Genetic ancestry in precision medicine is reshaping the race debate

Krainc, Talia; Fuentes, Agustín

doi:10.1073/pnas.2203033119

Cited by 24 publications

(25 citation statements)

References 41 publications

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“…First, we highlight the variability in PGS performance along the continuum of genetic ancestry, even within traditionally defined homogenous populations. With the increasing recognition that genetic ancestries are not discrete but rather continuous [15][16][17][18][19] , the individual-level accuracy introduced here provides a powerful tool to study PGS performance along the genetic ancestry continuum. Given the pervasive variable PGS accuracy across individuals, incorporating individual-level metrics of PGS performance can improve the utility of PGS.…”

Section: Discussionmentioning

confidence: 99%

“…PGS accuracy is traditionally assayed using population-level metrics of accuracy (e.g., R 2 ), thus assuming some level of homogeneity across individuals within the considered population 2,11,15 . However, homogeneous populations are an idealized concept that only roughly approximate human populations; human diversity exists along a genetic ancestry continuum without clearly defined clusters and with various correlations between genetic and socio-environmental factors [15][16][17][18][19][20] . Grouping individuals into genetic ancestry clusters obscures the impact of individual variation on PGS accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populations

Ding

Hou

Xu³

et al. 2022

Preprint

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Polygenic scores (PGS) have limited portability across different groupings of individuals (e.g., by genetic ancestries and/or social determinants of health), preventing their equitable use. PGS portability has typically been assessed using a single aggregate population-level statistic (e.g., R2), ignoring inter-individual variation within the population. Here we evaluate PGS accuracy at individual-level resolution, independent of its annotated genetic ancestries. We show that PGS accuracy varies between individuals across the genetic ancestry continuum in all ancestries, even within traditionally "homogeneous" genetic ancestry clusters. Using a large and diverse Los Angeles biobank (ATLAS, N= 36,778) along with the UK Biobank (UKBB, N= 487,409), we show that PGS accuracy decreases along a continuum of genetic ancestries in all considered populations and the trend is well-captured by a continuous measure of genetic distance (GD) from the PGS training data; Pearson correlation of -0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained in UKBB "white British" individuals to European-ancestry individuals of ATLAS, individuals in the highest GD decile have 14% lower accuracy relative to the lowest decile; notably the lowest GD decile of Hispanic/Latino American ancestry individuals showed similar PGS performance as the highest GD decile of European ancestry ATLAS individuals. GD is significantly correlated with PGS estimates themselves for 82 out of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestry in PGS interpretation. Our results highlight the need for moving away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGS and their applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populations

Ding

Hou

Xu³

et al. 2022

Preprint

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“…These racial categories persist to this day, and it is therefore important to better comprehend the differences between race, ethnicity and genetic ancestry, and their subsequent utility for precision oncology. Indeed, although being distinct concepts, they are often collected and used variably in precision oncology and other related activities (Adigbli, 2020;Krainc and Fuentes, 2022;Bonham, Green and Pérez-Stable, 2018;Popejoy et al, 2020).…”

Section: Main Text Introductionmentioning

confidence: 99%

“…In contrast, genetic ancestry is a biological attribute that "involves the comparison of a large number of DNA variants measured in an individual with the frequencies of these variants in reference populations sampled from across the world" (Krainc and Fuentes, 2022;Jorde and Bamshad, 2020).…”

Section: Main Text Introductionmentioning

confidence: 99%

Impact of structural racism on inclusion and diversity in precision oncology: A scoping and critical review of the literature

Geneviève¹,

Elger²,

Wangmo³

2022

Camb. prisms Precis. med.

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This scoping review provides a precise mapping of recruitment and participation barriers that racial and ethnic minorities are likely to face in precision oncology and related activities. It provides useful actionable insights for researchers, clinicians, healthcare and research institutions, and policy-makers to identify how systemic discriminatory pathways can lead to cancer disparities along racial and ethnic lines in precision oncology. In addition, it is the first scoping review to utilize the Public Health Critical Race Praxis (PHCRP) -an analytical framework rooted in critical race theory -to uncover how assumptions, simplifications, norms and paradigms used by researchers in their respective studies can further explain the limited representation of minorities. For instance, the PHCRP analysis revealed that researchers tend to embrace a monolithic view of racialized populations, wrongly equating self-reported race to the presence of specific genetic variants or homogeneous genetic backgrounds within these populations. Such a view was also found to be primed by inherent limitations of certain tools used commonly in genomic studies and precision oncology. For instance, publicly available genomic databases missing genetic ancestral information and failing to capture the genetic diversity of populations represented therein. Other factors revealed by the PHCRP analytical framework include the Black-White binary paradigm, researchers ignoring the contribution of racism to observed cancer disparities, and the lack of conceptual clarity on the meaning and use of race and ethnicity. Although predominantly centered on US studies and publications, results of this scoping review and of the PHCRP analysis are likely to be useful to the broader scientific community at tackling the lack of inclusion and diversity in their respective projects since structural racism knows no borders.

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“…Race may be a social construct, but ancestry is genetic. 7 Until we determine that the lower lung volumes observed in patients who identify with certain ''races'' are not in some part due to common genetic ancestry, we should be careful regarding how we proceed. We want to avoid having ''racial adjustments'' mask underlying pathologic lung function, but also not have normal lung function interpreted as pathologic, abnormal, or inferior.…”

mentioning

confidence: 99%