Evidence map›Paper›PMID 26339629›Full record

ArticleBioMed research international2015

On the Estimation of Heritability with Family-Based and Population-Based Samples.

Youngdoe Kim, Young Lee, Sungyoung Lee, Nam Hee Kim, Jeongmin Lim, Young Jin Kim, Ji Hee Oh, Haesook Min, Meehee Lee, Hyeon-Jeong Seo and 8 more

Abstract read
In one paragraph

Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Detecting inbreeding depression in structured populations.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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  6. Article
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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

18 authors.

Youngdoe KimThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea ; Department of Applied Statistics, Chung-Ang University, Seoul 156-756, Republic of Korea.
Young LeeThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Sungyoung LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea.
Nam Hee KimThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Jeongmin LimChunlab Inc., Seoul National University, Seoul 151-742, Republic of Korea.
Young Jin KimThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Ji Hee OhThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Haesook MinThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Meehee LeeThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Hyeon-Jeong SeoThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
So-Hyun LeeThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Joohon SungDepartment of Epidemiology, Seoul National University, Seoul 151-742, Republic of Korea.
Nam H ChoDepartment of Preventive Medicine, Ajou University School of Medicine, Suwon 443-380, Republic of Korea.
Bong-Jo KimThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Bok-Ghee HanThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.
Robert C ElstonDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106-7281, USA.
Sungho WonDepartment of Epidemiology and Biostatistics, School of Public Health & Institute of Health and Environment, Seoul National University, Seoul 151-742, Republic of Korea.
Juyoung LeeThe Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated--in particular the amount of variance attributable to common environment in twins can be substantial--which indicates heritability estimates should be interpreted with caution.

Indexed as

Body Mass IndexGenetics, PopulationQuantitative Trait, HeritableBlood PressureBody HeightCholesterolCholesterol, HDLCholesterol, LDLFemaleHumansMalePhenotypeRegression AnalysisRepublic of KoreaTriglyceridesTwins, DizygoticCholesterolCholesterol, HDLCholesterol, LDLTriglycerides

Identifiers

PMID26339629
PMCPMC4538414

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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.