Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132
https://doi.org/10.1186/s12881-020-01068-0

RESEARCH

Open Access

Ancestry effects on type 2 diabetes genetic
risk inference in Hispanic/Latino
populations
Aroon T. Chande1,2,3, Lavanya Rishishwar2,3, Andrew B. Conley2,3, Augusto Valderrama-Aguirre1,3,4,5,Miguel A. Medina-Rivas3,6 and I. King Jordan1,2,3*
From The 16th Annual MidSouth Conference on Computational Biology and Bioinformatics (MCBIOS ’19)
Birmingham, AL, USA. 28-30 March 2019

Abstract
Background: Hispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to
predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However,there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry
profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from
Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to theAfro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations.
Methods: Whole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research
Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry
fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes,
and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted
on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies ofancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here.
Results: T2D genetic risk in Colombian and US HL populations is positively correlated with African and Native Americanancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher
predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the EuropeanAmerican population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts usedfor variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects acrosspopulations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit
consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences.
(Continued on next page)* Correspondence: king.jordan@biology.gatech.edu
1School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic
Drive, Atlanta, GA 30332, USA
2
IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

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Conclusions: T2D associations that replicate across populations provide for more reliable risk inference, and modeling
population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation.
Keywords: Polygenic risk score (PRS), Genetic risk, Type 2 diabetes (T2D), Genetic ancestry, Population genetics, Hispanic/
Latino (HL), Colombia, Chocó, Antioquia

Background
Diabetes mellitus is a global pandemic [1–3]. The prevalence of adult onset (type 2) diabetes has nearly doubled
over the last 30 years, and the number of cases has increased by more than 300 million. This increase has been
driven largely by modernization and the accompanying
changes in diet and lifestyle. According to the International Diabetes Federation (IDF) Atlas [4], 425 million
adults worldwide are currently living with diabetes, withhalf of them remaining undiagnosed. In the United States(US) alone, more than 100 million adults have either prediabetes or diabetes. US Hispanic/Latino (HL) populations
bear a disproportionate burden of type 2 diabetes (T2D),
with a prevalence almost twice as high as that of nonHispanic whites [5, 6]. Globally, countries from the Latin
America and Caribbean region show the highest diabetes
prevalence compared to six other regions.
T2D is a multifactorial disease with a complex set ofinteracting environmental and genetic causes contributing to its etiology. Historically, risk management forT2D has been focused squarely on environmental factors, with an emphasis on changes in diet and lifestyle.
Physicians have been taught to evaluate a suite of clinically measurable risk factors, e.g. weight and blood pressure along with blood sugar and cholesterol levels, inassessing patients’ likelihood of developing T2D. In
addition to these clinical features, family history andrace/ethnicity are also widely recognized as T2D risk
factors, underscoring genetic contributions to disease expression. Indeed, genetic factors have been estimated toaccount for 20–80% of the variance in T2D development[7–9]. It follows that an understanding of individual patients’ genetic risk should become part of the standardof care for T2D screening and prevention.
Individuals’ risk for common heritable diseases, such as
T2D, can be quantified as polygenic risk scores (PRS) [10].
The ability to calculate PRS rests on genome-wide association studies (GWAS), which characterize specific genetic
variants (alleles) that increase disease risk [11]. GWAS
typically uncover numerous variants across the genome,
each of which contributes a small fraction of the overall
disease risk. PRS can be computed by summing the number of risk increasing alleles in individuals’ genomes, andscores can be weighted by the effect sizes of the risk alleles[12]. This approach to inferring genetic risk works very
well when it is applied to patient cohorts from the same

populations where the GWAS were conducted. However,
the extent to which genetic risk can be accurately calculated across populations with divergent ancestries is amatter of contention [13, 14]. On the one hand, many
GWAS are highly replicable, with the same variants often
discovered in multiple populations [15, 16]. On the other
hand, recent studies have shown that differences in genetic ancestry can lead to mis-estimation of PRS acrosspopulations [17–19].
The challenge of accurate PRS estimation across ancestry groups is particularly pressing for HL populations.
First, there is a severe bias towards European ancestry cohorts in GWAS. As of 2006, only 0.06% of GWAS samples
were from HL cohorts, and the fraction had only risen
slightly to 0.54% by 2016 [20, 21]. Second, HL is a politically inspired, pan-ethnic label that does not correspond to
any natural (i.e. genetic) classification of human populations [22]. Individuals with origins in Latin America typically have three-way ancestry contributions from African,
European, and Native American source populations, andthey can differ dramatically with respect to the relative
proportions of each [23–27]. Even neighboring populations from within the same Latin American country canshow widely divergent ancestry profiles [28]. Accordingly,
the extent to which existing GWAS variants can be used
to accurately infer genetic risk among diverse HL populations is currently unknown.
In this study, we explored the relationship between ancestry and T2D genetic risk inference in HL populations
from Colombia and the US. We found that T2D genetic
risk is positively correlated with African and Native
American ancestry and negatively correlated with
European ancestry, consistent with epidemiological results. We also show that T2D genetic risk inference
holds up well across different GWAS ancestry cohorts
and propose an approach whereby ancestry information
can be used to support cross-population risk inference.

