Increasing diversity in genomics requires investment in equitable partnerships and capacity building

Martin, Alicia R.; Stroud, Rocky E.; Abebe, Tamrat; Akena, Dickens; Alemayehu, Melkam; Atwoli, Lukoye; Chapman, Sinéad B.; Flowers, Katelyn; Gelaye, Bizu; Gichuru, Stella; Kariuki, Symon M.; Kinyanjui, Sam; Korte, Kristina J.; Koen, Nastassja; Koenen, Karestan C.; Newton, Charles R. J. C.; Olivares, Ana Maria; Pollock, Sam; Post, Kristianna; Singh, Ilina; Stein, Dan J.; Teferra, Solomon; Zingela, Zukiswa; Chibnik, Lori B.

doi:10.1038/s41588-022-01095-y

Cited by 43 publications

(34 citation statements)

References 22 publications

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“…Along with previous studies, our results emphasize the importance of powerful PGS training in non-European cohorts 32 to improve the PGS performance for individuals from diverse genetic background. To narrow the prediction gap and ameliorate consequent health disparities between European and non-European ancestries individuals, concerted global effort and equitable collaborations are needed to increase the sample size of underrepresented individuals 32,33 . Equally important is the development of multi-ancestry PGS methods that can effectively leverage ancestrally diverse populations to train PGS models 34 .…”

Section: Discussionmentioning

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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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%

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

Ding

Hou

Xu³

et al. 2022

Preprint

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“…As many have written before 15 , ensuring that scientific teams include and are led by people from groups harmed by weaponized science is one step that will make science more robust to misinterpretation and appropriation.…”

Section: Steps To Robustnessmentioning

confidence: 99%

Counter the weaponization of genetics research by extremists

Carlson¹,

Henn

Al-Hindi

et al. 2022

Nature

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“…It has been reported that over 80% of genomics data sets are made up of individuals from European ancestry. 20 This poses a serious data diversity and representation issue in precision medicine. 21 Studies have shown that mitigation methods for racial bias in data can increase fairness, avoid inequities in diagnosis and medical care.…”

Section: Discussionmentioning

confidence: 99%

Data bias in precision medicine

Laishram¹

2022

Int J Adv Med

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Precision medicine is poised to increasingly improve health outcomes for more people in the near future. In contrast to the more traditional reactive methods of disease treatment, precision medicine is a customizable treatment and disease prevention approach that is tailored for the individual. Artificial intelligence (AI) using sophisticated algorithms and machine learning (ML) tools powers these precision medicine processes. These algorithms analyze big data collected from multiple sources over the past decades to aid physicians to make data-backed critical clinical decisions. However, studies have shown that unintentional biases in the source data and in the process can affect these precision medicine efforts.

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Increasing diversity in genomics requires investment in equitable partnerships and capacity building

Cited by 43 publications

References 22 publications

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

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

Counter the weaponization of genetics research by extremists

Data bias in precision medicine

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