Evidence map›Paper›PMID 24141362›Full record

ArticleNature genetics2013

Evaluating empirical bounds on complex disease genetic architecture.

Vineeta Agarwala, Jason Flannick, Shamil Sunyaev, GoT2D Consortium, David Altshuler

Abstract read
In one paragraph

Article in Nature genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.

0numbers the graph read from it
0cells of the map it votes in
86citing 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

86 citing papers in PubMed.

  1. Genetic architectures of brain-related traits are shaped by strong selective constraints.Proceedings of the National Academy of Sciences of the United States of America · 2026
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26 more citing papers are in PubMed but not listed here.

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.

Vineeta Agarwala1] Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3] Program in Biophysics, Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA. [4].
Jason Flannick
Shamil Sunyaev
GoT2D Consortium
David Altshuler

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Low-Pass Sequencing and High-Density SNP Genotyping for Type 2 DiabetesRC2DK088389 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALTSHULER, DAVID, BOEHNKE, MICHAEL L · 2009 to 2010
$17.9M
MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008313 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI MURTHY, VENKATESH N · 1989 to 2023
$14.6M
TRAINING GRANT IN GENETICST32GM007748 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI Anne O'Donnell-Luria, Louise Wilkins-Haug · 1985 to 2026
$12.0M
New Methods and Enhanced Software for Predicting Functional SNPsR01GM078598 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI SUNYAEV, SHAMIL · 2007 to 2017
$3.5M
Statistical Methods for the Design and Interpretation of Deep Resequencing StudieR01MH084676 · NIMH · BRIGHAM AND WOMEN'S HOSPITAL · PI SUNYAEV, SHAMIL · 2008 to 2010
$1.7M
NIDDK NIH HHS 1RC2DK088389-01NIDDK NIH HHS RC2 DK088389NIGMS NIH HHS R01 GM078598NIGMS NIH HHS R01GM078598NIGMS NIH HHS T32 GM007748NIGMS NIH HHS T32GM007748-33NIGMS NIH HHS T32 GM007753NIGMS NIH HHS T32GM007753NIGMS NIH HHS T32 GM008313NIGMS NIH HHS T32GM008313NIMH NIH HHS R01 MH084676NIMH NIH HHS R01MH084676
6 · The paper itself

Abstract

The genetic architecture of human diseases governs the success of genetic mapping and the future of personalized medicine. Although numerous studies have queried the genetic basis of common disease, contradictory hypotheses have been advocated about features of genetic architecture (for example, the contribution of rare versus common variants). We developed an integrated simulation framework, calibrated to empirical data, to enable the systematic evaluation of such hypotheses. For type 2 diabetes (T2D), two simple parameters--(i) the target size for causal mutation and (ii) the coupling between selection and phenotypic effect--define a broad space of architectures. Whereas extreme models are excluded by the combination of epidemiology, linkage and genome-wide association studies, many models remain consistent, including those where rare variants explain either little (<25%) or most (>80%) of T2D heritability. Ongoing sequencing and genotyping studies will further constrain the space of possible architectures, but very large samples (for example, >250,000 unselected individuals) will be required to localize most of the heritability underlying T2D and other traits characterized by these models.

Indexed as

Empirical ResearchMultifactorial InheritanceComputer SimulationDiabetes Mellitus, Type 2DiseaseGenetic LinkageGenetic Predisposition to DiseaseGenetic VariationGenome-Wide Association StudyHumansModels, Genetic

Identifiers

PMID24141362
PMCPMC4158716

What OpenQuestion holds

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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.