Methods
Diabetes epidemiological data

Data on the worldwide prevalence of diabetes mellitus
were taken from The World Bank [29]. Worldwide diabetes prevalence values are expressed as the percentageof the population between the ages of 20 and 79 diagnosed with diabetes. Prevalence values are reported for
264 countries, which were broken down into sevenChande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

World Health Organization (WHO) regions and four
WHO income groups. Data on the prevalence of diabetes for the United States (US) were taken from theAmerican Diabetes Association [30]. US diabetes prevalence values are expressed as the age-adjusted percentageof the population diagnosed with diabetes. Prevalencevalues are broken down by the US census self-identified
race/ethnicity groups and further sub-divided into country/region of origin for individuals who self-identify as
Hispanic/Latino (HL). Diabetes prevalence values for theEuropean-American (EA) and Mexican-American (MA)populations were taken from the Utah Department of
Public Health [31] and the County of Los Angeles Public
Health agency [32]. Note that these US diabetes prevalence values correspond to the specific populations sampled as part of the 1000 Genomes Project and used forgenetic risk inference (see Methods section on Type 2 diabetes (T2D) genetic risk inference).
Genome wide association study (GWAS) data

GWAS data were taken from the NHGRI-EBI GWASCatalog [11]. All reported GWAS (as of 3/31/2018) werecharacterized with respect to the trait under consideration and the ancestry of the study cohort. GWAS cohorts were characterized as African, East Asian,
European, Hispanic/Latino, or Native American following the GWAS Catalog framework for representation ofancestry data in genomic studies [33]. The total numberof single-nucleotide polymorphism (SNP) associations
that reach the GWAS Catalog significance threshold(P < 1 × 10− 5) were recorded for each GWAS trait. For
each T2D SNP association, we recorded the study (ies)where it was reported, the cohort ancestry, the SNP
identifier, its chromosomal location, and the identity ofthe trait-increasing effect allele. T2D GWAS summary
statistics for a trans-ethnic meta-analysis, which integrated cohorts with four distinct ancestries, were takenfrom the DIAGRAM consortium [34, 35]. For these data,
the GWAS SNP effect alleles, ancestry-specific directionsof effect, effect sizes, and P-values were recorded.
Type 2 diabetes (T2D) genetic risk inference

Whole genome sequences from the 1000 Genomes Project [36] and imputed whole genome genotypes from the
ChocoGen Research Project https://www.chocogen.com[37] were used for T2D polygenic risk score (PRS) calculation (Table 1). For the 1000 Genomes Project data, SNP
data were taken from the phase 3 data release for one Colombian population – Colombians from Medellín,
Colombia – and two US populations: Utah Residents(CEPH) with Northern and Western European Ancestry
and Mexican Ancestry from Los Angeles USA. The 1000
Genomes Project human genome sequence data are deidentified and made publicly available for research use

Page 3 of 14

without restriction. For the ChocoGen Research Project,whole genome genotypes for sample donors were characterized using the Illumina HumanOmniExpress-24 SNP
array as previously described, yielding ~ 500,000 SNPs per
individual [28, 37]. The genotypes were imputed using the
program IMPUTE2 [38] with the 1000 Genomes Project
phase 3 haplotype reference panel [39] as previously described [40], yielding ~ 35 million additional SNPs acrossall samples. The ChocoGen project was conducted withthe approval of the Ethics Committee of the Univerisidad
Tecnológica del Chocó (ACTA No 01-v1), and all sample
donors signed informed consent documents.
For each individual genome, an unweighted T2D PRS
was computed by calculating the normalized sum of thenumber of T2D SNP effect alleles found in the genome[12]. It should be noted that T2D PRS were notweighted by SNP effect sizes owing to the fact that the
T2D SNP associations used here were curated from multiple studies whose effect sizes cannot be accurately
combined [16]. T2D PRS were calculated as:
Xn
Xn
PRS ¼
G
=
A
i
i¼1
i¼1 i
where Gi ∈ {0, 1, 2} corresponds to homozygous absent,
heterozygous, and homozygous present effect alleles foreach T2D SNP i and Ai ∈ {0, 1, 2} corresponding the total
number of alleles with variant calls at each SNP i. T2D
PRS were compared to individuals’ continental genetic ancestry fractions – African, European, and Native American– which were taken from our previous studies [28, 40].
T2D PRS were computed for the Colombian and US
populations using an unpruned set of 165 T2Dassociated SNPs along with a reduced linkage disequilibrium (LD) pruned set of 42 SNPs (Additional file 1:
Table S1). LD pruning was performed on the four Colombian and US populations analyzed here using the
program PLINK [41] with 2000 SNP window size and athreshold of r2 > 0.1, where r2 corresponds to the levelof linkage disequilibrium between pairs of SNPs in thewindow. An additional round of LD clumping was performed on the DIAGRAM GWAS summary statistic
data using the LDpred program, with the same suggested window size of 2000 SNPs [42]. LDpred uses the
LDscore method to choose the highest effect size SNP
for each LD window and subsequently reweights the effect sizes for all retained SNPs.
Genetic ancestry and T2D risk

