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Published in final edited form as: Neurobiol Aging. 2020 Dec 10;101:298.e11–298.e15. doi: 10.1016/j.neurobiolaging.2020.10.003

Dissecting the role of Amerindian genetic ancestry and ApoE ε4 allele on Alzheimer disease in an admixed Peruvian population

V Marca 1, F Rajabli 2, M Cornejo-Olivas 1,3, PG Whitehead 2, N Hofmann 2, M Illanes-Manrique 1, D Veliz-Otani 1,4,5, K Milla-Neyra 1, S Castro-Suarez 6,7, M Meza 6,8, LD Adams 2, PR Mena 2, R Isasi 2,10, ML Cuccaro 2,10, JM Vance 2,10, GW Beecham 2,10, N Custodio 9, R Montesinos 9, P Mazzetti 1,8, MA Pericak-Vance 2,10,*
PMCID: PMC8122013  NIHMSID: NIHMS1653715  PMID: 33541779

Abstract

Alzheimer disease (AD) is the leading cause of dementia in the elderly and occurs in all ethnic and racial groups. The apolipoprotein E (ApoE) ε4 is the most significant genetic risk factor for late-onset AD and shows the strongest effect among East Asian populations followed by non-Hispanic White (NHW) populations and has a relatively lower effect in African descent populations. Admixture analysis in the African American and Puerto Rican populations showed that the variation in ε4 risk is correlated with the genetic ancestral background local to the ApoE gene. Native American populations are substantially underrepresented in AD genetic studies. The Peruvian population with up to ~80 of Amerindian (AI) ancestry provides a unique opportunity to assess the role of AI ancestry in AD. In this study we assess the effect of the ApoE ε4 allele on AD in the Peruvian population.

A total of 79 AD cases and 128 unrelated cognitive healthy controls from Peruvian population were included in the study. Genome-wide genotyping was performed using the Illumina Global screening array v2.0. Global ancestry and local ancestry analyses were assessed. The effect of the ApoE ε4 allele on AD was tested using a logistic regression model by adjusting for age, gender, and population substructure (first three principal components). Results showed that the genetic ancestry surrounding the ApoE gene is predominantly AI (60.6%) and ε4 allele is significantly associated with increased risk of AD in the Peruvian population (OR = 5.02, CI: 2.3–12.5, p-value = 2e–4).

Our results showed that the risk for AD from ApoE ε4 in Peruvians is higher than we have observed in NHW populations. Given the high admixture of AI ancestry in the Peruvian population, it suggests that the AI genetic ancestry local to the ApoE gene is contributing to a strong risk for AD in ε4 carriers. Our data also support the findings of an interaction between the genetic risk allele ApoE ε4 and the ancestral backgrounds located around the genomic region of ApoE gene.

Keywords: Alzheimer Disease (AD), apolipoprotein E (ApoE), Amerindian (AI) genetic ancestry

1. Introduction

Alzheimer disease (AD) is a neurodegenerative disease accounting for over 70% of dementia cases in individuals ≥ 70 years of age (Alzheimer’s Association 2011). AD has a multifactorial etiology, with both genetic and non-genetic risk factors, with liability-scale heritability estimates based on twin studies ranging between 0.58 and 0.79, and with over 25 genetic risk factors contributing to AD risk (Gatz et al., 2006; Kunkle et al., 2019).

The apolipoprotein E (ApoE) gene (19q13.32) is the strongest known genetic risk factor for AD explaining up to 6% of the liability-scale phenotypic variance (Corder et al., 1993; Corder et al., 1994). ApoE codes for a protein that transports cholesterol through the interaction with cell surface receptors (Holtzman et al., 2012). There are three ApoE alleles, ε2, ε3, and ε4, defined by two polymorphisms rs429358 and rs7412, that code for three protein isoforms ApoE2 (Cys130, Cys176), ApoE3 (Cys130, Arg176) and ApoE4 (Arg130, Arg176) (Aleshkov et al., 1997).

