Evidence map›Paper›PMID 39256828›Full record

ArticleHuman genomics2024

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes.

Mahmoud Amiri Roudbar, Seyed Milad Vahedi, Jin Jin, Mina Jahangiri, Hossein Lanjanian, Danial Habibi, Sajedeh Masjoudi, Parisa Riahi, Sahand Tehrani Fateh, Farideh Neshati and 15 more

Abstract read
In one paragraph

Article in Human genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

25 authors.

Mahmoud Amiri RoudbarDepartment of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization, Dezful, Iran.
Seyed Milad VahediDepartment of Animal Science and Aquaculture, Dalhousie University, Bible Hill, NS, B2N5E3, Canada.
Jin JinDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Mina JahangiriDepartment of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Hossein LanjanianCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Danial HabibiCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sajedeh MasjoudiCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Parisa RiahiCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sahand Tehrani FatehSchool of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Farideh NeshatiCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Asiyeh Sadat ZahediCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Maryam Moazzam-JaziCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Leila Najd-Hassan-BonabCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Seyedeh Fatemeh MousaviDepartment of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Sara AsgarianCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Maryam ZarkeshCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad Reza MoghaddasCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Albert TenesaMRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
Anoshirvan KazemnejadDepartment of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Hassan VahidnezhadCenter for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA.
Hakon HakonarsonCenter for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA.
Fereidoun AziziEndocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mehdi HedayatiCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Maryam Sadat DaneshpourCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. daneshpour@sbmu.ac.ir.
Mahdi AkbarzadehCellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. akbarzadeh.ms@gmail.com.

Funding

Multi-ethnic risk prediction for complex human diseases integrating multi-source genetic and non-genetic informationR00HG012223 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JIN, JIN · 2023 to 2025
$727k
NHGRI NIH HHS R00 HG012223
6 · The paper itself

Abstract

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

Indexed as

Diabetes Mellitus, Type 2Genetic Predisposition to DiseasePedigreePolymorphism, Single NucleotideCase-Control StudiesCohort StudiesFamilyFamily StructureFemaleGenome-Wide Association StudyGenomicsGenotypeHumansIranMaleMiddle AgedEstimated risk values (ERV)Genome-wide association studies (GWAS)HeritabilityMissing heritabilityType 2 diabetes

Identifiers

PMID39256828
PMCPMC11389528

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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.