The program ADMIXTURE was used to compute thethree way continental ancestry percentages – African,
European, and Native American – for all individuals
from the Colombian and US populations analyzed here[43]. The modern Colombian and US populations werecompared to the proxy ancestral reference populations

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

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Table 1 Populations analyzed in this study
Data Sourcea

Population Description

Population Namenb

ChocoGen

Chocoano in Quibdó, Colombia

Chocó

94

1KGP

Colombian in Medellin, Colombia

Antioquia

94

1KGP

Yoruba in Ibadan, Nigeria

African

108

1KGP

Iberian populations in Spain

European

107

1KGP

Utah residents with NW European ancestry

European-American (EA)

99

1KGP

Mexican Ancestry from Los Angeles USA

Mexican-American (MA)

64

1KGP

Peruvian in Lima, Peru

Native American

85a

1KGP = 1000 Genomes Projectbn = number of sample donors per population

shown in Table 1, with ADMIXTURE run for K = 3 ancestral components, corresponding to each of the threecontinental population groups that admixed to form
modern American populations. This process yields avector of three ancestry fractions for any individual
admixed genome sampled from the modern populations:
fAfrican, fEuropean, fNativeAmerican (Additional file 2: FigureS1). Then, for each of the three continental ancestry
components, individuals’ continental ancestry fractions
were regressed against their T2D PRS using unweighted
ordinary least squares regression (OLS) with the lm
function in R:
PRS i ¼ α þ βxi þ εi
where PRSi is the predicted polygenic risk score for individual i; α and β are constants describing the intercept
and slope, respectively; xi is the ancestry fraction for individual i; and εi is an error term describing the deviation from the fitted line. The resulting OLS produces:
β0, the model β or slope; the standard error of themodel; the r2 value describing the model’s fit; the modelt-statistic; and a two-tailed P-value. For visualization
purposes, a best fit line with confidence intervals wascomputed using local polynomial regression (loess).

Results
Diabetes prevalence and population disparities

Diabetes is characterized by an extremely high disease
burden along with pronounced disparities in prevalence
among countries, regions, and income groups worldwide(Fig. 1a and b). It should be noted that, while theseprevalence data are not broken down into diabetes types,
the vast majority of diabetes cases correspond to adultonset, non-insulin dependent, type 2 diabetes (T2D).
The US is no exception to this trend; there is a highoverall diabetes prevalence in the country and marked
disparities among racial and ethnic groups (Fig. 1c). Native Americans, African Americans, and Hispanic/Latino(HL) populations bear a disproportionately high share of
the diabetes disease burden in the US compared to

Asian Americans and European Americans. Interestingly, there are also notable disparities within ethnic
groups. HL populations with distinct origins in Latin
America can have very different diabetes prevalence(Fig. 1d). Individuals from South America show diabetes
prevalence close to what is seen for Asian Americans,
whereas Mexican Americans show a two-times greater
prevalence, close to what is seen for Native Americans.
Among HL regional groups, diabetes prevalence can also differ between males and females in a group-specific manner.
The observed diabetes prevalence disparities among
HL groups with distinct origins begs an explanation.
Diabetes is a complex common disease with multifactorial causes, including genetic and environmental effects
along with interactions between them. Nevertheless,T2D in particular is strongly genetically influenced withestimates of heritability ranging from 20 to 80% [7–9].
Furthermore, genetic ancestry is known to impact theburden T2D; both African and Native American ancestry have been associated with increased T2D prevalence[44–48]. Thus, one may naively expect to observe more
uniformity in T2D prevalence within a single ethnic
group. But the pan-ethnic HL label does not in fact correspond to a ‘natural’ group with a shared genetic ancestry. Rather, HL groups encompass an extraordinarily
diverse set of populations, which are characterized by distinct combinations of ancestry from Africa, Europe, and
the Americas [23–27]. Additionally, the Native Americancomponent of HL ancestry varies substantially according
to the regional origins of the populations [28, 49, 50].
With this in mind, we have been investigating the contributions of ancestry to genetic risk and T2D health disparities in diverse HL populations.
GWAS ancestry bias and T2D risk inference

