Evidence map›Paper›PMID 26776194›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2016

KIDNEY DISEASE GENETICS AND THE IMPORTANCE OF DIVERSITY IN PRECISION MEDICINE.

Jessica N Cooke Bailey, Sarah Wilson, Kristin Brown-Gentry, Robert Goodloe, Dana C Crawford

Open access · goldAbstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact, top 85% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Review
  3. Observational
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Jessica N Cooke BaileyInstitute for Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road, Suite 2527, Cleveland, OH 44106, USA, jnc43@case.edu.
Sarah Wilson
Kristin Brown-Gentry
Robert Goodloe
Dana C Crawford
Cornell University · USVanderbilt University · USCenter for Human Genetics · US

Funding

Epidemiologic Architecture for Genes Linked to Environment (EAGLE)U01HG004798 · NHGRI · VANDERBILT UNIVERSITY · PI CRAWFORD, DANA C · 2008 to 2013
$8.8M
NHGRI NIH HHS U01 HG004798NHGRI NIH HHS U01HG004798
6 · The paper itself

Abstract

Kidney disease is a well-known health disparity in the United States where African Americans are affected at higher rates compared with other groups such as European Americans and Mexican Americans. Common genetic variants in the myosin, heavy chain 9, non-muscle (MYH9) gene were initially identified as associated with non-diabetic end-stage renal disease in African Americans, and it is now understood that these variants are in strong linkage disequilibrium with likely causal variants in neighboring APOL1. Subsequent genome-wide and candidate gene studies have suggested that MYH9 common variants among others are also associated with chronic kidney disease and quantitative measures of kidney function in various populations. In a precision medicine setting, it is important to consider genetic effects or genetic associations that differ across racial/ethnic groups in delivering data relevant to disease risk or individual-level patient assessment. Kidney disease and quantitative trait-associated genetic variants have yet to be systematically characterized in multiple racial/ethnic groups. Therefore, to further characterize the prevalence of these genetic variants and their association with kidney related traits, we have genotyped 10 kidney disease or quantitative trait-associated single nucleotide polymorphisms (SNPs) (rs2900976, rs10505955, rs10502868, rs1243400, rs9305354, rs12917707, rs17319721, rs2467853, rs2032487, and rs4821480) in 14,998 participants from the population-based cross-sectional National Health and Nutrition Examination Surveys (NHANES) III and 1999-2002 as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study. In this general adult population ascertained regardless of health status (6,293 non-Hispanic whites, 3,013 non-Hispanic blacks, and 3,542 Mexican Americans), we observed higher rates of chronic kidney disease among non-Hispanic blacks compared with the other groups as expected. We performed single SNP tests of association using linear regressions assuming an additive genetic model adjusted for age, sex, diastolic blood pressure, systolic blood pressure, and type 2 diabetes status for several outcomes including creatinine (urinary), creatinine (serum), albumin (urinary), eGFR, and albumin-to-urinary creatinine ratio (ACR). We also tested for associations between each SNP and chronic kidney disease and albuminuria using logistic regression. Surprisingly, none of the MYH9 variants tested was associated with kidney diseases or traits in non-Hispanic blacks (p>0.05), perhaps attributable to the clinical heterogeneity of kidney disease in this population. Several associations were observed in each racial/ethnic group at p<0.05, but none were consistently associated in the same direction in all three groups. The lack of significant and consistent associations is most likely due to power highlighting the importance of the availability of large, diverse populations for genetic association studies of complex diseases and traits to inform precision medicine efforts in diverse patient populations.

Indexed as

AdultBlack or African AmericanComputational BiologyCross-Sectional StudiesFemaleGene FrequencyGenetic VariationHealth Status DisparitiesHumansKidney DiseasesMaleMexican AmericansMiddle AgedNutrition SurveysPolymorphism, Single NucleotidePrecision Medicine

Identifiers

PMID26776194
PMCPMC4720994
OpenAlexW2325896827

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.