Evidence map›Paper›PMID 34529658›Full record

ArticlePLoS genetics2021

On cross-ancestry cancer polygenic risk scores.

Lars G Fritsche, Ying Ma, Daiwei Zhang, Maxwell Salvatore, Seunggeun Lee, Xiang Zhou, Bhramar Mukherjee

Abstract read
In one paragraph

Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 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

7 authors.

Lars G FritscheDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-2110-1690
Ying MaDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0003-3791-7018
Daiwei ZhangDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-5019-622X
Maxwell SalvatoreDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-3659-1514
Seunggeun LeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-8097-3878
Xiang ZhouDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-4331-7599
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0003-0118-4561

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 1988 to 2026
$178.2M
Statistical and computational methods for rare variant association analysisR01HG008773 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MUKHERJEE, BHRAMAR · 2016 to 2020
$1.9M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCI NIH HHS P30 CA046592NHGRI NIH HHS R01 HG008773
6 · The paper itself

Abstract

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

Indexed as

Genetic Predisposition to DiseaseMultifactorial InheritanceBreast NeoplasmsFemaleGenome-Wide Association StudyHumans

Identifiers

PMID34529658
PMCPMC8445431

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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.