Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci.
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ABSTRACT:
SUBMITTER: Moon CY
PROVIDER: S-EPMC8129787 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The identification of genes that modify pathological ocular phenotypes in mouse models may improve our understanding of disease mechanisms and lead to new treatment strategies. Here, we identify modifier loci affecting photoreceptor cell loss in homozygous Mfrp(rd6) mice, which exhibit a slowly progressive photoreceptor degeneration. A cohort of 63 F2 homozygous Mfrp(rd6) mice from a (B6.C3Ga-Mfrp(rd6)/J × CAST/EiJ) F1 intercross exhibited a variable number of cell bodies in the retinal outer nuclear layer at 20 weeks of age. Mice were genotyped with a panel of single nucleotide polymorphism markers, and genotypes were correlated with phenotype by quantitative trait locus (QTL) analysis to map modifier loci. A genome-wide scan revealed a statistically significant, protective candidate locus on CAST/EiJ Chromosome 1 and suggestive modifier loci on Chromosomes 6 and 11. Multiple regression analysis of a three-QTL model indicated that the modifier loci on Chromosomes 1 and 6 together account for 26% of the observed phenotypic variation, while the modifier locus on Chromosome 11 explains only an additional 4%. Our findings indicate that the severity of the Mfrp(rd6) retinal degenerative phenotype in mice depends on the strain genetic background and that a significant modifier locus on CAST/EiJ Chromosome 1 protects against Mfrp(rd6)-associated photoreceptor loss.
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Project description:Meta-analysis is an important tool in linkage analysis. The pooling of results across primary linkage studies allows greater statistical power to detect quantitative-trait loci (QTLs) and more-precise estimation of their genetic effects and, hence, yields conclusions that are stronger relative to those of individual studies. Previous methods for the meta-analysis of linkage studies have been proposed, and, although some methods address the problem of between-study heterogeneity, most methods still require linkage analysis at the same marker or set of markers across studies, whereas others do not result in an estimate of genetic variance. In this study, we present a meta-analytic procedure to evaluate evidence from several studies that report Haseman-Elston statistics for linkage to a QTL at multiple, possibly distinct, markers on a chromosome. This technique accounts for between-study heterogeneity and estimates both the location of the QTL and the magnitude of the genetic effect more precisely than does an individual study. We also provide standard errors for the genetic effect and for the location (in cM) of the QTL, using a resampling method. The approach can be applied under other conditions, provided that the various studies use the same linkage statistic.
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Project description:Identification of microRNA expression quantitative trait loci (miR-eQTL) can yield insights into regulatory mechanisms of microRNA transcription, and can help elucidate the role of microRNA as mediators of complex traits. Here we present a miR-eQTL mapping study of whole blood from 5,239 individuals, and identify 5,269 cis-miR-eQTLs for 76 mature microRNAs. Forty-nine per cent of cis-miR-eQTLs are located 300-500?kb upstream of their associated intergenic microRNAs, suggesting that distal regulatory elements may affect the interindividual variability in microRNA expression levels. We find that cis-miR-eQTLs are highly enriched for cis-mRNA-eQTLs and regulatory single nucleotide polymorphisms. Among 243 cis-miR-eQTLs that were reported to be associated with complex traits in prior genome-wide association studies, many cis-miR-eQTLs miRNAs display differential expression in relation to the corresponding trait (for example, rs7115089, miR-125b-5p and high-density lipoprotein cholesterol). Our study provides a roadmap for understanding the genetic basis of miRNA expression, and sheds light on miRNA involvement in a variety of complex traits.
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Project description:Whether in natural populations or between two unrelated members of a species, most phenotypic variation is quantitative. To analyze such quantitative traits, one must first map the underlying quantitative trait loci. Next, and far more difficult, one must identify the quantitative trait genes (QTGs), characterize QTG interactions, and identify the phenotypically relevant polymorphisms to determine how QTGs contribute to phenotype. In this work, we analyzed three Saccharomyces cerevisiae high-temperature growth (Htg) QTGs (MKT1, END3, and RHO2). We observed a high level of genetic interactions among QTGs and strain background. Interestingly, while the MKT1 and END3 coding polymorphisms contribute to phenotype, it is the RHO2 3'UTR polymorphisms that are phenotypically relevant. Reciprocal hemizygosity analysis of the Htg QTGs in hybrids between S288c and ten unrelated S. cerevisiae strains reveals that the contributions of the Htg QTGs are not conserved in nine other hybrids, which has implications for QTG identification by marker-trait association. Our findings demonstrate the variety and complexity of QTG contributions to phenotype, the impact of genetic background, and the value of quantitative genetic studies in S. cerevisiae.
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Project description:Statistical power calculations are a critical part of any study design for gene mapping. Most calculations assume that the locus of interest is biallelic. However, there are common situations in human genetics such as X-linked loci in males where the locus is haploid. The purpose of this work is to mathematically derive the biometric model for haploid loci, and to compute power for QTL mapping when the loci are haploid.We have derived the biometric model for power calculations for haploid loci and have developed software to perform these calculations. We have verified our calculations with independent mathematical methods.Our results fill a need in power calculations for QTL mapping studies. Furthermore, failure to appropriately model haploid loci may cause underestimation of power.
