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Admixture analysis and genetic causation: some quotes from the literature

A common comment on bias in scientific peer review is that reviewers don’t usually say openly they are applying double standards. Instead, they just silently increase their standards. If their bias against some finding is strong, the evidential burden to meet goes to infinity, making sure that nothing is rigorous enough to pass review. A case in point of this behavior was very clear in our attempts to get an admixture analysis for race and intelligence published. Although admixture analysis is commonplace in medical genetics and in scientific anthropology, somehow the interpretation of such findings is totally different when one changes the trait.

For instance, one really hostile reviewer recently wrote:

2. Second, I made the point that the fundamental logic of the study is weak. The authors simply state that they are following accepted protocol in genetic epidemiology. For one thing, the authors provide no basis to believe that their study follows accepted protocol in genetic epidemiology – their approach is certainly not widely accepted as a means of demonstrating a causal influence of continental ancestry on cognitive/behavioural traits; for another, saying ‘this is the way things are done’ does not rebut my point that the logic of the study is flawed.

[…]

4. but I do think that any such enquiries have to be held to a very high standard of evidence, given the potential social harms of misguided findings. The evidence presented here is not of a high standard at all.

I have discussed the topic of causality and admixture results at length (e.g. in my long PING write-up, and in other places), and it’s also done in this version of the paper (reviewer never commented on that, as to be expected). However, we can easily disprove his claim that admixture findings are not generally taken to indicate causality. We thank this particular reviewer for openly admitting his double standards.

The collection of quotes below is obviously not exhaustive. Indeed, I compiled most of these in about two hours. One can find 100s of such quotes if determined to spend a day or two. To find such quotes, one can use search queries like this one for African Americans.

Medical genetics

African Americans and health outcomes

In the Atherosclerosis Risk in Communities (ARIC) Study, African Americans are twice as likely as whites to develop incident type 2 diabetes—a disparity which persists even after extensive adjustment for socioeconomic status (SES) and behavioral risk factors [4]. This persistent disparity suggests that genetic factors may contribute to ethnic differences in susceptibility to type 2 diabetes.

[…]

Given the observed ethnic/racial disparities in diabetes prevalence, we hypothesized that some diabetes susceptibility alleles are present at higher frequency in African Americans than in European Americans, resulting in association between genetic ancestry and diabetes risk that is independent of its association with other non-genetic risk factors for type 2 diabetes. Thus we sought 1) to establish the association of genetic ancestry with diabetes and related quantitative traits in African Americans, after accounting for the non-genetic risk factors, and 2) to identify diabetes susceptibility loci by conducting a genome-wide admixture mapping scan.

[…]

In summary, in community-based populations with more than 7,000 African Americans, we found that genetic ancestry is significant associated with type 2 diabetes above and beyond the effects of markers of SES, and we detected several suggestive loci that may harbor genetic variants modulating diabetes risk. These results suggest that in African Americans, genetic ancestry has a significant effect on the risk of type 2 diabetes that are independent of the contribution of SES, but that no single locus with a major effect explains a large portion of the observed disparity in diabetes risk between African Americans and European Americans. In addition, they suggest that genetic measured African ancestry contributes to the risk of type 2 diabetes via both genetic and non-genetic pathways. The effect of ancestry on any individual locus in the genome is likely to be modest, but in aggregate, differences in ancestry may contribute substantially to the observed ethnic disparity in risk of type 2 diabetes.

This study is particularly noteworthy in that the authors explicitly present SIRE gaps that remain after extensive (sociologist fallacy style) controls as being evidence of genetic causation. They afterwards then hypothesize an association between genetically measured ancestry and outcome risk, which they then find. This is basically the same reasoning used by Jensen in in 1969 and forwards.

We have demonstrated that genetic ancestry may serve as a biomarker for identifying smokers who would benefit from targeted counseling regarding smoking cessation [41], [42]. One important implication of our findings is that there may be rare genetic variants relevant to smoking associated lung function decline that are population-specific and which co-vary with genetic ancestry [43]. While we cannot rule out that some of these associations may be in part due to environmental factors which co-vary with ancestry, these results highlight the scientific advantages of studying racially mixed populations. Future analyses should include admixture mapping to identify genomic regions associated with rate of lung function decline.

