A genome-wide search for gene-by-obesity interaction loci of dyslipidemia in Koreans shows diverse genetic risk alleles

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

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A Genome-Wide Search For Susceptibility Loci to Human Essential Hypertension

Hypertension ◽

10.1161/01.hyp.35.6.1291 ◽

2000 ◽

Vol 35 (6) ◽

pp. 1291-1296 ◽

Cited By ~ 40

Author(s):

Pankaj Sharma ◽

Jennie Fatibene ◽

Franco Ferraro ◽

Haiyan Jia ◽

Sue Monteith ◽

...

Keyword(s):

Essential Hypertension ◽

Susceptibility Loci ◽

Genome Wide ◽

A Genome ◽

Genome Wide Search

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Common genetic risk variants identified in the SPARK cohort implicate DDHD2 as a novel autism risk gene

10.1101/2020.01.13.20017319 ◽

2020 ◽

Cited By ~ 3

Author(s):

Nana Matoba ◽

Dan Liang ◽

Huaigu Sun ◽

Nil Aygün ◽

Jessica C. McAfee ◽

...

Keyword(s):

Gene Regulation ◽

Association Study ◽

Genetic Risk ◽

Molecular Mechanisms ◽

Autism Spectrum ◽

Risk Variants ◽

Genome Wide ◽

Common Genetic Variants ◽

A Genome ◽

Gene Regulatory

AbstractBackgroundAutism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of three common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) using the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD.MethodsWe performed an association study on 6,222 case-pseudocontrol pairs from SPARK and meta-analyzed with a previous GWAS. We integrated gene regulatory annotations to map non-coding risk variants to their regulated genes. Further, we performed a massively parallel reporter assay (MPRA) to identify causal variant(s) within a novel risk locus.ResultsWe identified one novel GWS locus from the SPARK GWAS. The meta-analysis identified four significant loci, including an additional novel locus. We observed significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. The MPRA identified one variant at the novel locus with strong impacts on gene regulation (rs7001340), and expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and pre-natal brains.ConclusionsBy integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.

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Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213014600082 ◽

2014 ◽

Vol 23 (03) ◽

pp. 1460008

Author(s):

Kevin Byron ◽

Jason T. L. Wang ◽

Dongrong Wen

Keyword(s):

Random Forests ◽

Life Science ◽

Science Research ◽

Genomic Region ◽

Computational Approach ◽

Search Method ◽

Genome Wide ◽

A Genome ◽

Genome Wide Search ◽

Covariance Models

Developing effective artificial intelligence tools to find motifs in DNA, RNA and proteins poses a challenging yet important problem in life science research. In this paper, we present a computational approach for finding RNA tertiary motifs in genomic sequences. Specifically, we predict genomic coordinate locations for coaxial helical stackings in 3-way RNA junctions. These predictions are provided by our tertiary motif search package, named CSminer, which utilizes two versatile methodologies: random forests and covariance models. A coaxial helical stacking tertiary motif occurs in a 3-way RNA junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Our CSminer tool first uses a genome-wide search method based on covariance models to find a genomic region that may potentially contain a coaxial helical stacking tertiary motif. CSminer then uses a random forests classifier to predict whether the genomic region indeed contains the tertiary motif. Experimental results demonstrate the effectiveness of our approach.

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Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk

Molecular Psychiatry ◽

10.1038/mp.2008.133 ◽

2008 ◽

Vol 14 (3) ◽

pp. 252-260 ◽

Cited By ~ 259

Author(s):

V Moskvina ◽

◽

N Craddock ◽

P Holmans ◽

I Nikolov ◽

...