The association of ApoE with AD risk differs across the ethnic/racial groups. The strongest association of ApoE and AD risk has been observed in East Asian (EA) populations (ε3/ε4 odds ratio OR: 3.1–5.6; ε4/ε4 OR: 11.8–33.1) followed by non-Hispanic White (NHW) populations (ε3/ε4 OR: 3.2; ε4/ε4 OR: 14.9) (Farrer et al., 1997; Liu et al., 2014). Its effect is weaker in African-descent and Hispanic populations (ε3/ε4 OR:1.1–2.2; ε4/ε4 OR: 2.2–5.7) (Tang et al., 1998; Tang et al., 2001; Tang et al., 1996; Sahota et al., 1997; Hendrie et al., 2014). Genetic studies examining the interaction of genetic ancestry and risk effect of the ApoE in Caribbean Hispanic populations (Puerto Rican and Dominican Republic) showed that the effect of the ε4 is correlated with the ancestral background around ApoE with the attenuated effect on African-originated haplotypes (Rajabli et al., 2018; Blue et al., 2019). However, the relationship between ApoE and AD risk in Amerindian (AI) descent populations is not well established. Thus, the inclusion of populations with high AI genetic ancestry is critical to understand the effects of ApoE and AI ancestry on AD risk.

Genetically the Peruvian population has approximately 80% AI ancestral background, higher than other Latin American populations, such as Mexico (50%), Chile (40%), Colombia (28%), Argentina (28%) and Puerto Rico (16%) (Norris et al., 2019; Homburger et al., 2015; Norris et al., 2018). Native Americans in Peru show ancestry from three ancestral groups, likely that originated by the split of an ancient group that migrated from EA, across the Bering Strait, and down the Americas (Harris et al., 2018). Through admixture with non-Native inhabitants that arrived after Peru’s Spanish colonization, these AI groups gave rise to the current Peruvian mestizo population, resulting in an admixed population with European (EU), EA, and African (AF) components (Homburger et al., 2015; Norris et al., 2018; Harris et al., 2018). Studies in a sample of mestizo Peruvian population suggest high allele frequency of ApoE ε3 allele (93.9%), with low ε4 (5 %) and ε2 (1.1 %) allele frequencies (Marca et al., 2011). No previous published studies have examined the association of ApoE and AD in the Peruvian population.

Our goal is to use data from the Peruvian population to assess the role of AI genetic ancestry and the ApoE gene on AD. Peruvians with the high AI genetic ancestry provides a unique opportunity to study the correlation of AI genetic ancestry with the effect of the ε4 allele over the risk of AD.

2. Methods

2.1. Study samples and ascertainment

Unrelated cases and controls were ascertained from the Instituto Nacional de Ciencias Neurologicas in Lima, Peru as part of a larger genetics study of AD. All cases were assessed by trained neurologists following NINCDS-ADRDA criteria for possible and probable AD (McKhann et al., 1984). Cognitively intact controls were screened using the Clock drawing test, and the Pfeffer functional activities questionnaire (Manos et al., 1994; Pfeffer et al., 1982). Controls were defined as individuals with no evidence of cognitive problems and age of exam (AOE) equal or greater than 65 years of age. The dataset contained 79 AD cases (67.0% female, mean age at onset (AAO) = 72.3 years [SD=8.4]) and 128 cognitively healthy controls (59.1 % female, mean AOE = 75.0 years [SD =6.6]). This study was approved by the Ethical Research Committee of Instituto Nacional de Ciencias Neurologicas of Lima and the IRB of the University of Miami, Miller School of Medicine.

2.2. Genotyping and quality control procedures

Genome-wide genotyping have been performed using Illumina Global Screening Array v2.0. Quality control analyses were performed using software PLINK v.2 (Purcell et al., 2007). Variants with the call score less than 95%, minor allele frequency less than 0.01, or not in Hardy-Weinberg equilibrium (HWE) (p<1.e-6) were eliminated. The concordance between reported sex and genotype-inferred sex was checked using X-chromosome data. The relatedness among the individuals were assessed using “identical by descent” allele sharing. ApoE genotyping was performed as in Saunders et al. (Saunders et al., 1996)

2.3. Assessment of Genetic Ancestry

Global ancestry was evaluated using GENESIS software program that is robust to known and cryptic relatedness (Conomos et al., 2019). Firstly, the KING-Robust kinship coefficient estimator was used to calculate the KING matrix that includes pairwise relatedness and measures of pairwise ancestry divergence (Manichaikul et al., 2010). PC-AiR method was then applied to calculate “preliminary” principal components (PC) by using KING matrix. Default kinship and divergence threshold values have been used. The PC-Relate method that uses “preliminary” PCs to account for the samples ancestry variation and calculate the genetic relationship matrix (GRM) that is robust for the population structure, admixture, and departure from HWE was applied to the data. PC-AiR method was once more applied to the data by using the robust kinship estimates (GRM) and calculated PCs that accurately capture population structure. PCs were calculated with and without population reference datasets. Four reference populations were used including AI, EU, AF, and EA from Human Genome Diversity Project (HGDP) data for the reference populations (Cavalli-Sforza, 2007).