In summary, a consistent association of African ancestry with asthma risk was observed in a large case-control sample of self-reported African American subjects. Although confounding effects attributable to other relevant risk factors cannot be ruled out, we replicate previous findings and support the notion that ethnic disparities in asthma incidence are affected, in part, by genetic determinants. Frequency differences for risk alleles across populations and/or differential gene-environmental interactions may lead to differential disease susceptibility.

Due to this heterogeneity, genetic admixture analysis offers a unique opportunity for studying the role of genetic factors within a single, admixed population, independent of social factors, and comorbidities. Ancestry informative markers, AIMs, are genetic loci showing alleles with large frequency differences between populations that can be used to estimate bio-geographical ancestry at the level of the population and individual. Ancestry estimates at both the subgroup and individual level can be directly instructive regarding the genetics of the phenotypes that differ qualitatively or in frequency between populations (Shriver et al., 2003). Specifically, an association between genetic ancestry and a disease phenotype within an admixed group such as AAs may be an indicator of genetic factors underlying differential expression among racial groups (Peralta et al., 2010).

A greater proportion of African genetic ancestry is independently associated with higher FG
levels in a non-diabetic community-based cohort, even accounting for other ancestry
proportions, obesity and SES. The results suggest that differences between African-Americans
and whites in type 2 diabetes risk may include genetically mediated differences in glucose
homeostasis.

The mechanisms that underlie differences in sleep characteristics between European Americans (EA) and African Americans (AA) are not fully known. Although social and psychological processes that differ by race are possible mediators, the substantial heritability of sleep characteristics also suggests genetic underpinnings of race differences. We hypothesized that racial differences in sleep phenotypes would show an association with objectively measured individual genetic ancestry in AAs.

[…]

Ancestry-phenotype association tests, which quantify associations between measured genetic ancestry and a phenotype in an admixed population, like AAs, can be used to test the extent to which the genetic characteristics underlying race may be responsible for observed population level differences.39,40 In the context of sleep, ancestry-phenotype association tests assume that multiple genetic variants, each with small effects on sleep, may have different allele frequencies in different continental populations that contributed to the admixed population. Because individuals from the admixed population inherit varying proportions of their genome from different ancestral populations, one expects contribution from any one ancestral population to show a wide range of variation (theoretically, spread between 0–100%). Any association between ancestry and a phenotype in an admixed group, then, indicates that multiple variants across the genome that have been inherited from one particular ancestral population are related to variation in the phenotype.39–45 In this manner, objectively measured genetic ancestry enables us to test the uniquely genetic facet of “race” parsed from the cultural, behavioral, and psychosocial aspects that may be responsible for the observed phenotypic differences.

[…]

By utilizing the genetic variability attributable to continental admixture, we show for the first time that visually scored percent SWS and NREM EEG delta are associated with %AF in AAs. Even after adjusting for several demographic, socioeconomic and clinical covariates, %AF explained between 9% and 11% of the variance in SWS in AAs. These results show that AAs have inherited multiple alleles (either few alleles of large effect sizes or several alleles of moderate to low effect sizes) from their African ancestors that may pre-dispose them to lower percent SWS. This association between measured genetic ancestry and SWS clearly establishes a partial genetic basis underlying the observed racial differences in this dimension of sleep.

The authors even write out the Jensen logic in the abstract.

The role of genetic predisposition in this disparity is supported by two admixture mapping

studies of AAs which demonstrated that greater proportion of European ancestry was inversely
associated with fibroids in AA women.

Latin Americans and health

Some recent studies, which also used AIMs, demonstrated/ suggested that the genomic Amerindian ancestry may be protective against hypertension in women from the United States [10] , protective against metabolic syndrome in the population of Costa Rica [11] and protective against Alzheimer’s disease in Brazilian population [12] . Furthermore, recent studies in the Brazilian population showed that Amerindian individuals had lesser arterial stiffness and hypertension [13 – 14] . These studies suggest that lower risk of diseases studied in individuals with Amerindian ancestry may be due to the existence of protective genetic factors associated with this ancestry.