The power to infer genetic risk for complex common
diseases, such as T2D, has exploded in recent years
owing to the accumulation of GWAS for a wide varietyof health-related traits [10, 11]. GWAS yield lists of trait
SNP associations, including the identity of traitincreasing effect alleles, each of which slightly increases

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

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Fig. 1 Diabetes global prevalence and population disparities. a Diabetes prevalence distributions shown for (a) the seven world healthorganization (WHO) geographic regions and (b) the four WHO income groups. (c) Diabetes prevalence for United States (US) census race/
ethnicity groups. d Hispanic/Latino (HL) diabetes prevalence in the US broken down by country (region) of origin and shown separately for males(black) and females (white)

the risk of disease. Accordingly, an individual’s genetic
risk for a given trait can be estimated as a polygenic risk
score (PRS), which is calculated as the normalized sumof risk (effect) alleles encoded in their genome. However,
the overwhelming bias towards European cohorts in

GWAS [20, 21] presents a major challenge to this paradigm. Specifically, the extent to which PRS can be accurately inferred across population groups with distinct
ancestry profiles is a matter of great concern [13, 14].
On the one hand, many robust SNP associations areChande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

known to replicate across populations [15, 16]. On theother hand, GWAS SNP ascertainment biases and demographic process have been shown to yield systematic errors in PRS calculation across populations [17–19].
Here, we aimed to explore the effects of ancestry on
the calculation of PRS for T2D across diverse populations. In support of this effort, we found that T2D is distinct compared to GWAS for most other traits in several
respects, largely owing to the intensity of focus on thegenetic architecture of the disease and its epidemiological importance for populations across the world.
T2D has the most independent studies of any trait in the
NHGRI-EBI GWAS catalog (Fig. 2a), and it has among
the most SNP associations reported for any trait (Fig. 2b).
Perhaps even more importantly, for our purposes, T2D
cohorts show substantially more ancestry diversity thantypical GWAS traits (Fig. 2c). A slight majority of T2D
GWAS cohorts have European ancestry, but there are substantial number of cohorts with East Asian, African, and
HL ancestry. A number of T2D GWAS have employed atrans-ethnic study design, whereby cohorts with distinct

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ancestries are combined in an effort to increase the reliability of discovered SNP associations [34, 35]. Taken together,
the large number of T2D studies with diverse ancestry cohorts and the large number of T2D associations provide
resolution for our efforts to (i) calculate PRS across diverse
populations and (ii) assess the impact of ancestry on predicted T2D genetic risk.
Ancestry and T2D genetic risk inference: Colombia

We first explored the relationship between ancestry andT2D genetic risk for the Colombian populations of Antioquia and Chocó. Despite the fact that these two administrative departments (states) share a common border, their
populations were historically isolated and show very distinct ancestry profiles. The population of Antioquia has
majority European ancestry (75%) followed by Native
American (18%) and African (7%) fractions, whereas theancestry of Chocó is primarily African (76%) with smaller
European (13%) and Native American (11%) components[28]. Genome sequences were characterized for individuals from the two populations and T2D PRS were

Fig. 2 Genome wide association studies (GWAS) on type 2 diabetes (T2D). The number of (a) GWAS and the number of (b) SNP-associations per
GWAS trait are shown, with T2D values in red. c The fractions of continental ancestry groups represented in GWAS cohorts are shown for all
GWAS and for T2D GWAS alone

