Evidence map›Paper›PMID 25636063›Full record

ReviewCurrent opinion in lipidology2015

Next-generation gene discovery for variants of large impact on lipid traits.

Elisabeth Rosenthal, Elizabeth Blue, Gail P Jarvik

Abstract readReview
In one paragraph

Review in Current opinion in lipidology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

Elisabeth RosenthalaDepartment of Medicine (Medical Genetics) bDepartment of Genome Sciences, University of Washington, Seattle, Seattle, Washington, USA.
Elizabeth Blue
Gail P Jarvik

Funding

Serum Amyloid and Inflammation in AtherogenesisP01HL030086 · NHLBI · UNIVERSITY OF WASHINGTON · PI JARVIK, GAIL PAIRITZ · 1985 to 2009
$14.7M
Medical Genetics Training GrantT32GM007454 · NIGMS · UNIVERSITY OF WASHINGTON · PI Gail Pairitz Jarvik, Andrew Ben Stergachis · 1985 to 2026
$6.9M
Identifying Alzheimer's disease genes using genomic and family dataR00AG040184 · NIA · UNIVERSITY OF WASHINGTON · PI BLUE, ELIZABETH ELOYCE · 2013 to 2016
$746k
NHLBI NIH HHS P01 HL030086NIA NIH HHS R00 AG040184NIGMS NIH HHS T32 GM007454
6 · The paper itself

Abstract

purpose of reviewDetection of high-impact variants on lipid traits is complicated by complex genetic architecture. Although genome-wide association studies (GWAS) successfully identified many novel genes associated with lipid traits, it was less successful in identifying variants with a large impact on the phenotype. This is not unexpected, as the more common variants detectable by GWAS typically have small effects. The availability of large familial datasets and sequence data has changed the paradigm for successful genomic discovery of the novel genes and pathogenic variants underlying lipid disorders. RECENT

findingsNovel loci with large effects have been successfully mapped in families, and next-generation sequencing allowed for the identification of the underlying lipid-associated variants of large effect size. The success of this strategy relies on the simplification of the underlying genetic variation by focusing on large single families segregating extreme lipid phenotypes. SUMMARY: Rare, high-impact variants are expected to have large effects and be more relevant for medical and pharmaceutical applications. Family data have many advantages over population-based data because they allow for the efficient detection of high-impact variants with an exponentially smaller sample size and increased power for follow-up studies.

Indexed as

AnimalsDyslipidemiasGenetic LinkageGenetic Predisposition to DiseaseGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansLipid MetabolismPolymorphism, Single Nucleotide

Identifiers

PMID25636063
PMCPMC4388051

What OpenQuestion holds

Textmetadata
LicenceTDM
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.