genetic structure race ethnicity admixture and confounding n.
Skip this Video
Loading SlideShow in 5 Seconds..
Genetic Structure, Race/Ethnicity, Admixture and Confounding PowerPoint Presentation
Download Presentation
Genetic Structure, Race/Ethnicity, Admixture and Confounding

Loading in 2 Seconds...

  share
play fullscreen
1 / 60
Download Presentation

Genetic Structure, Race/Ethnicity, Admixture and Confounding - PowerPoint PPT Presentation

bond
157 Views
Download Presentation

Genetic Structure, Race/Ethnicity, Admixture and Confounding

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Genetic Structure, Race/Ethnicity, Admixture and Confounding

  2. 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.”

  3. 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.”

  4. 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.”

  5. 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.

  6. 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.”

  7. 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.”

  8. 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.”

  9. What is the evidence regarding genetic structure and race?

  10. 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

  11. 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

  12. 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

  13. 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)

  14. 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

  15. Gennet • Investigators: • Alan Weder, Aravinda Chakravarti, Richard Cooper, Nik Schork • Field Centers: • Tecumseh, MI (CAU, 179) • Maywood, IL (AFR, 267)

  16. Genoa • Investigators: • Eric Boerwinkle, Andrew Brown, Craig Hanis, Sharon Kardia • Field Centers: • Jackson, MS (AFR 355) • Starr County, TX (HIS 411) • Rochester, MN (CAU 493)

  17. Hypergen • Investigators: • Steven Hunt, Donna Arnett, Mike Province, DC Rao, Mike Province • Field Centers: • Forsyth Cty, NC (AFR 222, CAU 69) • Minneapolis, MN (CAU 166) • Framingham, MA (CAU 178) • Salt Lake City, UT (CAU 179) • Birmingham, AL (AFR 463)

  18. SAPPHIRe • Investigators: • David Curb, Richard Olshen, Tom Quertermous, Neil Risch, Hua Tang • Field Centers: • Stanford (CHI 55, JAP 16) • Hawaii (CHI 25, JAP 144) • Taiwan #1 (CHI 79) • Taiwan #2 (CHI 59) • Taiwan #3 (CHI 189)

  19. 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

  20. FBPP • Genome Screen STR markers, all typed at the NHLBI sponsored Mammalian Genotyping Service, Marshfield, Wisconsin (James Weber) • Total number of markers included = 366.

  21. Analysis (Hua Tang) • 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

  22. Genetic Cluster Analysis4 Clusters

  23. Genetic Cluster AnalysisEast Asians Alone

  24. GCA Classification versus SIRE • Concordant: 3,631 • Discordant: 5 • Discordance Rate: .0014

  25. 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

  26. 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

  27. Nature Genetics37, 177 - 181 (2005) Published online: 23 January 2005; | doi:10.1038/ng1510 Admixture mapping for hypertension loci with genome-scan markers Xiaofeng Zhu1, Amy Luke1, Richard S Cooper1, Tom Quertermous2, Craig Hanis3, Tom Mosley4,C Charles Gu5, Hua Tang6, Dabeeru C Rao5, Neil Risch7, 8 & Alan Weder

  28. Admixture Mapping • Suppose an allele influencing a trait has a different frequency in two ancestral populations • In a population formed by admixture between these two or more ancestral groups, individuals with the trait will have an excess of ancestry at that location from the population with the higher allele frequency • Examples: African Americans, Hispanic Americans

  29. Admixture Mapping • If the admixture occurred recently in history (e.g. over the past 10 generations), then large the ancestry 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

  30. Admixture Mapping • The power of the method depends on how large the effect of the allele is on the trait, and how much the allele frequency differs between ancestral groups • 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

  31. Admixture Mapping in FBPP • Focused on the African American subjects, from three field centers (Gennet, GENOA, and HyperGEN). • Compared hypertensive subjects (cases) to normotensive subjects (controls)

  32. Admixture Mapping in FBPP • African Americans have higher rates of hypertension than other ethnic groups; the degree to which genetic and environmental factors contribute is unknown; there are also large national differences within ethnic groups • If a genetic factor(s) contributes to the excess risk, we would see elevated African ancestry among the African American hypertensive cases at specific chromosome locations

  33. Admixture Mapping in FBPP • 269 microsatellite loci were analyzed in the FBPP subjects • Marker allele frequencies from the ancestral populations are also required. The FBPP white subjects were used to represent the ancestral European population, while a sample of 236 Nigerians was used to represent the African ancestors

  34. Figure 2.Genome-wide marker information content and distributions of Z scores.(a) Marker information content for admixture mapping (red line) and genome-wide Z score plot in pooled cases (black line). (b) The distribution of Z scores in pooled cases. (c) The distribution of Z scores in pooled controls.