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

computed for all individuals as described in the Methods.
The distributions of T2D PRS for the two populations
were then compared in order to assess their relative genetic risk. Consistent with previous results [40], we foundthat Chocó has significantly higher predicted genetic risk
for T2D compared to Antioquia (Fig. 3a), and the higher
genetic risk for T2D in Chocó is correlated with Africanancestry (Fig. 3b). The elevated T2D risk for Chocó can beobserved when all 165 T2D-associated SNPs are used for
PRS calculation (Fig. 3) or when a reduced set of 42 linkage
disequilibrium (LD) pruned SNPs is used (Additional file 2:
Figure S2 panels A & B). These findings are consistent with
reports from the US showing a correlation between T2D
genetic risk and African ancestry [51], and African Americans are known to have substantially higher T2D prevalencecompared to European Americans [44, 46–48]. In Colombia
however, Antioquia shows approximately three-times higher
observed T2D prevalence compared to Chocó (Fig. 3c), in
direct contrast to the predicted genetic risk for the two populations and the epidemiological data from the US.
We previously attributed the difference between therelative predicted genetic risk of T2D for the two Colombian populations and their observed T2D prevalence
to gene-by-environment interactions, whereby diet andlifestyle in Chocó serve as protective factors against T2D[40]. However, another possible explanation for this discrepancy is that there is a systematic bias in T2D PRS
calculations across populations of this kind with distinct
ancestry profiles [17–19]. We addressed this possibility
by comparing the observed T2D relative risk for Chocó /Antioquia to a null distribution of relative risk generated
by permuting 500,000 random sets of GWAS SNPs (riskalleles) of the same size as the T2D SNP set. If there
were a systematic bias in the population-specific frequencies of GWAS risk alleles for the two populations,then the null distribution would be expected to show an
overall increase of genetic risk in Chocó. We do not observe any such bias; the observed relative risk of T2D issignificantly greater than the null expectation (Fig. 3d).
As previously described, the major source of bias forcross-population PRS calculation is attributed to the vast
over-representation of European cohort GWAS. It is
possible that GWAS SNPs discovered in European study
cohorts will not accurately capture genetic risk in nonEuropean cohorts. This problem could be even moreexacerbated in the case of the admixed Colombian populations studied here, one of which looks more
European while the other is more African. The fact thatT2D has been the subject of numerous GWAS across diverse population cohorts (Fig. 2) provides an opportunity
to interrogate this potential bias. To do so, we characterized T2D GWAS variants according to the ancestry of
the study cohorts where they were discovered and thenre-calculated population-specific T2D PRS distributions

Page 7 of 14

for each ancestry separately. We were able to classify
T2D SNPs into five different ancestry profiles, three of
which showed significantly higher risk in Chocó and twoof which yielded no significant difference (Fig. 4). Noneof the comparisons showed significantly higher T2D risk
in Antioquia, and all of the cohorts with ancestry mostsimilar to the Colombian populations (African, Multiethnic, and Admixed American) showed higher relativerisk in Chocó. These results support the finding of
higher genetic risk for T2D in Chocó, associated with
African ancestry, and do not suggest that this finding
can be attributed to GWAS SNP discovery bias.
Ancestry and T2D risk inference: United States (US)We performed a similar comparison of T2D genetic risk
for European-American (EA) and Mexican-American(MA) populations in the US. With the same set of T2D
SNPs used to compare genetic risk in Colombia, the MA
population shows marginally higher T2D genetic risk
than the EA population (Fig. 5a). As was the case forColombia, the same differences in T2D genetic risk between the US populations can be seen when all 165
T2D-associated SNPs are used for the PRS calculations(Fig. 5a) or when a reduced set of 42 linkage disequilibrium (LD) pruned SNPs is used (Figure S2 panels C &D). For these two US populations, T2D genetic risk isnegatively correlated with European ancestry and positively correlated with Native American ancestry (Fig. 5b).
However, unlike what we observed in Colombia, therelative genetic risk estimates between the two populations are consistent with the observed T2D prevalence;
the MA population shows approximately two-times
higher T2D prevalence than the EA population (Fig. 5c).
Despite the consistency of the T2D genetic risk estimates and the observed prevalence values for these two
populations, we wanted to further explore the contribution of genetic ancestry differences to potential biases ingenetic risk calculation. To do so, we took advantage ofa recent trans-ethnic GWAS meta-analysis [34, 35] tocurate T2D SNPs that were discovered in one or morecohorts with distinct ancestries, including European and
Mexican ancestry cohorts. We then computed T2D PRS
distributions using (i) significant SNPs that showed thesame direction of effect between the two ancestry cohorts, (ii) SNPs that were significant in the European ancestry cohort only, (iii) SNPs that were significant in the
Mexican ancestry cohort only, and (iv) SNPs thatshowed different directions of ancestry-specific effects(Fig. 6). The SNPs with effects that are shared between
populations or effects that are population-specific all
yielded higher T2D PRS in the MA compared to the EA
population. The magnitude and significance of this relationship were most pronounced for the ancestry shared
SNPs (Fig. 6a). The SNPs with different effects between

