Evidence map›Paper›PMID 32491157›Full record

ArticleHuman molecular genetics2020

Population-specific reference panels are crucial for genetic analyses: an example of the CREBRF locus in Native Hawaiians.

Meng Lin, Christian Caberto, Peggy Wan, Yuqing Li, Annette Lum-Jones, Maarit Tiirikainen, Loreall Pooler, Brooke Nakamura, Xin Sheng, Jacqueline Porcel and 7 more

Open access · bronzeAbstract read
In one paragraph

Article in Human molecular genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
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

24 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
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  5. Review
  6. Imputation accuracy across global human populations.American journal of human genetics · 2024
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  11. Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023
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  16. A genealogical estimate of genetic relationships.American journal of human genetics · 2022
    Article
  17. BMJ open diabetes research & care · 2022
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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

17 authors at 3 institutions in 1 country.

Meng LinCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Christian CabertoEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Peggy WanCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Yuqing LiDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA.
Annette Lum-JonesEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Maarit TiirikainenEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Loreall PoolerCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Brooke NakamuraCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Xin ShengCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Jacqueline PorcelCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Unhee LimEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Veronica Wendy SetiawanCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Loïc Le MarchandEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Lynne R WilkensEpidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA.
Christopher A HaimanCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Iona ChengDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA.
Charleston W K ChiangCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
University of Southern California · USUniversity of Hawaiʻi at Mānoa · USUniversity of California, San Francisco · US

Funding

University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI GERTRAUD MASKARINEC · 1996 to 2026
$56.2M
Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
Recruitment and Data CollectionP01CA168530 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 2012 to 2016
$19.9M
Epidemiologic Studies of Putative Functional Variation in Multiethnic CohortU01HG007397 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2013 to 2017
$3.7M
NCI NIH HHS P01 CA168530NCI NIH HHS P30 CA071789NCI NIH HHS U01 CA164973NHGRI NIH HHS U01 HG007397
6 · The paper itself

Abstract

Statistical imputation applied to genome-wide array data is the most cost-effective approach to complete the catalog of genetic variation in a study population. However, imputed genotypes in underrepresented populations incur greater inaccuracies due to ascertainment bias and a lack of representation among reference individuals, further contributing to the obstacles to study these populations. Here we examined the consequences due to the lack of representation by genotyping in a large number of self-reported Native Hawaiians (N = 3693) a functionally important, Polynesian-specific variant in the CREBRF gene, rs373863828. We found the derived allele was significantly associated with several adiposity traits with large effects (e.g. ~ 1.28 kg/m2 per allele in body mass index as the most significant; P = 7.5 × 10-5), consistent with the original findings in Samoans. Due to the current absence of Polynesian representation in publicly accessible reference sequences, rs373863828 or its proxies could not be tested through imputation using these existing resources. Moreover, the association signals at the entire CREBRF locus could not be captured by alternative approaches, such as admixture mapping. In contrast, highly accurate imputation can be achieved even if a small number (<200) of internally constructed Polynesian reference individuals were available; this would increase sample size and improve the statistical evidence of associations. Taken together, our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci such as CREBRF. Yet, they could be easily detected and prioritized with improved representation of diverse populations in sequencing studies.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyAdiposityAllelesBody Mass IndexFemaleGenetics, PopulationGenotypeHumansMaleNative Hawaiian or Pacific IslanderObesityPhenotypePolymorphism, Single NucleotideTumor Suppressor ProteinsCREB3 regulatory factor, humanTumor Suppressor Proteins

Identifiers

PMID32491157
PMCPMC7399533
OpenAlexW3031413374

What OpenQuestion holds

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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.