To estimate the admixture proportion, a model-based clustering algorithm was performed as implemented in the ADMIXTURE software (Alexander et al., 2009). Supervised ADMIXTURE analysis was used at K = 4 by including the same four reference populations from HGDP reference panel we used in PC-AiR approach.

To assess the local ancestry, the HGDP reference panel was combined with the Peruvian data using the PLINK v2 software including approximately the same number of individuals from three reference populations EU, AF and AI. Then, all individuals in combined dataset were phased using the SHAPEIT tool ver. 2 with default settings and 1000 Genomes Phase 3 reference panel (Delaneau et al., 2014; The 1000 Genomes Project Consortium, 2015). Finally, RFMix was performed using the discriminative modeling approach, to infer the local ancestry at each loci across the genome. We ran RFMix with the PopPhased option and a minimum node size of 5 (Maples et al., 2013).

The heterogenous risk effect of the ApoE gene across the populations is suggested to be correlated with the ancestral background local to the ApoE gene (Rajabli et al., 2018). Thus, to examine the ancestral background in our dataset we calculated the average ancestry proportions at the ApoE by taking the average of the local ancestry estimates around the ApoE gene (from 44 Mb to 46 Mb on chromosome 19) (Rajabli et al., 2018). In total we had 311 markers at the ApoE gene region. The pipeline to calculate the global and local ancestries was developed by our group using R and Python scripts.

2.4. Statistical Analysis

To assess the effect of the ApoE ε4 allele in our Peruvian cohort we performed logistic regression approach. In this model, the association was tested between the affection status and gene dose of the ApoE ε4 allele by adjusting for age, gender, and populations substructure (Equation 1). Statistical analysis was performed using the “GLM2” package available in R computing environment (Marschner, 2014).

AD~Age+sex+ApoEε4Dosage+PC1+PC2+PC3 (Equation 1)

3. Results

The supervised ADMIXTURE analysis showed that Peruvians are a four-way admixed population with the 63.6% AI, 29.7% EU, 3.8% AF and 2.9% EA ancestral background. This confirms recent studies showing similar distribution of admixture in Peruvians (Harris et al., 2018). Figure 1A shows the boxplot of the average ancestry across all individuals in the dataset. The ancestral proportion of each individual is illustrated in the bar-plot Figure 1B, where each column reflects the admixture structure of a single individual as the proportion of different colors.

Figure 1.

Figure 1.

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The boxplot of the four parental ancestries in Peruvian dataset (A). Bar-plot of four-way admixed Peruvian individuals estimated using ADMIXTURE software at K = 4 (B)

A few studies suggested protective effect of AI ancestral background (). However, we did not observe any statistical difference in proportion of AI ancestry between cases (62.6% (SD = 25.9)) and controls (63.7% (SD = 25.3)).

The allele frequency distribution of the ApoE alleles are illustrated in Table 1. The affected individuals have higher frequency of ApoE ε4 allele (9.2%) than individuals that are cognitively normal (4.6%). Logistic regression results showed that the ApoE ε4 allele is significantly associated with AD in this Peruvian cohort with the high-risk effect (OR = 5.02 (ApoE ε4 Dosage), CI: 2.3–12.5, p-value = 2e–4). The average of the local ancestries around the ApoE gene showed that the distribution of the parental ancestries local to the ApoE gene is the similar to the average ancestry across the genome with the highest proportion of AI (60.6%), followed by EU (33.9%) and AF (5.5%) ancestral backgrounds.

Table 1.

