Celebrating 50 years since Lewontin's apportionment of human diversity

Edge, Michael D.; Ramachandran, Sohini; Rosenberg, Noah A.

doi:10.1098/rstb.2020.0405

Cited by 6 publications

(5 citation statements)

References 46 publications

(104 reference statements)

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“…In 1972, Richard Lewontin found that the majority of human genetic variation was found within (85%) rather than between (15%) racial groups [39]. This fundamental result has been replicated many times since, and it is often taken to support the irrelevance of racial classification to human genetic variation [40,41]. This is consistent with the notion that race and ethnicity are purely social constructs with little or no biological significance [42].…”

Section: Adverse Drug Reactionsmentioning

confidence: 73%

Population Pharmacogenomics for Health Equity

Jordan,

Sharma,

Mariño-Ramírez

2023

Genes

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Health equity means the opportunity for all people and populations to attain optimal health, and it requires intentional efforts to promote fairness in patient treatments and outcomes. Pharmacogenomic variants are genetic differences associated with how patients respond to medications, and their presence can inform treatment decisions. In this perspective, we contend that the study of pharmacogenomic variation within and between human populations—population pharmacogenomics—can and should be leveraged in support of health equity. The key observation in support of this contention is that racial and ethnic groups exhibit pronounced differences in the frequencies of numerous pharmacogenomic variants, with direct implications for clinical practice. The use of race and ethnicity to stratify pharmacogenomic risk provides a means to avoid potential harm caused by biases introduced when treatment regimens do not consider genetic differences between population groups, particularly when majority group genetic profiles are assumed to hold for minority groups. We focus on the mitigation of adverse drug reactions as an area where population pharmacogenomics can have a direct and immediate impact on public health.

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Section: Adverse Drug Reactionsmentioning

confidence: 73%

Population Pharmacogenomics for Health Equity

Jordan,

Sharma,

Mariño-Ramírez

2023

Genes

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“…Fifty years ago, in the pre-DNA sequencing era, Richard Lewontin marshalled data on protein polymorphisms to show that the vast majority of human genetic diversity falls within rather than between racial groups. This seminal result has held up remarkably well over the years, through the invention of DNA sequencing and well into the genomic era 9 , 10 . Lewontin concluded that race is both a destructive force in human relations and a construct that has virtually no genetic significance.…”

Section: Discussionmentioning

confidence: 96%

“…His seminal paper showed that ~85% of human genetic variation was found within racial groups, compared to only ~15% between groups, and he concluded that race is “of no social value … is positively destructive of social and human relations … [and is] of virtually no genetic or taxonomic significance” 8 . Lewontin’s fundamental result has been confirmed numerous times since and is widely taken to support the irrelevance of genetic differences between racial and ethnic groups 9 , 10 . Here, we consider differences in the frequencies of pharmacogenomic variants 11 , which affect how individuals respond to medication and are thus particularly relevant to clinical decision-making, among the three largest racial and ethnic groups in the United States: Black, Hispanic, and White 12 .…”

Section: Introductionmentioning

confidence: 86%

The Apportionment of Pharmacogenomic Variation: Race, Ethnicity, and Adverse Drug Reactions

Jordan

Sharma

Nagar

et al. 2022

MRAJ

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Fifty years ago, Richard Lewontin found that the vast majority of human genetic variation falls within (~85%) rather than between (~15%) racial groups. This result has been replicated numerous times since and is widely taken to support the notion that genetic differences between racial groups are trivial and thus irrelevant for clinical decision-making. The aim of this study was to consider how the apportionment of pharmacogenomic variation within and between racial and ethnic groups relates to risk disparities for adverse drug reactions. We confirmed that the majority of pharmacogenomic variation falls within (97.3%) rather than between (2.78%) the three largest racial and ethnic groups in the United States: Black, Hispanic, and White. Nevertheless, pharmacogenomic variants showing far greater within than between-group variation can have high predictive value for adverse drug reactions, particularly for minority racial and ethnic groups. We predicted excess adverse drug reactions for minority Black and Hispanic groups, compared to the majority White group, and considered these results in light of the apportionment of genetic variation within and between groups. For 85% within and 15% between group variation, there are 700 excess adverse drug reactions per 1,000 patients predicted for a recessive effect model and 300 for a dominant model. We found high numbers of predicted Black and Hispanic excess adverse drug reactions for widely prescribed platinum chemotherapy compounds, such as cisplatin and oxaliplatin, as well as controlled narcotics, including fentanyl and tramadol. Our results indicate that race and ethnicity, while imprecise proxies for genetic diversity, correlate with patterns of pharmacogenomic variation in a way that is clearly relevant to medical treatment decisions. The effects of this variation is particularly pronounced for Black and Hispanic minority groups, owing to genetic differences from the majority White group. Treatment decisions that are made based on (assumed) White pharmacogenomic variant frequencies can be harmful for minority groups. Ignoring clinically relevant genetic differences among racial and ethnic groups, however well-intentioned, will exacerbate rather than ameliorate health disparities.

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“…With this perspective, they have a simple interpretation as a population-level generalization of an individual-level statistic. They also have a natural connection to a fundamental computation in human population genetics – the apportionment of genetic diversity among different levels of genetic structure ( Edge et al. 2022 ; Lewontin 1972 ) – which can be viewed in terms of various mean pairwise dissimilarities across certain subsets of individuals ( Rosenberg 2011 ).…”

Section: Introductionmentioning

confidence: 99%

When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?

Liu,

Ahsan,

Martheswaran

et al. 2023

Statistical Applications in Genetics and Molecular Biology

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Allele-sharing statistics for a genetic locus measure the dissimilarity between two populations as a mean of the dissimilarity between random pairs of individuals, one from each population. Owing to within-population variation in genotype, allele-sharing dissimilarities can have the property that they have a nonzero value when computed between a population and itself. We consider the mathematical properties of allele-sharing dissimilarities in a pair of populations, treating the allele frequencies in the two populations parametrically. Examining two formulations of allele-sharing dissimilarity, we obtain the distributions of within-population and between-population dissimilarities for pairs of individuals. We then mathematically explore the scenarios in which, for certain allele-frequency distributions, the within-population dissimilarity – the mean dissimilarity between randomly chosen members of a population – can exceed the dissimilarity between two populations. Such scenarios assist in explaining observations in population-genetic data that members of a population can be empirically more genetically dissimilar from each other on average than they are from members of another population. For a population pair, however, the mathematical analysis finds that at least one of the two populations always possesses smaller within-population dissimilarity than the value of the between-population dissimilarity. We illustrate the mathematical results with an application to human population-genetic data.

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Celebrating 50 years since Lewontin's apportionment of human diversity

Cited by 6 publications

References 46 publications

Population Pharmacogenomics for Health Equity

Population Pharmacogenomics for Health Equity

The Apportionment of Pharmacogenomic Variation: Race, Ethnicity, and Adverse Drug Reactions

When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?

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