of patients with relapsed disease. Immunotherapy with monoclonal antibodies (MAbs) and/or chimeric antigen receptor (CAR) T-cells for T-ALL is limited by identification of tumor specific target antigens. Differential expression is necessary to prevent on-target/off-tumor toxicities and fratricide of activated T-cells. Targeting multiple antigens can bypass immune escape and result in improved T-cell effector function, since antigen density correlates with T-cell activation. Here we designed a pipeline (Figure 1) to identify unique surface antigens expressed in T-ALL using proteomic and transcriptomic analyses followed by flow cytometry validation, and functional studies with CAR T cells targeting the identified antigens. We generated an Illumina total stranded RNAseq library from healthy donor myeloid and lymphoid cells of bone marrow, peripheral blood and cord blood (N= 116). We compared data to 265 St. Jude pediatric T-ALL samples and against 53 normal tissue expression data from the GTEx (Genotype-Tissue Expression) project. To analyze the T-cell surface proteome, we isolated plasma membrane fractions from 11 samples including healthy T-cells and T-ALL cell lines using a differential centrifugation-based method. The purity of the plasma membrane fraction was confirmed by western blot. Na+/K+ ATPase and GAPDH were used as controls for the plasma membrane and cytosolic fractions respectively. Following plasma membrane enrichment, the membrane proteins were applied for proteomic analysis using an advanced TMT-L/LC-MS/MS pipeline, and the acquired proteomic data were further processed via the JUMP software suite. 997 unique proteins were quantified from the membrane fractions. Integrated analysis the transcriptomic and proteomic datasets showed significant correlation and yielded a list of candidate genes, which were validated by flow cytometry on a panel of T-ALL cell lines (CCRF, RPMI8402, and MOLT3) and resting and activated T-cells from healthy donors. We identified GRP78 as one of the differentially expressed cell surface antigens and further confirmed its expression on additional T-ALL cell lines (KE37, PF382, PEER, CEMC7) and 3 PDX samples. Finally, we generated GRP78-CAR T cells and demonstrate that GRP78-CAR T cells recognize and kill GRP78+ T-ALL cells and have potent antitumor activity in xenograft and PDX models. We have established an unbiased pipeline to identify differentially expressed antigens on the cell surface of T-ALL blasts and created a healthy tissue RNAseq library. The results from our analyses are encouraging and interrogation of our pipeline has yielded differentially expressed immunotherapy targets for the treatment of relapsed refractory T-ALL. Our results highlight the importance of integrated surface proteomics and transcriptomics analysis. Figure 1: Outline of strategy for target selection: Figure Disclosures Hebbar: St. Jude: Patents &amp; Royalties. Epperly:St. Jude: Patents &amp; Royalties. Gottschalk:Inmatics and Tidal: Membership on an entity's Board of Directors or advisory committees; TESSA Therapeutics: Other: research collaboration; Patents and patent applications in the fields of T-cell &amp; Gene therapy for cancer: Patents &amp; Royalties; Merck and ViraCyte: Consultancy. Mullighan:AbbVie, Inc.: Research Funding; Illumina: Consultancy, Honoraria, Speakers Bureau; Pfizer: Honoraria, Research Funding, Speakers Bureau. Velasquez:Rally! Foundation: Membership on an entity's Board of Directors or advisory committees; St. Jude: Patents &amp; Royalties.

HeRA: an atlas of enhancer RNAs across human tissues.

Enhancer RNA (eRNA) is a type of long non-coding RNA transcribed from DNA enhancer regions. Despite critical roles of eRNA in gene regulation, the expression landscape of eRNAs in normal human tissue remains unexplored. Using numerous samples from the Genotype-Tissue Expression project, we characterized 45 411 detectable eRNAs and identified tens of thousands of associations between eRNAs and traits, including gender, race, and age. We constructed a co-expression network to identify millions of putative eRNA regulators and target genes across different tissues. We further constructed a user-friendly data portal, Human enhancer RNA Atlas (HeRA, https://hanlab.uth.edu/HeRA/). In HeRA, users can search, browse, and download the eRNA expression profile, trait-related eRNAs, and eRNA co-expression network by searching the eRNA ID, gene symbol, and genomic region in one or multiple tissues. HeRA is the first data portal to characterize eRNAs from 9577 samples across 54 human tissues and facilitates functional and mechanistic investigations of eRNAs.

RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.

Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. https://github.com/mwgrassgreen/RobNorm. Supplementary data are available at Bioinformatics online.

Genetic variation in GC and CYP2R1 affects 25-hydroxyvitamin D concentration and skeletal parameters: A genome-wide association study in 24-month-old Finnish children

Vitamin D is important for normal skeletal homeostasis, especially in growing children. There are no previous genome-wide association (GWA) studies exploring genetic factors that influence vitamin D metabolism in early childhood. We performed a GWA study on serum 25-hydroxyvitamin D (25(OH)D) and response to supplementation in 761 healthy term-born Finnish 24-month-old children, who participated in a randomized clinical trial comparing effects of 10 μg and 30 μg of daily vitamin D supplementation from age 2 weeks to 24 months. Using the Illumina Infinium Global Screening Array, which has been optimized for imputation, a total of 686085 markers were genotyped across the genome. Serum 25(OH)D was measured at the end of the intervention at 24 months of age. Skeletal parameters reflecting bone strength were determined at the distal tibia at 24 months using peripheral quantitative computed tomography (pQCT) (data available for 648 children). For 25(OH)D, two strong GWA signals were identified, localizing to GC (Vitamin D binding protein) and CYP2R1 (Vitamin D 25-hydroxylase) genes. The GWA locus comprising the GC gene also associated with response to supplementation. Further evidence for the importance of these two genes was obtained by comparing association signals to gene expression data from the Genotype-Tissue Expression project and performing colocalization analyses. Through the identification of haplotypes associated with low or high 25(OH)D concentrations we used a Mendelian randomization approach to show that haplotypes associating with low 25(OH)D were also associated with low pQCT parameters in the 24-month-old children. In this first GWA study on 25(OH)D in this age group we show that already at the age of 24 months genetic variation influences 25(OH)D concentrations and determines response to supplementation, with genome-wide significant associations with GC and CYP2R1. Also, the dual association between haplotypes, 25(OH)D and pQCT parameters gives support for vertical pleiotropy mediated by 25(OH)D.

Association study between genetic variants in retinol metabolism pathway genes and prostate cancer risk

BackgroundEvidence suggests that serum retinol level is associated with prostate cancer risk, but the association between genetic variants in the retinol metabolism pathway genes and prostate cancer risk remains unclarified.MethodsSingle‐nucleotide polymorphisms (SNPs) in 31 genes in the retinol metabolism pathway were genotyped to evaluate the association with prostate cancer risk in 4,662 cases and 3,114 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The gene expression analysis was evaluated using data from the Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. Data from the Genotype‐Tissue Expression (GTEx) project dataset were utilized to perform the expression quantitative trait loci (eQTL) analysis.ResultsTwo SNPs were significantly associated with prostate cancer risk [rs1330286 in ALDH1A1: odds ratio (OR) = 0.88, 95% confidence interval (CI) = 0.83‐0.94, p = 2.45 × 10−4; rs4646653 in ALDH1A3: OR = 1.17, 95% CI =1.07‐1.27, p = 4.33 × 10−4]. Moreover, the mRNA level of ALDH1A3 was significantly higher in prostate cancer tissues than in normal tissues in both TCGA datasets and GEO datasets (p = 1.63 × 10−12 and p = 4.33 × 10−2, respectively). rs1330286 was an eQTL of ALDH1A1 (P = 2.90 × 10−3).ConclusionOur findings highlight that genetic variants in retinol metabolism pathway genes are associated with prostate cancer risk.

A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1, and PGAP3) and six novel genes not yet reported for pancreatic cancer risk [6q27: SFT2D1 OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: MTMR6 OR (95% CI), 0.78 (0.70-0.88); 14q24.3: ACOT2 OR (95% CI), 1.35 (1.17-1.56); 17q12: STARD3 OR (95% CI), 6.49 (2.96-14.27); 17q21.1: GSDMB OR (95% CI), 1.94 (1.45-2.58); and 20p13: ADAM33 OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1, and PGAP3) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.