Race and Ethnicity in Genetic Epidemiology

Neil Risch

Does Race/Ethnicity Matter?

• Editorial, New England Journal of Medicine:– “Race is biologically meaningless.”

• Nature Genetics Editorial:– “Commonly used ethnic labels are both insufficient

and inaccurate representations of inferred genetic clusters.”

– “Genetic data … show that any two individuals within a particular population are as different genetically as any two people selected from any two populations in the world.”

Does Race/Ethnicity Matter?

• Jack Kemp:– “The human genome project shows there is no

genetic way to tell the races apart. For scientific purposes, race doesn’t exist.”

• President Bill Clinton:– “All the schoolchildren will soon be learning in their

biology classes that all the people in the world – all the people in the world, in terms of their genetic makeup, scientifically, are 99.9% the same. The Serbs, the Albanians, the Irish, the Latins, the Asians.”

Does Race/Ethnicity Matter?

• J. Craig Venter:– “It is disturbing to see reputable scientists and

physicians even categorizing things in terms of race … there is no basis in the genetic code for race.”

Does Race/Ethnicity Matter?

• Eric Lander (Nova Interview):– “The genetic difference between any two

people, whether it’s a Sumo wrestler or a Sports Illustrated bathing suit model – one tenth of a percent. Those two, and any two people on this planet, are 99.9% identical at the DNA level.

Does Race/Ethnicity Matter?

– Eric Lander (continued):• “So race is not a very helpful category to a

geneticist, because it’s focusing on a fairly small number of genes that describe appearance. But if we’re talking about the 30,000 genes that run the human symphony, that’s a tapestry that weaves through every population. That’s why geneticists really don’t think race is a terribly helpful concept.

• “But then to define all the human variation on top of it, we sequenced millions and millions of DNA segments from a worldwide sample of 24 people: Pacific Islanders, Asians, Africans, Americans.”

Does Race/Ethnicity Matter?

• Haga and Venter (Science;July, 2003):– “We are concerned that applying antiquated

labels to the analysis and interpretation of scientific data could result in misleading and biologically meaningless conclusions.”

Does Race/Ethnicity Matter?

• Shields et al (Am Psychol, 2005):– “The authors examine the history of racial

categories, current research practices, and arguments for and against using race variables in genetic analyses. The authors argue that the sociopolitical constructs appropriate for monitoring health disparities are not appropriate for use in genetic studies investigating the etiology of complex diseases.”

What is the evidence regarding genetic structure and race?

Results from Population Genetics Studies

• Bowcock et al, Nature, 1994:– 30 microsatellite loci– 14 populations, 148 subjects:

• African - CAR pygmy, Zaire pygmy, Lisongo• Caucasian – Northern European, Italians• Oceania – Melanesian, New Guinean, Australian• East Asia – Chinese, Japanese, Cambodian• Americas – Maya, Surui, Karatiana

Calafell et al, Eur J Hum Genet, 1998

• 45 microsatellite loci

• 10 populations, 504 subjects– African: CAR pygmy, Zaire pygmy– Caucasian: Dane, Druze– Oceania: Melanesian (Nasioi)– East Asia: Chinese, Japanese, Yakut– Americas: Maya, Surui

Unpublished data (Collaboration with Ken and Judy Kidd)

• 49 SNPs in 14 Loci• 33 populations, 1716 subjects

– African: Biaka, Mbuti, Yoruba, Ibo, Hausa, Ethiopia, African American

– Caucasian: Yemen, Druze, Samaritan, Adygei, Russia, Finn, Dane, Irish, European American

– Oceania: Nasioi, Micronesian– East Asia: SF Chinese, Taiwan Chinese, Hakka, Ami,

Atayal, Japanese, Cambodian, Yakut– Americas: Cheyenne, AZ Pima, MX Pima, Maya,

Ticuna, Surui, Karitiana

What is the evidence regarding genetic structure and race?

• How much correlation is there between self-identified race/ethnicity (SIRE) and genetic structure in the human population?

• Results from the Family Blood Pressure Program (FBPP)

FBPP

• Study of genetic and environmental determinants of hypertension in families

• Four networks, 15 field centers (collection sites), four major race/ethnicity groups: Caucasian (CAU), African American (AFR), East Asian (Chinese, Japanese) (EAS), Hispanic (Mexican American) (HIS)

• Our analysis includes one subject per family

FBPP

• Total of 3,636 individuals included (one per family)

• CAU 1349, 6 sites

• AFR 1308, 4 sites

• HIS 412, 1 site

• EAS 567 (407 CHI, 160 JAP), 5 sites

• 18 SIRE-site combinations total

FBPP

• Genome Screen STR markers, all typed at the NHLBI sponsored Mammalian Genotyping Service, Marshfield, Wisconsin (James Weber)

• Total number of markers included = 366.

Analysis

• Genetic Distances (Reynolds,1983; Nei, 1978) between all pairs of SIRE-sites (18x17/2 = 153 comparisons)

• Multidimensional scaling (MDS) for two dimensional depiction of genetic distances

• Branching tree relating 18 SIRE-sites• Genetic Cluster Analysis (GCA) using

STRUCTURE on all 3,636 subjects (326 markers), comparison with SIRE

Genetic Cluster Analysis4 Clusters

Cluster A Cluster B Cluster C Cluster D

CAU 1348 0 0 1

AFR 3 0 1305 0

HIS 1 0 0 411

CHI 0 407 0 0

JAP 0 160 0 0

Genetic Cluster AnalysisEast Asians Alone

Cluster A Cluster B

CHI 405 2

JAP 4 156

GCA Classification versus SIRE

• Concordant: 3,631

• Discordant: 5

• Discordance Rate: .0014

Reynolds-Stanford-Kaiser Cardiovascular Disease Project

• Investigators:– Stanford: Tom Quertermous, Mark Hlatky,

Steve Fortmann, Rick Myers, Richard Olshen, Neil Risch

– Kaiser: Alan Go, Carlos Iribarren, Malini Chandra, Phenius Lathon

• Analysis by Analabha Basu

SELF-IDENTIFIED RACE ETHNICITIES

White (Caucasoid) 2281

Black (African-American) 438

Hispanic 197

Indian-Pakistani (South-Asian) 55

Asian/ Asian-American (East-Asian) 223

Native Hawaiian or Other Pacific Islander 9

American-Indian/Native American 2

Mixed-Hispanic 326

Mixed-Other 138

Overview of Genetic data

• 467 Markers (SNPs)

• 452 Autosomal Markers + 15 X-chromosomal Markers

• 77 Candidate Genes

• 73 on Autosomal Chromosomes + 4 on X-chromosome

Multidimensional Scaling ( using Reynolds Distance)

South-Asians are with Hispanics

Multidimensional Scaling

Structure with 4 ancestral populations

Self-Identified Inferred Clusters Number ofPopulation 1 2 3 4 IndividualsCaucasian 0.943 0.004 0.004 0.050 265 African-American 0.011 0.989 0.000 0.000 183 Hispanic 0.138 0.000 0.000 0.862 181 South-Asian 0.287 0.000 0.006 0.706 55 East-Asian 0.014 0.000 0.981 0.005 215

Structure with 5 ancestral populations

Self-Identified Inferred Clusters Number ofPopulation 1 2 3 4 5 IndividualsCaucasoid 0.858 0.027 0.108 0.004 0.004 265 African-American 0.011 0.000 0.000 0.000 0.989 183 Hispanic 0.126 0.742 0.132 0.000 0.000 181 South-Asian 0.046 0.018 0.935 0.000 0.000 55 East-Asian 0.014 0.005 0.000 0.981 0.000 215

Analysis of Group Differences

• SIRE and GCA give nearly identical results with enough genetic markers

• Important environmental/social/cultural differences also exist between SIRE groups

• High correlation between SIRE and GCA leads to strong confounding between genetic and non-genetic factors when examining group differences in prevalence of diseases or traits

Analysis of Group Differences

• Ignoring the SIRE/GCA relationship (and avoiding SIRE, using GCA only) runs the risk of false inference of genetic explanations for group differences

• Distinguishing between genetic and non-genetic sources of group differences best examined within a single admixed group, but depends on variation in admixture levels, and is still possibly subject to residual correlation and confounding

Analysis of Individuals Admixture Analysis

• Even though the four ethnic groups were easily separable based on genetic markers, African Americans and Latino Americans typically have ancestry from multiple continents. Using the same genetic markers, it is possible to estimate for each individual the proportions of ancestry, or individual ancestry (IA) from each continental/ancestral group.