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

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Fig. 3 T2D genetic risk and observed prevalence in Colombia. a T2D polygenic risk score distributions are shown for Antioquia (green) and
Chocó (purple). b T2D polygenic risk scores for individuals from Antioquia and Chocó regressed against their percent African ancestry. c
Observed T2D diabetes prevalence for Antioquia (green), Chocó (purple), and Colombia overall (gray). d Observed T2D relative genetic risk
Chocó/Antioquia compared to the null distribution of relative genetic risk between the two populations

the two ancestry cohorts were the only ones that showed
higher T2D PRS in the EA population (Fig. 6d). These
results underscore the potential utility of combining cohorts with distinct ancestries for GWAS SNP discovery,in terms of both increasing the reliability of SNP effectallele discovery and decreasing the likelihood of false
discoveries. Indeed, we found that the T2D SNPs thatshowed shared effects across ancestry cohorts had effectsize odds-ratio (OR) values almost an order of magnitude higher than SNPs with divergent ancestry-specificeffects (Shared OR = 2.40 versus Divergent OR = 0.28).
Correcting for ancestry bias in T2D risk inference

A number of recent studies have underscored (i) the extreme bias of European ancestry cohorts in GWAS [20, 21]
and (ii) the corollary potential to mis-estimate genetic risk

across populations with diverse ancestries [13, 14, 17–19].
Kim et al. identified two potential sources of bias for crosspopulation ancestry risk inference [18], which we will callhere SNP ascertainment bias and SNP discovery bias. SNP
ascertainment bias is related to the fact that SNP microarrays are typically used for genotyping in GWAS, and thesemicroarrays are designed, for the most part, to capture highminor allele frequency (MAF) SNPs in European populations. This will lead to the ascertainment of SNPs withhigher MAF in European populations compared to otherglobal populations, particularly populations from Africa
that are enriched for ancestral alleles [52]. Then, systematicdifferences in the proportions of derived alleles, which mostoften correspond to the minor allele, versus ancestral alleles, may lead to directional biases in the estimation ofgenetic risk. SNP discovery bias is related to the increased

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

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Fig. 4 T2D genetic risk comparison in Colombia based on different GWAS cohort continental ancestries. T2D polygenic risk score distributions for
Antioquia (green) and Chocó (purple) are shown for SNP associations discovered in patient cohorts with distinct continental ancestries

power of GWAS to detect SNPs with higher MAF. Irrespective of microarray design, discovery of SNPs in
European cohorts will yield relatively higher MAF in
European populations compared to other populations,which can also lead to mis-estimation of genetic risk acrosspopulations with distinct ancestries.
Here, we propose a potential control for these twosources of PRS bias, based on correction for systematicdifferences in the proportions of ancestral versus derived
alleles in populations with distinct ancestry profiles. Ancestral alleles tend to correspond to major alleles,
whereas derived alleles most often correspond to minor
alleles in discovery cohort populations. While GWAS
risk alleles can be more evenly distributed across ancestral (44%) versus derived (56%) alleles, differences in thefrequencies of these allele classes across populations canstill introduce bias in genetic risk inference [18]. The
idea behind the control that we propose here is to eliminate any possible bias owing to population-specific differences in the frequencies of ancestral versus derived
alleles, which are mainly attributed to demographic factors (i.e. genetic drift).
The steps in the control are shown below. Further detail regarding the execution of each individual step areprovided in Additional file 2 (see pages 5–7).
1. Collect trait SNP set and calculate populationspecific PRS values and between-population PRS
differences (ΔPRS).

2. Determine the distribution of derived allele
frequencies (DAF) for trait-associated SNPs in the
GWAS cohort source population.
3. Randomly sample SNP sets parameterized by thisDAF distribution based on the DAFs from thedistinct populations being compared (thereby
eliminating between-population DAF biases).
4. Calculate between-population ΔPRS for all randomly sampled SNP sets and determine thenull ΔPRS distribution.
5. Compare the observed ΔPRS to the null ΔPRS distribution
and compute a z-score as the ancestry-corrected ΔPRS: corr.
ΔPRS = (obsΔPRS − μnullΔPRS)/σnullΔPRS.
An example of this control can be seen for the comparison of T2D genetic risk between the EA and MA
populations (Fig. 7). The observed value of ΔPRS for
EA-MA is − 2.08, while the null ΔPRS distribution iscentered around 0 with a mean value of − 0.16 and astandard deviation of 1.25. Thus, there is a slight bias in
PRS calculation for the two populations. Accordingly,
correcting for SNP ascertainment bias does mitigate thedifference in predicted risk between the two populations,with a corrected ΔPRS value of 1.54 that is marginallysignificant at P = 0.054. Given what we know about thehigher prevalence of T2D in the MA population, we may
consider this correction to be accurate, in the sense thatit preserves the direction of the genetic risk difference,but conservative as it dampens the observed effect.

