Evidence map›Paper›PMID 36333282›Full record

SynthesisNature communications2022

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians.

Young Jin Kim, Sanghoon Moon, Mi Yeong Hwang, Sohee Han, Hye-Mi Jang, Jinhwa Kong, Dong Mun Shin, Kyungheon Yoon, Sung Min Kim, Jong-Eun Lee and 5 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
–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

41 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

15 authors.

Young Jin Kim *Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.ORCID 0000-0002-4132-4437
Sanghoon Moon *Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Mi Yeong HwangDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.ORCID 0000-0002-8208-7925
Sohee HanDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Hye-Mi JangDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Jinhwa KongDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Dong Mun ShinDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Kyungheon YoonDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.ORCID 0000-0001-6727-2789
Sung Min KimDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Jong-Eun LeeDNALink, Seoul, Republic of Korea.
Anubha MahajanGenentech, 1 DNA Way, South San Francisco, CA, USA.
Hyun-Young ParkDepartment of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea.
Mark I McCarthyGenentech, 1 DNA Way, South San Francisco, CA, USA.ORCID 0000-0002-4393-0510
Yoon Shin ChoBiomedical Science, Hallym University, Chuncheon, Republic of Korea. yooncho33@hallym.ac.kr.ORCID 0000-0002-7121-3471
Bong-Jo KimDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea. kbj6181@korea.kr.ORCID 0000-0003-3562-2654

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.

Indexed as

Diabetes Mellitus, Type 2Genome-Wide Association StudyAsian PeopleBlood GlucoseGenetic Predisposition to DiseaseGenetic VariationHumansPhenotypePolymorphism, Single NucleotideBlood Glucose

Identifiers

PMID36333282
PMCPMC9636136

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.