Human Genome-wide Association Studies Data Research Articles

Monkey multi-organ cell atlas exposed to estrogen

Abstract Awareness of estrogen’s effects on health is broadening rapidly. The effects of long-term high levels of estrogen on the body involve multiple organs. Here, we used both single-cell chromatin accessibility and RNA sequencing data to analyze the potential effect of estrogen on major organs. The integrated cell map enabled in-depth dissection and comparison of molecular dynamics, cell-type compositions, and cellular heterogeneity across multiple tissues and organs under estrogen stimulation. We also inferred pseudotime cell trajectories and cell–cell communications to uncover key molecular signatures underlying their cellular processes in major organs in response to estrogen. For example, estrogen could induce the differentiation of IFIT3+ neutrophils into S100A9+ neutrophils involved in the function of endosome-to-lysosome transport and the multivesicular body sorting pathway in liver tissues. Furthermore, through integration with human genome-wide association study data, we further identified a subset of risk genes during disease development that were induced by estrogen, such as AKT1 (related to endometrial cancer), CCND1 (related to breast cancer), HSPH1 (related to colorectal cancer), and COVID-19 and asthma-related risk genes. Our work uncovers the impact of estrogen on the major organs, constitutes a useful resource, and reveals the contribution and mechanism of estrogen to related diseases.

Single-cell profiling of vascular endothelial cells reveals progressive organ-specific vulnerabilities during obesity

Obesity promotes diverse pathologies, including atherosclerosis and dementia, which frequently involve vascular defects and endothelial cell (EC) dysfunction. Each organ has distinct EC subtypes, but whether ECs are differentially affected by obesity is unknown. Here we use single-cell RNA sequencing to analyze transcriptomes of ~375,000 ECs from seven organs in male mice at progressive stages of obesity to identify organ-specific vulnerabilities. We find that obesity deregulates gene expression networks, including lipid handling, metabolic pathways and AP1 transcription factor and inflammatory signaling, in an organ- and EC-subtype-specific manner. The transcriptomic aberrations worsen with sustained obesity and are only partially mitigated by dietary intervention and weight loss. For example, dietary intervention substantially attenuates dysregulation of liver, but not kidney, EC transcriptomes. Through integration with human genome-wide association study data, we further identify a subset of vascular disease risk genes that are induced by obesity. Our work catalogs the impact of obesity on the endothelium, constitutes a useful resource and reveals leads for investigation as potential therapeutic targets.

Genetic pathway analysis reveals a major role for extracellular matrix organization in inflammatory and neuropathic pain.

Chronic pain is a debilitating and poorly treated condition whose underlying mechanisms are poorly understood. Nerve injury and inflammation cause alterations in gene expression in tissues associated with pain processing, supporting molecular and cellular mechanisms that maintain painful states. However, it is not known whether transcriptome changes can be used to reconstruct a molecular pathophysiology of pain. In the current study, we identify molecular pathways contributing to chronic pain states through the analysis of global changes in the transcriptome of dorsal root ganglia, spinal cord, brain, and blood in mouse assays of nerve injury- and inflammation-induced pain. Comparative analyses of differentially expressed genes identified substantial similarities between 2 animal pain assays and with human low-back pain. Furthermore, the extracellular matrix (ECM) organization has been found the most commonly regulated pathway across all tested tissues in the 2 animal assays. Examination of human genome-wide association study data sets revealed an overrepresentation of differentially expressed genes within the ECM organization pathway in single nucleotide polymorphisms most strongly associated with human back pain. In summary, our comprehensive transcriptomics analysis in mouse and human identified ECM organization as a central molecular pathway in the development of chronic pain.