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

Page 10 of 14

Fig. 5 T2D genetic risk and observed prevalence for European-American (EA) and Mexican-American (MA) cohort populations. a T2D polygenic
risk score distributions are shown for EA (gold) and MA (green). b T2D polygenic risk scores for EA and MA individuals are regressed against theirpercent European and percent Native American ancestry. c Observed T2D diabetes prevalence values for EA (gold), MA (green), and the UnitedStates overall (gray)Discussion
HL populations are burdened with a high and increasing
prevalence of T2D, both in the US and in Latin America(Fig. 1) [5, 6]. Recent developments in the estimation ofgenetic risk using PRS provide opportunities to reduce
this burden through improved screening and prevention
efforts [10]. Nevertheless, there are a number of challenges that need to be met in order to ensure that genetic risk of T2D, and other common heritable diseases,
can be accurately predicted using PRS [13, 14]. In particular, the bias towards European ancestry cohorts in
GWAS [20, 21] has the potential to limit the utility of
PRS in HL populations. In addition, the extremely diverse ancestries that can be found among HL populations could lead to mis-estimation of genetic risk for
distinct HL subgroups.
There are two broad solutions to these ancestryrelated challenges to genetic risk inference: (i) more dataand (ii) better methods. Obviously, more GWAS that

include cohorts that capture the genetic diversity of HL
populations will go a long way towards providing the
raw material, in the form of risk increasing genetic variants relevant to those same populations, which areneeded to compute accurate PRS. However, given thecurrent pace of efforts to diversify GWAS, along withthe very high cost of these studies, it is unrealistic to expect the GWAS coverage of HL populations to approachthat of European ancestry cohorts any time soon. In themeantime, new methods that explicitly leverage ancestry,e.g. modeling differences in allele frequencies acrosspopulations, may help to increase confidence in crosspopulation PRS calculation.
Here, we have shown that considering the consistency
of GWAS variant effects across populations and modeling population-specific allele frequencies can increase
confidence in cross-population PRS. T2D is a special
case with respect to common heritable diseases in thesense that it has been extensively studied via numerous

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

Page 11 of 14

Fig. 6 T2D genetic risk comparison between European-American (EA) and Mexican-American (MA) cohort populations based on ancestry-specific
SNP effects. T2D polygenic risk score distributions for EA (gold) and MA (green) populations are compared for (a) all SNPs with consistentancestry effects, (b) SNPs with European ancestry-specific effects, (c) SNPs with Mexican ancestry-specific effects, and (d) SNPs with opposing
ancestry effects

GWAS, and it has the most diverse set of ancestry cohorts seen for any GWAS trait (Fig. 2) [11]. In addition,
recent studies have combined cohorts from different ancestries to increase confidence in the discovery of T2D
associated variants [34, 35]. These facts allowed us toevaluate the extent to which GWAS variants discovered

in cohorts with different ancestries yield similar PRS.
The signal of T2D relative risk in Colombia is highly
similar when GWAS variants discovered in different ancestry cohorts are used for PRS (Fig. 4). A similar result
was seen for T2D risk in the US, but in this case,consistency of T2D associations across cohorts seemedChande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

Page 12 of 14

Fig. 7 Correcting for ancestry bias in T2D risk inference. The null distribution of T2D relative risk between the EA-MA populations is generated byrandomly sampling risk alleles with the same frequency of ancestral/derived alleles as the GWAS source population. The initial observed T2D
relative risk is compared to the null distribution to yield a corrected score. The observed T2 relative risk is shown on the distribution (arrow) alongwith the corrected T2D relative risk value and its significance (upper left)

to provide more reliable PRS estimates (Fig. 6). Finally,
we proposed a conservative control for cross-population
PRS inference based on modeling the frequencies of ancestral and derived alleles in the different populations
being considered (Fig. 7).
A recent study compared the utility of GWAS SNPs
ascertained from EA versus HL populations for a calculating PRS in HL populations across twelve different traits[53]. While there was a wide variety of relative performance of EA SNPs across the traits, the majority of EA SNP
sets showed comparable risk prediction accuracy compared to the best performing SNP sets, which included information from HL GWAS cohorts. Nevertheless, theinclusion of non-EA GWAS association results to refine
the SNP weights improved accuracy across the board. The
results are consistent with our own findings suggestingthat information from multi-ethnic GWAS cohorts can beused to refine PRS inference.