Analysis of Individuals Admixture Analysis

• African Americans and Latino Americans typically have ancestry from multiple continents. Using genetic markers, it is possible to estimate for each individual the proportions of ancestry, or individual ancestry (IA) from each continental/ancestral group.

Admixture AnalysisFBPP

• Estimation of ancestry requires genotypes of individuals representing the original indigenous ancestors. For our analyses, we included 1,378 unrelated Caucasians from the FBPP, 127 unrelated sub-Saharan Africans and 50 Native Americans from the World Diversity Panel.

Admixture Analysis - FBPP

• These various data sources shared 284 microsatellite markers from the Marshfield Screening Set 10, where all subjects were genotyped.

• IA estimates were obtained from the genetic cluster analysis program Structure (Pritchard et al).

African Ancestry in African Americans

Ancestry in Mexican Americans from Starr County, Texas

Admixture Analysis

• Distinguishing between genetic and non-genetic sources of group differences can be examined within a single admixed population.

• Depends on variation in admixture levels within that population

• Examine correlation of individual ancestry (IA) with trait of interest (e.g. does blood pressure correlate with African ancestry?)

Admixture Analysis - FBPP

• 3,207 African Americans representing 1,801 sibships from 4 recruitment sites

• 1,506 Mexican Americans representing 453 sibships from 1 recruitment site

• Estimated IA and its correlation with blood pressure, hypertension, and BMI

Admixture Analysis – Blood Pressure and BMI

• For blood pressure and BMI, performed linear regression on estimated African IA for the African Americans (n=1424) and on African IA and Caucasian IA for the Mexican Americans (n=1122), adjusted for age, age2, sex and field center. BMI was included as a covariate for blood pressure

African IA in hypertensives versus normotensives

Site Group Hypertensive Normotensive Delta P value

Number Mean (sd)

Number Mean (sd)

Maywd Afr. Amer.

49 .863 (.097)

141 .867 (.092)

-.004 .805

Jackson Afr. Amer.

223 .851 (.123)

37 .827 (.113)

.024 .264

Forsyth Afr. Amer.

144 .845 (.114)

47 .820 (.139)

.025 .225

Birming Afr. Amer.

351 .881 (.086)

34 .860 (.102)

.021 .170

Starr Mex. Amer.

101 .043 (.029)

161 .043 (.030)

.000 .89

Results of ANOVA of African IA

Factor df Sum of Sq.

Mean Sq.

F value P value

Site 3 .271 .093 8.269 .00002

Hyper-tension

1 .035 .035 3.185 .075

Resid. 1021 11.148 .011

Linear Regression on African IA in African Americans

b(IA)

SBP

b(IA)

DBP

b(IA)

MAP

b(IA)

BMI

5.4 (4.5) 3.0 (3.1) 6.2 (3.3) 4.0 (2.0)*

Regression in Mexican Americans on African and Caucasian IA

Outcome b(IA)

African

b(IA)

Caucasian

SBP 9.5 (21.6) -8.9 (5.8)

DBP 18.9 (10.0)* -1.0 (2.6)

MAP 15.6 (12.6) -3.9 (3.3)

BMI 3.9 (6.0) 4.3 (1.7)*

Admixture Analysis

• Caveat: Still possibly subject to residual correlation and confounding

• For example, within African Americans, discrimination may be related to both skin pigment and adverse health outcomes

• Skin pigment is likely to be genetically correlated with degree of European versus African ancestry

Admixture Mapping

• As opposed to ancestry estimates based on the entire genome, which may be confounded with non-genetic factors, ancestry at specific genetic locations are less likely to be so confounded