Abstract 034: Leveraging Mouse Liver Co-expression Networks and Human Lipid Gwas Data to Identify Cholesterol Metabolism Genes

Genetic factors play an important role in contributing to variation in plasma lipid levels across the human population. Large genome-wide association studies (GWAS) in humans have identified more than 100 loci significantly associated with plasma levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), and triglycerides. To prioritize genes that are involved in cholesterol metabolism, we developed a systematic approach to leverage genome-wide liver transcriptomic and proteomic data from multiple mouse reference populations along with human lipid GWAS data. We constructed global co-expression networks from twelve distinct mouse liver datasets, encompassing more than 800 unique mice and identified a conserved module of genes highly enriched for cholesterol biosynthesis. Based on replication across datasets and presence in transcript and protein, we prioritized 112 unique genes. Intersection of these 112 prioritized genes with human GWAS data for LDL, HDL, TC, and triglycerides identified 54 genes to be within 100 kilobases of a significant or suggestive significant single nucleotide polymorphism (SNP). Out of the 54 identified genes that overlap with human GWAS data, 29 have well documented biological roles in cholesterol metabolism, such as LDLR , PCSK9 , and INSIG1 . With the 25 identified genes with no described role in cholesterol metabolism, we tested for transcriptional regulation to cholesterol and performed a functional screen by siRNA knockdown. From this analysis, we identified twelve genes that show transcriptional regulation to cholesterol levels and nine genes that are able to modulate cholesterol metabolism when targeted in vitro with siRNA. Five genes out of the 25 prioritized genes show both transcriptional regulation to cholesterol and ability to modulate cholesterol metabolism in vitro. One of these genes, Sestrin1 , we validate in vivo and in vitro as a modulator of cholesterol metabolism. Collectively, through a systematic approach we have identified 25 highly prioritized genes that have no documented role in cholesterol metabolism, five genes are transcriptionally regulated by cholesterol and influence cholesterol metabolism.

Leveraging mouse liver co‐expression networks and human lipid GWAS data to identify and validate cholesterol metabolism genes

Genetic factors play an important role in contributing to variation in plasma lipid levels across the human population. Large genome‐wide association studies (GWAS) in humans have identified more than 100 loci significantly associated with plasma levels of low‐density lipoprotein (LDL) cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol (TC). To prioritize genes that are involved in cholesterol metabolism we developed a systematic approach to leverage genome‐wide liver transcriptomic and proteomic data from multiple mouse reference populations along with human lipid GWAS data. We constructed global co‐expression networks from 12 distinct mouse liver datasets and identified a conserved module of genes highly enriched for cholesterol biosynthetic processes. Based on replication across mutiple datasets we prioritized 112 unique genes. Intersection with human GWAS data for LDL, HDL, TC, and triglycerides identified 54 of these genes to be within 100 kilobases of a significant or suggestive significant single nucleotide polymorphism (SNP). 29 of the 54 prioritized genes have well documented biological roles in cholesterol metabolism, such as LDLR, PCSK9, and INSIG1. With the 25 identified genes with no documented role in cholesterol metabolism, we tested for sensitivity to cholesterol modulation and performed a functional screen by siRNA knockdown. From this analysis we identified, Sestrin1, as a candidate gene that is responsive to cholesterol manipulation and capable of modulating cellular cholesterol levels. Using Sestrin1 targeted mice we show that Sestrin1 heterozygous or homozygous knockout mice have elevated levels of plasma cholesterol when fed a diet enriched in cholesterol. Collectively, through a systematic approach we have identified 25 highly prioritized genes that have no documented role in cholesterol metabolism. One of these genes, Sestrin1, we validate in vivo and in vitro as a modulator of cholesterol metabolism.Support or Funding InformationBWP is supported by NIH R00‐HL123021This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

GWAB: a web server for the network-based boosting of human genome-wide association data.

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10−8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS.

Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10−8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10−10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.

Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney function

Genomewide association studies have identified numerous chronic kidney disease-associated genetic variants, but often do not pinpoint causal genes. This limitation was addressed by combining Mouse Genome Informatics withhuman genomewide association studies of kidney function. Genes for which mouse models showed abnormal renal physiology, morphology, glomerular filtration rate (GFR), or urinary albumin-to-creatinine ratio were identified from Mouse Genome Informatics. The corresponding human orthologs were then evaluated for GFR-associated single-nucleotide polymorphisms in 133,814 individuals and urinary albumin-to-creatinine ratio-associated SNPs in 54,451 individuals in genome-wide association studies meta-analysis of the CKDGen Consortium. After multiple testing corrections, significant associations with estimated GFR in humans were identified for single-nucleotide polymorphisms in 2, 7, and 17 genes causing abnormal GFR, abnormal physiology, and abnormal morphology in mice, respectively. Genes identified for abnormal kidney morphology showed significant enrichment for estimated GFR-associated single-nucleotide polymorphisms. In total, 19 genes contained variants associated with estimated GFR or the urinary albumin-to-creatinine ratio of which 16 mapped into previously reported genomewide significant loci. CYP26A1 and BMP4 emerged as novel signals subsequently validated in a large, independent study. An additional gene, CYP24A1, was discovered after conditioning on apublished nearby association signal. Thus, our novel approach to combine comprehensive mouse phenotype information with human genomewide association studies data resulted in the identification of candidate genes for kidney disease pathogenesis.