Conclusions
One promising area for future work entails the application of machine learning methods to the inference ofpolygenic risk [54]. Currently, PRS calculations are basedon GWAS that explicitly assume an additive model ofgenetic effects on traits of interest. Accordingly, standard
methods for computing PRS, such as the kind we use
here, entail a straightforward summation of risk alleles
genome-wide. Of course, it may be more biologicallyrealistic to assume that there are non-additive genetic effects among variants discovered by GWAS and used for
PRS. If this is indeed the case, then more sophisticatedmachine learning algorithms may ultimately improve theaccuracy of PRS calculation. The use of machine learning for polygenic risk inference is still in the very earlystages; it remains to be seen if this approach will yielddemonstrable improvement over current best practices.
The control we developed here for cross-population PRS
inference is based on differences in ancestral versus derived
allele frequencies among populations with distinct ancestry
profiles. However, differences in LD across populations withdivergent ancestries can also confound cross-population PRS
inference. This is particularly true for African ancestry populations, which tend to have short and distinct LD blocks
compared to non-African populations. Accordingly, controlling for such differences provides another promising approach for improving cross-population PRS inference.

Chande et al. BMC Medical Genetics 2020, 21(Suppl 2):132

Indeed, a previous study has shown that accommodating differences in LD patterns across populations can substantially
improve the accuracy of PRS computed for distinct ancestry
cohorts [55]. In the future, we plan to combine allele frequency and LD based approaches to improving the accuracy
cross-population PRS.
We employed a population-level approach to T2D genetic risk inference and evaluation in this study, comparing
T2D relative genetic risk between populations topopulation-specific ancestry profiles and epidemiological
data on observed T2D prevalence. Taken together withthe robust collection of T2D variant associations from anumber of diverse GWAS cohorts, this approach allowed
us to broadly assess the impacts of ancestry on T2D genetic risk inference in HL populations. Going forward, amore rigorous assessment of PRS accuracy will require
individual-level phenotype data, for both model training
and test sets. Data of this kind are beginning to emerge
thanks to the activity of a number of diabetes research
consortia along with more broadly focused biobanks thatcollect patient genotypes and electronic health records.
We anticipate that joint analysis of individual-level
genotype-phenotype data gleaned from sources of thiskind will help to further develop and validate ancestryinformed approaches to T2D genetic risk inference.

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12881-020-01068-0.
Additional file 1: Table S1. Summary statistics for type 2 diabetes(T2D) SNP associations from the EBI-NHGRI GWAS Catalog used for PRS
calculations.
Additional file 2. Results on genetic ancestry and admixture forColombian and US populations (Figure S1) and the effects of linkage
disequilibrium (LD) on T2D genetic risk inference (Figures S2 and S3).
Details on the control used to correct for ancestry bias in T2D risk
inference.

Abbreviations
EA: European-American; GWAS: Genome-wide association study;
HL: Hispanic/Latino; LD: Linkage disequilibrium; MA: Mexican-American;
PRS: Polygenic risk score; T2D: Type 2 diabetes; WHO: World HealthOrganization
Acknowledgements
The authors would like to acknowledge the ChocoGen project donors from
Chocó, Colombia.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 21
Supplement 2, 2020: Proceedings of the 16th Annual MCBIOS Conference:
medical genetics. The full contents of the supplement are available athttps://bmcmedgenet.biomedcentral.com/articles/supplements/volume-21supplement-2
Authors’ contributions
Conceptualization, ATC, AV-A and IKJ; methodology, ATC, LR, ABC and IKJ;
formal analysis, ATC, LR and ABC; resources, AV-A, MAM-R and IKJ; writing
ATC and IKJ; visualization, ATC and LR; supervision and project

Page 13 of 14

administration, AV-A, MAM-R and IKJ. All authors have read and approved
the manuscript.
Funding
This research and publication costs were funded by IHRC-Georgia Tech Applied Bioinformatics Laboratory (ATC, LR, ACB, IKJ). AV-A was supported by
Fulbright Colombia. The funding bodies played no role in the design of thestudy and collection, analysis, and interpretation of data or in writing themanuscript.
Availability of data and materials
Whole genome genotype data for ChocoGen project donors - ~ 550 k SNPs
for 98 individuals - are made available upon request (see https://www.
chocogen.com/data). All other data used for the analysis are publicly
available and can be accessed as described in the Methods section.
Ethics approval and consent to participate
The ChocoGen project was conducted with the approval of the Ethics
Committee of the Univerisidad Tecnológica del Chocó (ACTA No 01-v1), andall sample donors signed informed consent documents.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic
Drive, Atlanta, GA 30332, USA. 2IHRC-Georgia Tech Applied Bioinformatics
Laboratory, Atlanta, GA, USA. 3PanAmerican Bioinformatics Institute, Cali, Valledel Cauca, Colombia. 4Biomedical Research Institute (COL0082529), Cali,
Colombia. 5Universidad Santiago de Cali, Cali, Colombia. 6Centro deInvestigación en Biodiversidad y Hábitat, Universidad Tecnológica del Chocó,
Quibdó, Chocó, Colombia.
1

Received: 4 June 2020 Accepted: 10 June 2020
Published: 25 June 2020
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