Evidence map›Paper›PMID 28498917›Full record

ReviewEndocrinology2017

The New Genomics: What Molecular Databases Can Tell Us About Human Population Variation and Endocrine Disease.

Peter Rotwein

Open access · hybridAbstract readReview
In one paragraph

Review in Endocrinology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Variation in Akt protein kinases in human populations.American journal of physiology. Regulatory, integrative and comparative physiology · 2017
    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

1 author at 1 institution in 1 country.

Peter RotweinDepartment of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech Health University Health Sciences Center, El Paso, Texas 79905.
Texas Tech University Health Sciences Center · US

Funding

INSULINLIKE GROWTH FACTORS AND MUSCLE DIFFERENTIATIONR01DK042748 · NIDDK · WASHINGTON UNIVERSITY · PI ROTWEIN, PETER S · 1991 to 2017
$5.6M
NIDDK NIH HHS R01 DK042748
6 · The paper itself

Abstract

Major recent advances in genetics and genomics present unique opportunities for enhancing our understanding of human physiology and disease predisposition. Here I demonstrate how analysis of genomic information can provide new insights into endocrine systems, using the human growth hormone (GH) signaling pathway as an illustrative example. GH is essential for normal postnatal growth in children, and plays important roles in other biological processes throughout life. GH actions are mediated by the GH receptor, primarily via the JAK2 protein tyrosine kinase and the STAT5B transcription factor, and inactivating mutations in this pathway all lead to impaired somatic growth. Variation in GH signaling genes has been evaluated using DNA sequence data from the Exome Aggregation Consortium, a compendium of information from >60,000 individuals. Results reveal many potential missense and other alterations in the coding regions of GH1, GHR, JAK2, and STAT5B, with most changes being uncommon. The total number of different alleles per gene varied by ~threefold, from 101 for GH1 to 338 for JAK2. Several known disease-linked mutations in GH1, GHR, and JAK2 were present but infrequent in the population; however, three amino acid changes in GHR were sufficiently prevalent (~4% to 44% of chromosomes) to suggest that they are not disease causing. Collectively, these data provide new opportunities to understand how genetically driven variability in GH signaling and action may modify human physiology and disease.

Indexed as

Databases, ChemicalEndocrine System DiseasesGenetic VariationGenomicsHuman Growth HormoneHumansPolymorphism, GeneticReceptors, SomatotropinSignal TransductionHuman Growth HormoneReceptors, Somatotropin

Identifiers

PMID28498917
PMCPMC7282473
OpenAlexW2612347270

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.