ApoE genotype and allele frequencies in cases and controls

Cases(%) Controls (%)
Genotypes
ε2ε3 3(1.4) 8(3.9)
ε3ε3 43(20.8) 102(49.3)
ε3ε4 28(13.5) 17(8.2)
ε4ε4 5(2.4) 1(0.5)
Alleles
ε2 3(0.7) 8(1.9)
ε3 117(28.3) 229(55.3)
ε4 38(9.2) 19(4.6)
Total 79 128
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4. Discussion:

The ApoE ε4 allele is the most significant genetic risk factor for late-onset AD with the differences in effect size among the populations. Our results showed that the risk for AD from ApoE ε4 allele in Peruvians is higher than we have observed in NHW populations. Given the high admixture of AI in the Peruvian population, it suggests that the AI local ancestry is contributing to a strong risk for AD in ApoE ε4 carriers. This would align with the current believed migration pattern of AI from EA, where ApoE ε4 carriers have the highest ApoE ε4 risk for AD (Farrer et al., 1997; Liu et al., 2014).

The prevalence of AD, and its genetic architecture, varies among the diverse populations. Moreover, AD genetic studies in different ethnic groups have shown variation in both risk effect size and variants (e.g. ApoE, ABCA7, SORL1, etc.) (Farrer et al., 1997; Reitz et al., 2013; Cukier et al, 2016; Liu et al., 2009). Studies have reported an interaction of social factors and the ApoE ε4 allele to affect the progression of dementia (Hasselgren et al., 2019; Hasselgren et al., 2018; Seeman et al., 2005). Indeed, the FINGER study in Finland has shown that changes in diet and exercise can affect the progression of the disease (Ngandu et al., 2015). This information led to the world-wide FINGER initiative by the Alzheimer’s Association (Kivipelto et al., 2020). As AD is a late-onset disorder, delaying the eventual onset of AD from genetic factors such as ApoE ε4 will lower the incidence of AD, as individuals succumb to other factors leading to death before the onset of dementia. As ApoE is well known for its effects on the cardiovascular system (Stakos et al., 202), cultural differences between populations are likely to affect the onset and progression of AD in carriers of ApoE ε4. The studies of genetic ancestry effects within an admixed population such as Peru, where the social, dietary and economic effects are similar for the majority of the population, have led to the identification of the distinct ancestral genetic architecture surrounding ApoE ε4 as the driving factor for the overall risk of AD within these populations, (Rajabli et al., 2018; Blue et al., 2019). Thus, studying diverse populations is critical to the understanding of the molecular mechanism underlying the disease pathogenesis and the success of precision medicine for all populations. However, diverse populations and especially populations with the AI ancestry are substantially underrepresented in AD genetic studies. The Peruvian population with a large proportion of AI ancestry provides a unique opportunity to assess the role of AI ancestry in AD. By confirming the correlation of the genetic ancestry with the risk effect in ε4 allele our findings show the importance of studying different populations to evaluate the ancestry-specific genetic modifiers correlated with ancestry. Ultimately, studying diverse populations is essential to understand the genetic factors initiating and influencing AD pathogenesis that may contribute to health disparities and ultimately the development of effective therapies.

Highlights.

  • Risk for Alzheimer disease from ApoE ε4 in Peruvians is higher than observed in non-Hispanic White populations.

  • Amerindian genetic ancestry local to the ApoE gene is contributing to a strong risk for Alzheimer disease in ε4 carriers.

  • Confirms the findings of an interaction between the genetic risk allele ApoE ε4 and the ancestral backgrounds located around the genomic region of ApoE gene.

5. Acknowledgement

Research reported in this publication was supported in part by the Fogarty International Center (FIC) of the National Institutes of Health and the National Institute of Neurological Disorders and Stroke (NINDS) under grant #D43TW009345 awarded to the Northern Pacific Global Health Fellows Program, grant #D43TW009137 awarded to the Interdisciplinary Cerebrovascular Diseases Training Program in South America, the AG054074 grant from the National Institutes on Aging, and the A2018556F grant from the BrightFocus Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We are grateful for Erik Figueroa-Ildefonso for his contribution on the logistics and support on lab work.

Abbreviation List

AAO

age at onset

AD

Alzheimer disease

AF

African

AI

Amerindian

AOE

age of exam

ApoE

apolipoprotein E

EA

East Asian

EU

European

GRM

genetic relationship matrix

HGDP

Human Genome Diversity Project

HWE

Hardy-Weinberg equilibrium

NHW

non-Hispanic White

PC

principal components

Footnotes

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