• The power of the method depends on how large the effect of an allele is on the trait, and the difference in the frequency of that allele between ancestral groups

Admixture Analysis

• If only a small number of genes contribute to ethnic difference, global estimate may be only poorly correlated with those specific locations

• Therefore, locus-specific analysis might be more informative (admixture mapping)

Admixture Mapping

• If the admixture occurred recently in history (e.g. over the past 10 generations), then the ancestry excess will extend over large segments of the chromosome

• Thus, markers in the vicinity of the trait locus will also show excess ancestry from the population with the higher allele frequency

Admixture Mapping in FBPP

• Estimated locus-specific African ancestry for hypertensives from 3 networks separately; also a pooled group of cases based on more stringent criteria; performed similar analysis on controls (normotensives)

Cases Controls

Red Line = Marker Information

Black Line = Genome-wide Z scores

Distribution of Z Scores

Table 2 Marker locations associated with the largest excess of African ancestry in hypertensive subjects for each individual network

Network and marker Location (cM) Excess African ancestry Z score

GenNet

GATA184A08 6q24.1 (146) 0.021 3.08

D6S2436 6q25.1 (155) 0.021 3.08

D21S1437 21q21 (13) 0.017 2.55

GENOA

GATA184A08 6q24.1 (146) 0.011 4.23

D6S2436 6q25.1 (155) 0.010 3.01

HyperGEN

GATA184A08 6q24.1 (146) 0.017 4.69

D6S2436 6q25.1 (155) 0.011 2.91

D21S1437 21q21 (13) 0.011 2.88

Lessons from Asthma

• Data from Esteban Burchard and colleagues.

• Example of complex interplay between ancestry and environmental factors

Lifetime Asthma Prevalence in US

25.8%

15.8%

12.7%

10.1%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

MexicanAmerican

Caucasian AfricanAmerican

Puerto Rican

Lara et al, 2006

Genetics of Asthma in Latino Americans (GALA)

• Esteban Burchard, PI

• Study of Mexican and Puerto Rican asthmatics from Mexico, Puerto Rico and the US.

Genetics of Asthma in Latino Americans (GALA)

• Estimated African, European and Native American ancestry in Puerto Ricans with ancestry informative markers (AIMS)

• Examined relationship of ancestry and socio-economic status (SES) on asthma risk

• Found an interaction between ancestry, SES and asthma risk

Ancestry-Socioeconomic Status Interaction & Risk of Asthma

Mod / Mid Upper

Cases

Controls

0

5

10

15

20

25

Percent African Ancestry

SES

Asthma

Cases

Controls

In lower SES category, Puerto Ricans patients with asthma had less African and more European ancestry compared to healthy controls, whereas in upper SES category, patients with asthma had more African and less European ancestry compared to healthy controls

Conclusion

• Epidemiologic and genetic studies in admixed populations (e.g. African Americans and Latinos) offers unique opportunities to unravel complex genetic and environmental contributors to disease

Two Examples of Ethnic-Specific Alleles in Pharmacogenetics

• Irinotecan (Camptosar) and colon cancer

• Carbamazepine and Stevens-Johnson Syndrome

Irinotecan and Colon Cancer

• Extreme side effects in some patients– Severe diarrhea, neutropenia– Recommended reduced starting dosage

• Metabolized by uridine diphosphate glucuronosyltransferase isoform 1A1 (UGT1A1)

• Homozygotes/compound heterozygotes for deficiency alleles at greatly increased risk for side effects

Frequency of UGT1A1 Deficiency Genotypes by Ethnic Group

Blacks Whites Asians Pac Isl’s

*28/*28 20% 15% 1% <0.1%

*6/*6 + *6/*28

<0.1% <0.1% 5.5% ?

Stevens-Johnson Syndrome and Carbamazepine (Tegretol)

• Carbamazepine most common cause of SJS in Asians

• HLA B*1502 a major risk factor in Han Chinese• Relative Risk estimated at 2,500 (Chung et al,

Nature 2004)• B*1502 carrier frequency about 8% in Chinese,

very rare or non-existent in other racial groups• May explain greater proportion of SJS due to

carbamazepine in Asians than other groups