Keyword(s):

Bipolar Disorder ◽

Genetic Risk ◽

Genome Wide Association ◽

Data Sets ◽

Risk Alleles ◽

Genome Wide ◽

Association Data

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Regulation of angiogenesis through a microRNA (miR-130a) that down-regulates antiangiogenic homeobox genes GAX and HOXA5

Blood ◽

10.1182/blood-2007-07-104133 ◽

2008 ◽

Vol 111 (3) ◽

pp. 1217-1226 ◽

Cited By ~ 317

Author(s):

Yun Chen ◽

David H. Gorski

Keyword(s):

Binding Sites ◽

Untranslated Region ◽

Vascular Endothelial Cells ◽

Homeobox Gene ◽

Vascular Endothelial ◽

Down Regulation ◽

Antiangiogenic Activity ◽

Genome Wide ◽

A Genome ◽

Genome Wide Search

Abstract Angiogenesis is critical to tumor progression. The homeobox gene GAX inhibits angiogenesis in vascular endothelial cells (ECs). We have identified a microRNA (miR-130a) that regulates GAX expression and hypothesized that it plays a major role in modulating GAX activity in ECs. A 280-bp fragment from the GAX 3′-untranslated region (3′-UTR) containing 2 miR-130a targeting sites was observed to be required for the rapid down-regulation of GAX expression by serum and proangiogenic factors, whereas the activity of the GAX promoter did not vary with exposure to serum or proangiogenic factors. This same 280-bp sequence in the GAX 3′-UTR cloned into the psiCHECK2-Luciferase vector mediated serum-induced down-regulation of the reporter gene when placed 3′ of it. Finally, forced expression of miR-130a inhibits GAX expression through this specific GAX 3′-UTR sequence. A genome-wide search for other possible miR-130a binding sites revealed an miR-130a targeting site in the 3′-UTR of the antiangiogenic homeobox gene HOXA5, the expression and antiangiogenic activity of which are also inhibited by miR-130a. From these data, we conclude that miR-130a is a regulator of the angiogenic phenotype of vascular ECs largely through its ability to modulate the expression of GAX and HOXA5.

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Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs

BMC Genomics ◽

10.1186/1471-2164-14-648 ◽

2013 ◽

Vol 14 (1) ◽

pp. 648 ◽

Cited By ~ 58

Author(s):

Jeroen Crappé ◽

Wim Van Criekinge ◽

Geert Trooskens ◽

Eisuke Hayakawa ◽

Walter Luyten ◽

...

Keyword(s):

In Silico ◽

Ribosome Profiling ◽

In Silico Prediction ◽

Genome Wide ◽

A Genome ◽

Genome Wide Search

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What genomic research has told us about the obesity and its possible gene therapy targets.

10.31219/osf.io/ym49s ◽

2021 ◽

Author(s):

Moataz Dowaidar

Keyword(s):

Genetic Risk ◽

Genomic Research ◽

Activity Measurement ◽

Unhealthy Lifestyle ◽

Sweetened Beverages ◽

Therapy Targets ◽

New Information ◽

Genome Wide ◽

A Genome ◽

Primary Advantage

The discovery of a genome-wide correlation with obesity-related genes hasrevealed new information about the genetics of obesity. Given the lowproportion of obesity heritability explained by available SNPs, it's not shockingthat these SNPs aren't scientifically effective as methods for assessing whowould acquire obesity. The roles of the majority of loci, the majority of whichmap to non-coding sequences, will take thorough analysis to determine theresponsible gene at each locus, which may not be the closest gene. Thismechanistic information, as well as the resulting elucidation of thepathophysiology of obesity, will allow the creation of new therapies, whichcould be the primary advantage of these genetic discoveries.Fortunately, a lack of mechanistic information hasn't stopped researchers fromusing SNPs and genetic risk ratings to shed light on how obesity biologyinteracts with environmental and lifestyle influences. These findings suggestthat an unhealthy lifestyle may amplify the genetic risk of obesity, despite thefact that environmental studies of obesity genes may be distorted byinaccuracies in diet and physical activity measurement. More research isrequired to confirm this theory and to identify the specific dietary components(such as sugar-sweetened beverages) that interfere with genetic variants. Thisstudy could contribute to personalized obesity prevention and care measures inthe future (pending confirmation in clinical trials of genetic-risk-guidedinterventions). Obesity genetics has offered researchers the opportunity toexamine causal interactions between obesity and its various possiblecomplications. However, since the majority of the studies discussed above wereconducted on people of European ethnicity, more research is required inminority ethnic groups with a high risk of obesity to understand the role ofbiology, climate, and relationships among these factors in explaining theirincreased risk.

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