Biological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits.

While the importance of epistasis is well established, specific gene-gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein-protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene-gene interactions play in connecting genotypes and complex phenotypes in future GWAS.

Convergent functional genomics in addiction research - a translational approach to study candidate genes and gene networks.

Convergent functional genomics (CFG) is a translational methodology that integrates in a Bayesian fashion multiple lines of evidence from studies in human and animal models to get a better understanding of the genetics of a disease or pathological behavior. Here the integration of data sets that derive from forward genetics in animals and genetic association studies including genome wide association studies (GWAS) in humans is described for addictive behavior. The aim of forward genetics in animals and association studies in humans is to identify mutations (e.g. SNPs) that produce a certain phenotype; i.e. “from phenotype to genotype”. Most powerful in terms of forward genetics is combined quantitative trait loci (QTL) analysis and gene expression profiling in recombinant inbreed rodent lines or genetically selected animals for a specific phenotype, e.g. high vs. low drug consumption. By Bayesian scoring genomic information from forward genetics in animals is then combined with human GWAS data on a similar addiction-relevant phenotype. This integrative approach generates a robust candidate gene list that has to be functionally validated by means of reverse genetics in animals; i.e. “from genotype to phenotype”. It is proposed that studying addiction relevant phenotypes and endophenotypes by this CFG approach will allow a better determination of the genetics of addictive behavior.

G6PC2: A Negative Regulator of Basal Glucose-Stimulated Insulin Secretion

Elevated fasting blood glucose (FBG) is associated with increased risk for the development of type 2 diabetes and cardiovascular-associated mortality. Genome-wide association studies (GWAS) have linked polymorphisms in G6PC2 with variations in FBG and body fat, although not insulin sensitivity or glucose tolerance. G6PC2 encodes an islet-specific, endoplasmic reticulum–resident glucose-6-phosphatase catalytic subunit. A combination of in situ perfused pancreas, in vitro isolated islet, and in vivo analyses were used to explore the function of G6pc2 in mice. G6pc2 deletion had little effect on insulin sensitivity and glucose tolerance, whereas body fat was reduced in female G6pc2 knockout (KO) mice on both a chow and high-fat diet, observations that are all consistent with human GWAS data. G6pc2 deletion resulted in a leftward shift in the dose-response curve for glucose-stimulated insulin secretion (GSIS). As a consequence, under fasting conditions in which plasma insulin levels were identical, blood glucose levels were reduced in G6pc2 KO mice, again consistent with human GWAS data. Glucose-6-phosphatase activity was reduced, whereas basal cytoplasmic calcium levels were elevated in islets isolated from G6pc2 KO mice. These data suggest that G6pc2 represents a novel, negative regulator of basal GSIS that acts by hydrolyzing glucose-6-phosphate, thereby reducing glycolytic flux.

Rapid variance components–based method for whole-genome association analysis

The variance component tests used in genome-wide association studies (GWAS) including large sample sizes become computationally exhaustive when the number of genetic markers is over a few hundred thousand. We present an extremely fast variance components-based two-step method, GRAMMAR-Gamma, developed as an analytical approximation within a framework of the score test approach. Using simulated and real human GWAS data sets, we show that this method provides unbiased estimates of the SNP effect and has a power close to that of the likelihood ratio test-based method. The computational complexity of our method is close to its theoretical minimum, that is, to the complexity of the analysis that ignores genetic structure. The running time of our method linearly depends on sample size, whereas this dependency is quadratic for other existing methods. Simulations suggest that GRAMMAR-Gamma may be used for association testing in whole-genome resequencing studies of large human cohorts.

Variance component model to account for sample structure in genome-wide association studies

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.