AAPA Statement on Race and Racism

Fuentes, Agustín; Ackermann, Rebecca Rogers; Athreya, Sheela; Bolnick, Deborah A.; Lasisi, Tina; Lee, Sang‐Hee; McLean, Shay‐Akil; Nelson, Robin

doi:10.1002/ajpa.23882

Cited by 122 publications

(92 citation statements)

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“…In a 2015 study, we compared self‐declaration based on skin color (used by IBGE) and HLA data from more than 71 000 voluntary bone marrow donors from all regions of the Rio Grande do Sul state . We found that HLA itself is a better predictor of ancestry than the self‐declaration based on skin color, corroborating findings from other studies with other genetic markers . In 2017, in an extensive study using more than 97 000 voluntary bone marrow donors across the state, we tested whether there was population structure according to the state's political division .…”

Section: Bone Marrow Donors and Other Studies In Urban Populationssupporting

confidence: 60%

“…42 We found that HLA itself is a better predictor of ancestry than the selfdeclaration based on skin color, corroborating findings from other studies with other genetic markers. 12,[15][16][17][18] In 2017, in an extensive study using more than 97 000 voluntary bone marrow donors across the state, we tested whether there was population structure according to the state's political division. 43 The results indicated that there is no population structure according to this division, but there is genetic (HLA) structure reflecting the cultural regions associated to the colonization history of Rio Grande do Sul.…”

Section: Bone Marrow Donors and Other Studies In Urban Populationsmentioning

confidence: 99%

“…However, another possible interpretation is that there is indeed an increase in African ancestry from White to Black categories, with Brown showing intermediate values, as expected if they represent a proxy to genomic ancestry (though with low precision). Ironically, even though we recognize that classifications based on skin color may sound obsolete, and that genomic variation across human populations is fully incompatible with the idea of “races,” recognizing that there is some genomic ancestry structure among these categories may help to guide best health care practices towards historically neglected groups.…”

Section: Introductionmentioning

confidence: 99%

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HLA diversity in Brazil

Boquett

Bisso‐Machado

Zagonel-Oliveira

et al. 2019

HLA

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Brazil is the fifth largest country in the world in area and the fifth most populous. The Brazilian voluntary Bone Marrow Donor Registry is the third largest in terms of number of donors in the world, being a valuable source of HLA genetics to characterize the donor population of Brazil as well. The genetic background of the Brazilian population is quite heterogeneous, resulting from 5 centuries of admixture among Native Americans, Europeans and Africans, making the Brazilian population unique in terms of genetic ancestry. The unique characteristics of populations in different Brazilian regions make them an exciting focus for genetic diversity studies. Studies on HLA genetic diversity of Brazilian populations have been conducted since the late 1980s and, in this review, we highlight the main findings from studies carried out in Brazil based on classical HLA. In addition, we calculated the genetic distance from the molecular data of the studies included in this review in order to have a broader view of the HLA diversity in Brazilian populations. We emphasize that characterization of HLA diversity is not only important for transplantation programs, but can shed a light on ancestry, history and other demographic patterns with or without association with autoimmune disease.

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Section: Bone Marrow Donors and Other Studies In Urban Populationssupporting

confidence: 60%

Section: Bone Marrow Donors and Other Studies In Urban Populationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HLA diversity in Brazil

Boquett

Bisso‐Machado

Zagonel-Oliveira

et al. 2019

HLA

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“…Given the strong associations between deprivation, race, and Covid-19 in this study, our findings may be due, in part, to discriminatory hiring practices within the labor market. Finally, any genetic differences amongst races are highly unlikely to explain our results, as multiple studies have shown that genetic variation within racial groups is greater than between racial groups [45][46][47].…”