Significant questions remain unanswered regarding t he genetic versus environmental contributions to racial/ethnic differences in sleep and circadian rhythms. We addressed this question by investigating the association betw een diurnal preference, using the MorningnessAEveningness questionnaire (MEQ), and ge netic ancestry within the Baependi Heart Study cohort, a highly admixed Brazi lian population based in a rural town. Analysis was performed using measures of ance stry, using the Admixture program, and MEQ from 1,453 individuals. We found a n association between the degree of Amerindian (but not European of African) ancestry and morningness, equating to 0.16 units for each additional percent of Amerindian ancestry, after adjustment for age, sex, education, and residential zone. To our knowledge, this is the first published report identifying an association b etween genetic ancestry and MEQ, and above all, the first one based on ancestral contrib utions within individuals living in the same community. This previously unknown ancestral d imension of diurnal preference suggests a stratification between racial/ethnic gro ups in an as yet unknown number of genetic polymorphisms.
The authors essentially cover the entire reasoning in their abstract.

Anthropology

Pygmy height

Note the title “genetic determination”!

Considering a subset of 213 individuals for which DNA was available, we were able to formally compare the individual variation in height with the neutral genetic variation among individuals from the different Pygmy and Non-Pygmy populations.

Controlling for the binary categorization of individuals as Pygmies or Non-Pygmies, as well as for population substructure, we found strongly significant positive correlations between Pygmy individuals’ stature and their levels of admixture with the Non-Pygmy gene-pool estimated using the clustering software STRUCTURE. This result suggests that the major difference in average stature observed between Central African Pygmy and Non- Pygmy populations is likely determined by complex genetic factors.

In this context, Genome Wide Association studies and Admixture Mapping methods will likely reveal the genetic loci involved in the determination of the differences of average height found in existing African Pygmy and Non-Pygmy populations. This will further help us to better understand the determination and evolution of height variation among human populations.

We observed extensive and significant genetic and phenotypic differentiation (Figure 1, Figure 2, Figure S1) and varying levels of admixture among the Pygmy and Bantu populations. Average levels of Bantu ancestry, as determined by STRUCTURE (K = 2), in the three Western Pygmy populations were 27% (Bakola), 35% (Baka), and 49% (Bedzan) with individual values ranging from 16–73%. Average levels of Pygmy ancestry in the three Bantu populations were <1% (Lemande), 2% (Tikar), and 7% (Ngumba), with individual values ranging from 0–39%. We also observed a highly significant correlation between ancestry and height (p = 5.047×10−18) after correcting for the effect of sex (full model r2 = 0.7411, r2 for sex = 0.4247; r2 for ancestry = 0.3164). In addition, the effect of ancestry remains significant in a model that also includes Pygmy-Bantu ethnicity as a covariate (p = 3.8×10−5). These results are consistent with Becker et al. [21] and indicate a strong genetic influence on height. Similar findings were also observed using Pygmy samples only (pancestry = 0.000216; full model r2 = 0.5066; r2 sex = 0.3744; r2 ancestry = 0.1322) and the independent set of genome-wide microsatellite markers described in Tishkoff et al. [9] (data not shown).

We used the results from ADMIXTURE to estimate individual ancestry proportion ( K = 5 for esti- mating pygmy ancestry, and K = 8 for Asian ancestry) and its correlation with adult height for 43 men and 27 women from the different pygmy groups of the Philippines (Aeta, Agta, and Batak) and for the nonpygmy groups (Tagbanua, Zambales, Casiguran). Because K = 5 separates negritos and Asians, we used individual “negrito” ancestry proportion to correlate with their adult height. This procedure allows us to estimate the effect of genetic contribution on adult height.