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Project description:Progressive supranuclear palsy is a rare parkinsonian disorder with characteristic neurofibrillary pathology consisting of hyperphosphorylated tau protein. Common variation defining the microtubule associated protein tau gene (MAPT) H1 haplotype strongly contributes to disease risk. A recent genome-wide association study (GWAS) revealed 3 novel risk loci on chromosomes 1, 2, and 3 that primarily implicate STX6, EIF2AK3, and MOBP, respectively. Genetic associations, however, rarely lead to direct identification of the relevant functional allele. More often, they are in linkage disequilibrium with the causative polymorphism(s) that could be a coding change or affect gene expression regulatory motifs. To identify any such changes, we sequenced all coding exons of those genes directly implicated by the associations in progressive supranuclear palsy cases and analyzed regional gene expression data from control brains to identify expression quantitative trait loci within 1 Mb of the risk loci. Although we did not find any coding variants underlying the associations, GWAS-associated single-nucleotide polymorphisms at these loci are in complete linkage disequilibrium with haplotypes that completely overlap with the respective genes. Although implication of EIF2AK3 and MOBP could not be fully assessed, we show that the GWAS single-nucleotide polymorphism rs1411478 (STX6) is a strong expression quantitative trait locus with significantly lower expression of STX6 in white matter in carriers of the risk allele.
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Project description:Trauma- and stress-related disorders are clinically heterogeneous and associated with substantial genetic risk. Understanding the biological origins of heterogeneity of key intermediate phenotypes such as cognition and emotion can provide novel mechanistic insights into disorder pathogenesis. Performing quantitative genetics in animal models is a tractable strategy for examining both the genetic basis of intermediate phenotypes and functional testing of candidate quantitative traits genes (QTGs). Here, existing and newly collected data were used for collaborative genome-wide mapping of cued fear acquisition and expression in 65 mouse strains from the BXD genetic reference panel. For fear acquisition, we identified a significant locus on chromosome (Chr) 10 and eight suggestive loci on Chr 2, 4, 5, 11, 13, and 15. For fear expression, we identified one significant and another highly suggestive locus on Chr 13, as well as four suggestive loci on Chr 10, 11, and X. Across these loci, 60 putative QTGs were identified. The quantitative trait locus on distal Chr 13 contained a single, highly promising gene at the location of the peak likelihood ratio statistic score. The gene, hyperpolarization-activated cyclic nucleotide-gated channel 1 (Hcn1), regulates neuronal excitability. Validation experiments using behavioral pharmacology revealed that functional Hcn channels in the basolateral amygdala are necessary for conditioned fear acquisition and expression. Hcn1, together with the other candidate QTGs, thus provide new targets for neurobiological and treatment studies of fear learning and trauma- and stress-related disorders.There is a knowledge gap in understanding the genetic contributions to behavioral heterogeneity in typical and atypical populations. Mouse genetic reference panels (GRPs) provide one approach for identifying genetic sources of variation. Here, we identified three loci for conditioned fear acquisition and expression in a mouse GRP. Each locus contained candidate quantitative trait genes (QTGs). One locus had a single QTG, Hcn1 (hyperpolarization-activated cyclic nucleotide-gated channel 1), which has been implicated in neuronal excitability and learning. This discovery was validated using behavioral pharmacology, revealing that Hcn channels in the basolateral amygdala are required for fear acquisition and expression. The study thus identifies novel candidate QTGs that may contribute to variation in emotional learning and highlight the utility of mouse GRPs for the identification of genes underlying complex traits.
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Project description:BackgroundExpression QTL analyses have shed light on transcriptional regulation in numerous species of plants, animals, and yeasts. These microarray-based analyses identify regulators of gene expression as either cis-acting factors that regulate proximal genes, or trans-acting factors that function through a variety of mechanisms to affect transcript abundance of unlinked genes.ResultsA hydroponics-based genetical genomics study in roots of a Zea mays IBM2 Syn10 double haploid population identified tens of thousands of cis-acting and trans-acting eQTL. Cases of false-positive eQTL, which results from the lack of complete genomic sequences from both parental genomes, were described. A candidate gene for a trans-acting regulatory factor was identified through positional cloning. The unexpected regulatory function of a class I glutamine amidotransferase controls the expression of an ABA 8'-hydroxylase pseudogene.ConclusionsIdentification of a candidate gene underlying a trans-eQTL demonstrated the feasibility of eQTL cloning in maize and could help to understand the mechanism of gene expression regulation. Lack of complete genome sequences from both parents could cause the identification of false-positive cis- and trans-acting eQTL.
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Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.
2010-06-25 | E-GEOD-7628 | biostudies-arrayexpress
Project description:There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8x10(-57)), CCL4L1 (p = 3.9x10(-21)), IL18 (p = 6.8x10(-13)), LPA (p = 4.4x10(-10)), GGT1 (p = 1.5x10(-7)), SHBG (p = 3.1x10(-7)), CRP (p = 6.4x10(-6)) and IL1RN (p = 7.3x10(-6)) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8x10(-40)), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways.
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