Section: Plos Onementioning

confidence: 89%

Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank

et al. 2020

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Background Coronavirus disease 2019 (Covid-19) has rapidly infected millions of people worldwide. Recent studies suggest that racial minorities and patients with comorbidities are at higher risk of Covid-19. In this study, we analyzed the effects of clinical, regional, and genetic factors on Covid-19 positive status. Methods The UK Biobank is a longitudinal cohort study that recruited participants from 2006 to 2010 from throughout the United Kingdom. Covid-19 test results were provided to UK Biobank starting on March 16, 2020. The main outcome measure in this study was Covid-19 positive status, determined by the presence of any positive test for a single individual. Clinical risk factors were derived from UK Biobank at baseline, and regional risk factors were imputed using census features local to each participant’s home zone. We used robust adjusted Poisson regression with clustering by testing laboratory to estimate relative risk. Blood types were derived using genetic variants rs8176719 and rs8176746, and genomewide tests of association were conducted using logistic-Firth hybrid regression. Results This prospective cohort study included 397,064 UK Biobank participants, of whom 968 tested positive for Covid-19. The unadjusted relative risk of Covid-19 for Black participants was 3.66 (95% CI 2.83–4.74), compared to White participants. Adjusting for Townsend deprivation index alone reduced the relative risk to 2.44 (95% CI 1.86–3.20). Comorbidities that significantly increased Covid-19 risk included chronic obstructive pulmonary disease (adjusted relative risk [ARR] 1.64, 95% CI 1.18–2.27), ischemic heart disease (ARR 1.48, 95% CI 1.16–1.89), and depression (ARR 1.32, 95% CI 1.03–1.70). There was some evidence that angiotensin converting enzyme inhibitors (ARR 1.48, 95% CI 1.13–1.93) were associated with increased risk of Covid-19. Each standard deviation increase in the number of total individuals living in a participant’s locality was associated with increased risk of Covid-19 (ARR 1.14, 95% CI 1.08–1.20). Analyses of genetically inferred blood types confirmed that participants with type A blood had increased odds of Covid-19 compared to participants with type O blood (odds ratio [OR] 1.16, 95% CI 1.01–1.33). A meta-analysis of genomewide association studies across ancestry groups did not reveal any significant loci. Study limitations include confounding by indication, bias due to limited information on early Covid-19 test results, and inability to accurately gauge disease severity. Conclusions When assessing the association of Black race with Covid-19, adjusting for deprivation reduced the relative risk of Covid-19 by 33%. In the context of sociological research, these findings suggest that discrimination in the labor market may play a role in the high relative risk of Covid-19 for Black individuals. In this study, we also confirmed the association of blood type A with Covid-19, among other clinical and regional factors.

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“…Scientists who are beginners to discussions of race, lacking guidance or background knowledge, may adopt unevolved viewpoints or weak policies that unintentionally harm BIPOC [11] or contribute to an erosion of trust among people of different racial or ethnic backgrounds in a lab group. Harmful approaches include engaging in objectifying thought experiments that question the instrumental value of BIPOC in science, confusing race as a biological entity as indicated by human genetic variation instead of a socially constructed concept [12], arguing that the unbiased nature of science and scientists precludes racial biases in scientific workplaces, and hijacking discussions of race with anecdotes from other types of discrimination (e.g., gender-based and/or class-based) without employing an intersectional framework [13]. As scientists of color who actively engage in work to promote racial and ethnic DEI, we have encountered all of these harmful scenarios and more.…”

Section: Introductionmentioning

confidence: 99%

Ten simple rules for building an antiracist lab

2020

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Demographics of the science, technology, engineering, and mathematics (STEM) workforce and student body in the US and Europe continue to show severe underrepresentation of Black, Indigenous, and people of color (BIPOC). Among the documented causes of the persistent lack of diversity in STEM are bias, discrimination, and harassment of members of underrepresented minority groups (URMs). These issues persist due to continued marginalization, power imbalances, and lack of adequate policies against misconduct in academic and other scientific institutions. All scientists can play important roles in reversing this trend by shifting the culture of academic workplaces to intentionally implement equitable and inclusive policies, set norms for acceptable workplace conduct, and provide opportunities for mentorship and networking. As scientists are increasingly acknowledging the lack of racial and ethnic diversity in science, there is a need for clear direction on how to take antiracist action. Here we present 10 rules to help labs develop antiracists policies and action in an effort to promote racial and ethnic diversity, equity, and inclusion in science.

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AAPA Statement on Race and Racism

Cited by 122 publications

References 0 publications

HLA diversity in Brazil

HLA diversity in Brazil

Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank

Ten simple rules for building an antiracist lab

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