As expected, mean stature estimates for the Batwa (66 males, 152.9 cm; 103 females, 145.7 cm) were lower than those for the Bakiga (20 males, 165.4 cm; 41 females, 155.1 cm; Fig. 2 B ). Batwa stature is significantly positively correlated with the proportion of Bakiga admixture: for males, females, and for all samples combined after regressing out the sex effect (Fig. 2 C – E ), confirming a genetic basis for the African pygmy phenotype (6, 12).

[…]

We can draw four primary conclusions from our analyses. ( i ) The African pygmy phenotype has a genetic basis, rather than a solely environmental one, based on the positive correlation between stature and Bakiga admixture for Batwa individuals raised in Batwa communities (Fig. 2 C – E ). These results confirm those obtained from other African rainforest hunter-gatherer populations by Becker et al. (12) and Jarvis et al. (6) and are consistent with individual case observations from Cavalli-Sforza (4).

Although environmental variation is an important factor influencing adult height, such influences are considered insufficient to account fully for observed population differences. Some African populations are considerably taller than others, for example, despite experiencing poorer nutrition and elevated levels of pathogen exposure (Deaton, 2007), suggesting that such differences may have a genetic basis. To date, very few studies have addressed this issue. Notable exceptions are studies investigating the difference in height observed between the Baka pygmies of Cameroon and taller neighbouring non-Pygmy populations (Becker et al., 2011; Jarvis et al., 2012). Both of these studies showed that Pygmy individuals who were genetically more similar to non-Pygmy individuals (i.e. higher levels of genetic admixture) were taller. Most recently, Perry et al. (2014) have shown that the pygmy phenotype likely arose several times independently due to positive natural selection for short stature. Additional evidence for genetic factors underlying population differences in height come from a Korean population (Cho et al., 2009).[…]

Substantial levels of non-Pygmy genetic admixture have been observed across Central African Pygmy populations [ 24 – 2 7 , 29 – 31 ] , correlating positively with adult standing height [ 32 – 34 ] . The general genetic difference be tween Pygmies and non-Pygmies together with the correlation of genetic admixture and standing height suggests that adult body size differences among Central African Pygmies and neighboring non-Pygmies are attributable in large part to genetic factors, arguing against a view that diminutive Central African pygmy body size is the consequence solely of phenotypic plasticity in a challenging nutritional and parasitic environment [ 8 ].

[…]

Our findings accord with prior observations [ 59 , 63 ] that while Pygmy body size is generally proportionally reduced relative to non-Pygmies, their leg lengths are significantly shorter relative to their trunk length. Importantly, our results provide further support for an appreciable genetic component to the determination of body size differences between Pygmies and non-Pygmies, as implied by the correlations observed between the different measures and inferred levels of non-Pygmy admixture that replicate those reported previously for adult standing height [ 32 – 34 ] .

Note that the authors changed their wordings a bit in the published version. Maybe they too encountered some funny reviewers!

Amerindian-descent physical appearance

A number of studies look at admixture in Amerindian populations, relating both macro-race/continental ancestry to phenotypes as well as sub-Amerindian clusters.

Furthermore, studies of regional human genome diversity, and its bearing on phenotypic variation, have so far been strongly biased towards European-derived populations17. The study of populations with non-European ancestry is essential if we are to obtain a more complete picture of human diversity. Latin America represents an advantageous setting in which to examine regional genetic variation and its bearing on human phenotypic diversity18, considering that the extensive admixture resulted in a marked genetic and phenotypic heterogeneity2,3,19. Relative to disease phenotypes, the genetics of physical appearance can be viewed as a model setting with distinct advantages for analyzing patterns of genetic and phenotypic variation. Many physical features are relatively simple to evaluate, show substantial geographic diversity and are highly heritable. We have previously shown that variation at a range of physical features correlates with continental ancestry in Latin Americans19 and have identified genetic variants with specific effects for a number of features20,21,22.

[…]

We infer the timings of these genetic contributions and relate them to historically-attested migrations, for example providing compelling new evidence of widespread ancestry from undocumented migrants during the colonial era. We further show how differences in Native and European sub-continental ancestry components are associated with variation in physical appearance traits in Latin Americans, highlighting the impact of regional genetic variation on human phenotypic diversity.

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