Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.

Search

Type in a keyword to search

On page 1 showing 1 ~ 10 papers out of 10 papers

Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference.

  • Wanqing Wen‎ et al.
  • Scientific reports‎
  • 2016‎

Sixty genetic loci associated with abdominal obesity, measured by waist circumference (WC) and waist-hip ratio (WHR), have been previously identified, primarily from studies conducted in European-ancestry populations. We conducted a meta-analysis of associations of abdominal obesity with approximately 2.5 million single nucleotide polymorphisms (SNPs) among 53,052 (for WC) and 48,312 (for WHR) individuals of Asian descent, and replicated 33 selected SNPs among 3,762 to 17,110 additional individuals. We identified four novel loci near the EFEMP1, ADAMTSL3 , CNPY2, and GNAS genes that were associated with WC after adjustment for body mass index (BMI); two loci near the NID2 and HLA-DRB5 genes associated with WHR after adjustment for BMI, and three loci near the CEP120, TSC22D2, and SLC22A2 genes associated with WC without adjustment for BMI. Functional enrichment analyses revealed enrichment of corticotropin-releasing hormone signaling, GNRH signaling, and/or CDK5 signaling pathways for those newly-identified loci. Our study provides additional insight on genetic contribution to abdominal obesity.


Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes.

  • David Z Pan‎ et al.
  • Genome medicine‎
  • 2021‎

Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.


Open Chromatin Profiling in Adipose Tissue Marks Genomic Regions with Functional Roles in Cardiometabolic Traits.

  • Maren E Cannon‎ et al.
  • G3 (Bethesda, Md.)‎
  • 2019‎

Identifying the regulatory mechanisms of genome-wide association study (GWAS) loci affecting adipose tissue has been restricted due to limited characterization of adipose transcriptional regulatory elements. We profiled chromatin accessibility in three frozen human subcutaneous adipose tissue needle biopsies and preadipocytes and adipocytes from the Simpson Golabi-Behmel Syndrome (SGBS) cell strain using an assay for transposase-accessible chromatin (ATAC-seq). We identified 68,571 representative accessible chromatin regions (peaks) across adipose tissue samples (FDR < 5%). GWAS loci for eight cardiometabolic traits were enriched in these peaks (P < 0.005), with the strongest enrichment for waist-hip ratio. Of 110 recently described cardiometabolic GWAS loci colocalized with adipose tissue eQTLs, 59 loci had one or more variants overlapping an adipose tissue peak. Annotated variants at the SNX10 waist-hip ratio locus and the ATP2A1-SH2B1 body mass index locus showed allelic differences in regulatory assays. These adipose tissue accessible chromatin regions elucidate genetic variants that may alter adipose tissue function to impact cardiometabolic traits.


Rare and low-frequency coding variants alter human adult height.

  • Eirini Marouli‎ et al.
  • Nature‎
  • 2017‎

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.


Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey.

  • Cassandra N Spracklen‎ et al.
  • PLoS genetics‎
  • 2018‎

To identify genetic contributions to type 2 diabetes (T2D) and related glycemic traits (fasting glucose, fasting insulin, and HbA1c), we conducted genome-wide association analyses (GWAS) in up to 7,178 Chinese subjects from nine provinces in the China Health and Nutrition Survey (CHNS). We examined patterns of population structure within CHNS and found that allele frequencies differed across provinces, consistent with genetic drift and population substructure. We further validated 32 previously described T2D- and glycemic trait-loci, including G6PC2 and SIX3-SIX2 associated with fasting glucose. At G6PC2, we replicated a known fasting glucose-associated variant (rs34177044) and identified a second signal (rs2232326), a low-frequency (4%), probably damaging missense variant (S324P). A variant within the lead fasting glucose-associated signal at SIX3-SIX2 co-localized with pancreatic islet expression quantitative trait loci (eQTL) for SIX3, SIX2, and three noncoding transcripts. To identify variants functionally responsible for the fasting glucose association at SIX3-SIX2, we tested five candidate variants for allelic differences in regulatory function. The rs12712928-C allele, associated with higher fasting glucose and lower transcript expression level, showed lower transcriptional activity in reporter assays and increased binding to GABP compared to the rs12712928-G, suggesting that rs12712928-C contributes to elevated fasting glucose levels by disrupting an islet enhancer, resulting in reduced gene expression. Taken together, these analyses identified multiple loci associated with glycemic traits across China, and suggest a regulatory mechanism at the SIX3-SIX2 fasting glucose GWAS locus.


Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

  • Cecilia M Lindgren‎ et al.
  • PLoS genetics‎
  • 2009‎

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.


Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

  • Mariaelisa Graff‎ et al.
  • PLoS genetics‎
  • 2017‎

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.


Discovery and refinement of loci associated with lipid levels.

  • Cristen J Willer‎ et al.
  • Nature genetics‎
  • 2013‎

Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.


Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

  • Chelsea K Raulerson‎ et al.
  • American journal of human genetics‎
  • 2019‎

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.


Subcutaneous adipose tissue splice quantitative trait loci reveal differences in isoform usage associated with cardiometabolic traits.

  • Sarah M Brotman‎ et al.
  • American journal of human genetics‎
  • 2022‎

Alternate splicing events can create isoforms that alter gene function, and genetic variants associated with alternate gene isoforms may reveal molecular mechanisms of disease. We used subcutaneous adipose tissue of 426 Finnish men from the METSIM study and identified splice junction quantitative trait loci (sQTLs) for 6,077 splice junctions (FDR < 1%). In the same individuals, we detected expression QTLs (eQTLs) for 59,443 exons and 15,397 genes (FDR < 1%). We identified 595 genes with an sQTL and exon eQTL but no gene eQTL, which could indicate potential isoform differences. Of the significant sQTL signals, 2,114 (39.8%) included at least one proxy variant (linkage disequilibrium r2 > 0.8) located within an intron spanned by the splice junction. We identified 203 sQTLs that colocalized with 141 genome-wide association study (GWAS) signals for cardiometabolic traits, including 25 signals for lipid traits, 24 signals for body mass index (BMI), and 12 signals for waist-hip ratio adjusted for BMI. Among all 141 GWAS signals colocalized with an sQTL, we detected 26 that also colocalized with an exon eQTL for an exon skipped by the sQTL splice junction. At a GWAS signal for high-density lipoprotein cholesterol colocalized with an NR1H3 sQTL splice junction, we show that the alternative splice product encodes an NR1H3 transcription factor that lacks a DNA binding domain and fails to activate transcription. Together, these results detect splicing events and candidate mechanisms that may contribute to gene function at GWAS loci.


  1. SciCrunch.org Resources

    Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.

  2. Navigation

    You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.

  3. Logging in and Registering

    If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.

  4. Searching

    Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:

    1. Use quotes around phrases you want to match exactly
    2. You can manually AND and OR terms to change how we search between words
    3. You can add "-" to terms to make sure no results return with that term in them (ex. Cerebellum -CA1)
    4. You can add "+" to terms to require they be in the data
    5. Using autocomplete specifies which branch of our semantics you with to search and can help refine your search
  5. Save Your Search

    You can save any searches you perform for quick access to later from here.

  6. Query Expansion

    We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.

  7. Collections

    If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.

  8. Facets

    Here are the facets that you can filter your papers by.

  9. Options

    From here we'll present any options for the literature, such as exporting your current results.

  10. Further Questions

    If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.

Publications Per Year

X

Year:

Count: