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Short answer: Most often ancestry and environment together mould disease risk. Ancestry alone directly confers disease risk less often, specifically so in cases where single gene mutations have outsize effects. Tay–Sachs disease – Wikipedia, Sickle-cell disease – Wikipedia and Cystic fibrosis – Wikipedia are some well-known examples of the latter.

Longer answer if interested

Why It’s So Difficult to Scientifically Tease Apart The Role Of Ancestry In Disease Causes

Human ancestry is the outcome of choice that liberally pockmarks both past and present with gratuitous violence and calamities since it’s a choice contrived to mediate and enforce differential access to resources. This choice manifests itself as caste, class, ethnicity, linguistic group, race, sect, tribe, etc. They’re social, i.e., explicitly political and cultural, not biological categories (1) but they end up influencing biology anyway. Here’s how.

  • Human societies tend to practice some form of social stratification or another. Over time, differential access to critical healthful life-sustaining resources such as quality education, health care and nutrition impact health, especially since social stratification-engendered privations tend to be experienced across generations.
  • Thus, health disparity is the outcome of historical inequities a particular social category experiences at the hands of what usually tends to be a long-prevailing hegemony.
  • Persistence of social stratifications across generations thus end up influencing biology by differentially influencing disease risk through the human-created construct of health disparity.
  • The ethically unambiguous and appropriate place for ancestry in biomedicine lies in helping to try to tease apart the relative contributions of health disparity versus genetic predisposition to disease.
  • However, it’s well nigh impossible to disentangle genetic predisposition from the many environmental confounders health disparities impose on disease risk. This stymies the effort to accurately parse and pinpoint the role of ancestry in many disease causes.

Problem is by tending to examine it devoid of its inherent sociological context, biomedicine artificially insulates ancestry. That leaves its role vulnerable to exploitation by the scientific flavor of the day, which these days is genomics.

The genomic era is like the proverbial hammer primed to seek and find nails everywhere so it’s become quite the fashion to privilege or attempt to privilege genetic predilection even in the case of multi-factorial diseases. This problem is also grounded in the fact that biomedicine has evolved an inherently siloed approach such that super-specialists, be they molecular biologists, geneticists, epidemiologists, public health researchers, etc., examine a given health issue through the lens of their training often without simultaneously attempting to look beyond, especially at the sociological context of disease. Thus, ancestry in the form of race/ethnicity has been and continues to be used as a proxy for genotype, albeit devoid of sociological context.

Official guidelines add to the problem, being inadequate and/or inaccurate and/or too riddled with ambiguity. For example, in the US, when classifying their research subjects, NIH-funded scientists are required to adhere to the racial and ethnic categories specified by the Office of Management and Budget (OMB)’s Directive #15, the so-called NIH Inclusion Policy and Guidelines (see below from 2),

‘American Indian or Alaska Native, Asian, Black or African American, Hawaiian or Pacific Islander, and White, Hispanic or Latino and Not Hispanic or Latino’

  • At least one survey of 18 NIH-funded scientists (11 men, 7 women) (3) reports these guidelines
    • Are applied too unquestioningly regardless of their utility or accuracy.
    • Are bureaucratic, one size fits all, catch all, inflexible, an example cited being Barack Obama. Categorize as Black or Caucasian?
    • Are difficult to comply with in geographic areas with few minority residents.
    • Force minority inclusion numbers that yield data lacking sufficient statistical power to provide meaningful results, i.e., difficult to generate representative and therefore generalizable data sets.
  • A study from the UK (4) reports similar flawed approach to study of racial/ethnic contribution to health and disease.

Genomic Approaches To Assess Ancestry Remain Inherently Flawed

Ancestry-informative marker – Wikipedia (AIMs) are inherently misleading and fraught with flawed assumptions (5, 6, 7, 8, 9). So what are AIMs? They’re population-specific markers, typically Single-nucleotide polymorphism – Wikipedia (SNPs) that occur at different frequencies in different populations. Since they’re shared by all humans, rather than presence or absence, analysis focuses on their frequency.

These days TV ads about learning one’s ancestry are plentiful. Just send a saliva sample in and get genetic genealogical results back. Too deceptively simple but also flawed to boot.

Obviously a test sample is compared to reference samples of Africans, Asians, Europeans, Native Americans, etc.

  • Who are the ‘reference populations’ for each race or ethnicity? To assign ‘purity’, ideally they should be groups that have remained immobile, isolated and endogamous for millennia till date since reference samples should represent ‘pure’ examples of different ethnic/racial categories, the standard against which the test sample would be compared.
  • Obviously such reference populations are an impossibility for much of the world’s population.
  • Instead ‘small groups of contemporary people‘ (9) are chosen as representative samples for a particular continent or region or ethnic, linguistic or tribal group.
  • Who is chosen? What are the criteria used to choose one individual and not another to supply the reference sample?
  • How many people from a given population (caste/ethnicity/linguistic group/race/sect/tribe) are sampled to develop a representative reference sample base?
  • How many are necessary? How many sufficient? 100, 1000, 10000, 100000, …?
  • How are thresholds set to determine how a given result is interpreted to include or exclude a particular population?

The answers to these scientifically crucial questions lie hidden behind the legal iron curtain of proprietary information preventing even the bare minimum in terms of rigorous science, namely to independently replicate and thereby verify (9). Thus, the widely advertised genomic categorization of ancestry using AIM touted by various commercial entities is more or less the outcome of a technological Sleight of hand – Wikipedia or two or three or more.

Diseases Found To Be More Prevalent in A Particular Race/Ethnicity Are Typically Monogenic

Tay–Sachs disease – Wikipedia & BRCA1 – Wikipedia mutations

At least 2 disease-causing/associated genes are more prevalent among Ashkenazi Jews – Wikipedia, infantile form of Tay–Sachs disease – Wikipedia and BRCA1 – Wikipedia mutations associated with higher risk for breast cancer.

Tay-Sachs is an autosomal recessive (Dominance (genetics) – Wikipedia) Genetic disorder – Wikipedia caused by a Mutation – Wikipedia in HEXA – Wikipedia gene on Chromosome 15 (human) – Wikipedia where children usually die by the age of 4. Disease is caused by the impaired function of lysosomal enzyme, Hex A.

Higher prevalence in these two instances is presumed to owe to the fact that the Jewish population descended from a small number of founders and remained largely endogamous (10).

Genetic counseling and prenatal screening are also advised for Cajuns – Wikipedia in Louisiana and French Canadians – Wikipedia since similar mutations have been identified among them.

Sickle-cell disease – Wikipedia

An adaptation to thwart malaria, sickle cell is more common among those with West African ancestry, specifically those with the globin S (betas) mutation (11).

Cystic fibrosis – Wikipedia

An autosomal recessive genetic disorder caused by mutations in Cystic fibrosis transmembrane conductance regulator – Wikipedia (CFTR) gene, it’s found to be more prevalent among people of European descent (12, 13).

More Accurate To Envision Ancestry As A Continuum Rather Than Groups Of Independently Evolving Discrete Units

An additional challenge is the fact that ancestry as a social construct is fast becoming less categorical as populations meet and meld as perhaps never before, even while they may have remained geographically isolated for varying lengths of time here and there in previous millennia.

Consider USA for example, a country that assesses race in its census.

  • The category ‘Other’ was first listed in the US 1910 census. Now listed as ‘Some Other Race’, in the 2010 census it had become the 3rd largest category after ‘White’ and ‘Black’ (14, 15).
  • In recent years, 15% of US marriages are between people of different ethnicities and races.
  • One in seven US infants is today born into a ethnically and/or racially mixed family.
  • A particular genomics example perfectly hints at the potentially vast complexity hidden underneath the surface of the race/ethnicity categories commonly used in our times. Complete genomic sequences of two famous European origin American scientists, James Watson – Wikipedia, Craig Venter – Wikipedia, and Seong-Jin Kim, an Asian-origin scientist, showed the former shared fewer (461000) SNPs with each other than they each shared with the Asian (569000 and 481000, respectively) (16, 17), something utterly unlikely to be discerned from physical appearance alone.
  • A genomic analysis of self-identified European Americans (n = 326), African Americans (n = 324) and Hispanics (n = 327) in Manhattan, New York, revealed such substantial ancestral mix in both African Americans and Hispanics, the authors concluded (18, emphasis mine; see figure below from 19).

‘A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative

  • Needless to say, such melding happened or is happening faster in some countries and especially faster in large cosmopolitan cities. Largely the mix of Native American (Amerindian), European (mainly Portugal) and African, Brazil is a country that famously embodies more than anything else racial ambiguity (20).

Finally, data also suggests higher genetic diversity within races (85%) rather than between races (15%) (21), which only further undermines the practical value of race/ethnicity in dissecting disease risks and causes at group level. This is especially the case for Africa, the continent with the greatest degree of genetic variation (see below from 22 quoted in 9).

‘For many regions of the human genome, there are more variants found among people of Africa than found among people in the rest of the world. This is probably because humans have resided in Africa for much longer than we have resided any place else in the world, so our species had time to accumulate genetic changes within the people in Africa.’

In other words, race/ethnic categories such as African, Asian, Caucasian, Hispanic, Latino, White poorly predict human biological similarity and diversity. As Cuban geneticist Dr. Beatriz Marcheco put it (23),

‘The classic mirror reflects skin color; but the DNA mirror reflects our common ancestors’

Race/ethnicity are thus becoming less and less relevant as proxies for genotype or rather the discernible truth about ancestry lies more and more between rather than within these commonly accepted social categories.

Some Examples Where Misapplication Of Ancestry Obfuscates Rather Than Clarifies Cause For Disease Predisposition

Hypertension – Wikipedia

Hypertension and its clinical outcomes such as heart disease, stroke and renal failure are so much more prevalent among African Americans that a racial predisposition ascribed back in the 20th century still erroneously prevails as a dogma (19). Erroneous because large studies comparing West African, Caribbean and American Blacks show high prevalence of hypertension among African Americans is an outlier, being lower among other Blacks (24, also see figures below from 19).

  • Low blood pressures in rural West Africa that change little with age.
  • Similar average blood pressures to White North Americans among West Indian Blacks.
  • Higher blood pressure among urban African Americans from Maywood in Chicago.
  • Obesity, high sodium and low potassium intake, the lifestyle factors known to increase blood pressure matched blood pressure averages among these three groups of Blacks. In other words, abrupt diet and lifestyle changes better explain hypertension rates among African Americans.
  • The specific example of hypertension reveals how difficult it is to assert which is more consequential, nature or environment, simply because it’s practically impossible to observe the obverse, people from Africa leading a US lifestyle without experiencing either racial or class inequities.

An even larger study of 85000 subjects including Whites from 8 surveys in the US, Canada and Europe, and 3 surveys among Blacks in Africa, the Caribbean and the US, showed preventable causes of hypertension overlap across races and ethnicities (25). Meantime a much smaller (n = 1056) US study (26) on African Americans served as the basis for the US FDA’s approval of a hypertension drug, BiDil (Isosorbide dinitrate/hydralazine – Wikipedia), supposedly designed for African Americans (27, 28).

Moral? Far from vaunted impartiality, an example of how economics (patents) and politics (tokenism) trump science.

The issue of causality is further complicated by the fact that blood pressure regulation is extremely complex and thus unlikely to be explained by genes alone. One of the largest blood pressure GWAS (Genome-wide association study – Wikipedia) examined 200000 subjects and found the 29 genetic markers most strongly associated with blood pressure could only account for 23% of risk for hypertension (29). Since lifetime hypertension risk in the US is ~85%, this means genomics has so far provided little by way of predictive value. Cherry on the cake is blood pressure susceptibility variants are similar among subjects with African, Asian, European and South Asian ancestry (29).


Judenkrankheit or Jews’ disease, as recently as 1904, this is how physicians in the US and Europe tended to perceive diabetes (see below from 30).

‘THERE IS NO RACE, WHICH is so subject to diabetes as the Jews,” wrote W. H. Thomas in 1904 in the eugenically obsessed language of his day. Thomas, a New York physician, was voicing an almost universally held belief in the United States that of all the “races,” Jews had the greatest likelihood of developing diabetes. At the same time, most members of the medical community considered the prevalence of diabetes among Blacks to be unusually low. In the words of a Johns Hopkins physician in 1898, “Diabetes is a rare disease in the colored race”.’

Fast forward 100 or so years and in the US,

  • Diabetes rates have sky-rocketed among African Americans to 2X those in Whites while they’ve declined among Jews.
  • Today, Pima people – Wikipedia have the highest rate of Diabetes mellitus type 2 – Wikipedia in the world and of course, since they form a homogenous group, unsurprisingly, mapping their genetics has become an intense focus of research interest. Laughable weren’t it so soul-crushingly tragic for the following reasons.
    • Before the advent of European American encroachers on their land after the American Civil War, the Pima had a reputation for excellent farming and lived independent, autonomous lives along the Gila river on lands presently known as Arizona (31). They even called themselves Akimel O’Tham or the River People.
    • White settlers directly competing for irrigation rights, the 1877 Desert Land Act which ‘required bona fide application of water to the land to obtain title‘, new dams that re-directed water away from traditional Pima farms (9), all these human-made interventions forced Pima to abandon their age-old ways of life in a matter of a few decades.
    • A health survey in 1902 found a single case of diabetes among the Pima. By the 1930s, this number had increased to >500.
    • With the completed Coolidge Dam not sending enough water their way, their traditional farming essentially going bust, the Pima quickly sank into abject poverty and started dying early.
    • Like a benighted god the US federal government rode to the rescue, providing Pima free government surplus food and what food it is! Refined white flour, processed cheese, lard, candy, chips, refined sugar, grape juice, macaroni when the Pima’s original diet consisted of (32 quoted in 9).

‘…seeds, buds, fruits and joints of various cacti; seeds of the mesquite, ironwood, palo verde, amaranth, salt bush, lambsquarter, horsebean and squash; acorns and other wild nuts; . . . roots and bulbs of the sandroot (wild potato) . . . deer, antelope, ..rabbits, quail, dove, wild ducks, wild turkey.’

    • By the mid-20th century, this ancient diet had been entirely supplanted by boxes and boxes of macaroni and cheese. Where Pima dietary fat intake was 15% in the 1890s, it had increased to an incredible 40% by the 1990s (33).
    • And yet it apparently sounds eminently reasonable and soundly scientific to probe and probe Pima genetics to sincerely try to understand their sky-high rates of diabetes these days (34). An exercise in callousness, ignorance, stupidity or all three.

At this point it becomes necessary to ask whether it is really reasonable to highlight ancestry as a mechanistic contributing factor to diabetes when rates can be evidently higher than the norm and drop to average in just 100 years in one group while they increase and increase in two other groups over the same period?

A simpler explanation is how abrupt diet and lifestyle changes impact life trajectories and chronic disease risk in the short-term. Plausible proof? Traditional rural dwelling societies practicing ‘traditional culture’ have vanishingly low rates of diabetes compared to their counterparts newly adapted to ‘westernized’ diets and lifestyle (see below from 9).

Attitudes ranging from the cavalier to sheer ineptitude suggest the prevailing culture of biomedical science is ill-equipped to deal with divisive political topics such as ancestry. Science exists within society, not outside of it and the prevalent untenable allegiance to the implausible notion of striving to be perceived as ahistorical and apolitical ill-serves biomedical science and society alike.

And so we’re back where we started, namely unable to parse environmental and genetic factors in assigning causes to many, especially multi-factorial diseases. Impasse largely owing to biomedical scientists ignoring sociology when probing the role of ancestry, specifically race/ethnicity, in diseases.


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