Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation

Mark Shriver, Rui Mei, Esteban J. Parra, Vibhor Sonpar, Indrani Halder, Sarah A. Tishkoff, Theodore G. Schurr, Sergev I. Zhadanov, Ludmila P. Osipova, Tom D. Brutsaert, Jonathan Friedlaender, Lynn B. Jorde, W. Scott Watkins, Michael J. Bamshad, Gerardo Gutierrez, Halina Loi, Hajime Matsuzaki, Rick A. Kittles, George Argyropoulos, Jose R. FernandezJoshua M. Akey, Keith W. Jones

Research output: Contribution to journalArticle

112 Citations (Scopus)

Abstract

Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

Original languageEnglish (US)
Pages (from-to)81-89
Number of pages9
JournalHuman Genomics
Volume2
Issue number2
StatePublished - Jun 1 2005

Fingerprint

Medical Genetics
Single Nucleotide Polymorphism
Population
Cluster Analysis
Alleles
Genes
Inborn Genetic Diseases
Genetic Markers
African Americans
Sample Size

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Drug Discovery

Cite this

Shriver, M., Mei, R., Parra, E. J., Sonpar, V., Halder, I., Tishkoff, S. A., ... Jones, K. W. (2005). Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. Human Genomics, 2(2), 81-89.
Shriver, Mark ; Mei, Rui ; Parra, Esteban J. ; Sonpar, Vibhor ; Halder, Indrani ; Tishkoff, Sarah A. ; Schurr, Theodore G. ; Zhadanov, Sergev I. ; Osipova, Ludmila P. ; Brutsaert, Tom D. ; Friedlaender, Jonathan ; Jorde, Lynn B. ; Watkins, W. Scott ; Bamshad, Michael J. ; Gutierrez, Gerardo ; Loi, Halina ; Matsuzaki, Hajime ; Kittles, Rick A. ; Argyropoulos, George ; Fernandez, Jose R. ; Akey, Joshua M. ; Jones, Keith W. / Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. In: Human Genomics. 2005 ; Vol. 2, No. 2. pp. 81-89.
@article{f07970bc278046728210358c271c0701,
title = "Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation",
abstract = "Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.",
author = "Mark Shriver and Rui Mei and Parra, {Esteban J.} and Vibhor Sonpar and Indrani Halder and Tishkoff, {Sarah A.} and Schurr, {Theodore G.} and Zhadanov, {Sergev I.} and Osipova, {Ludmila P.} and Brutsaert, {Tom D.} and Jonathan Friedlaender and Jorde, {Lynn B.} and Watkins, {W. Scott} and Bamshad, {Michael J.} and Gerardo Gutierrez and Halina Loi and Hajime Matsuzaki and Kittles, {Rick A.} and George Argyropoulos and Fernandez, {Jose R.} and Akey, {Joshua M.} and Jones, {Keith W.}",
year = "2005",
month = "6",
day = "1",
language = "English (US)",
volume = "2",
pages = "81--89",
journal = "Human Genomics",
issn = "1473-9542",
publisher = "Henry Stewart Publications",
number = "2",

}

Shriver, M, Mei, R, Parra, EJ, Sonpar, V, Halder, I, Tishkoff, SA, Schurr, TG, Zhadanov, SI, Osipova, LP, Brutsaert, TD, Friedlaender, J, Jorde, LB, Watkins, WS, Bamshad, MJ, Gutierrez, G, Loi, H, Matsuzaki, H, Kittles, RA, Argyropoulos, G, Fernandez, JR, Akey, JM & Jones, KW 2005, 'Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation', Human Genomics, vol. 2, no. 2, pp. 81-89.

Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. / Shriver, Mark; Mei, Rui; Parra, Esteban J.; Sonpar, Vibhor; Halder, Indrani; Tishkoff, Sarah A.; Schurr, Theodore G.; Zhadanov, Sergev I.; Osipova, Ludmila P.; Brutsaert, Tom D.; Friedlaender, Jonathan; Jorde, Lynn B.; Watkins, W. Scott; Bamshad, Michael J.; Gutierrez, Gerardo; Loi, Halina; Matsuzaki, Hajime; Kittles, Rick A.; Argyropoulos, George; Fernandez, Jose R.; Akey, Joshua M.; Jones, Keith W.

In: Human Genomics, Vol. 2, No. 2, 01.06.2005, p. 81-89.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation

AU - Shriver, Mark

AU - Mei, Rui

AU - Parra, Esteban J.

AU - Sonpar, Vibhor

AU - Halder, Indrani

AU - Tishkoff, Sarah A.

AU - Schurr, Theodore G.

AU - Zhadanov, Sergev I.

AU - Osipova, Ludmila P.

AU - Brutsaert, Tom D.

AU - Friedlaender, Jonathan

AU - Jorde, Lynn B.

AU - Watkins, W. Scott

AU - Bamshad, Michael J.

AU - Gutierrez, Gerardo

AU - Loi, Halina

AU - Matsuzaki, Hajime

AU - Kittles, Rick A.

AU - Argyropoulos, George

AU - Fernandez, Jose R.

AU - Akey, Joshua M.

AU - Jones, Keith W.

PY - 2005/6/1

Y1 - 2005/6/1

N2 - Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

AB - Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

UR - http://www.scopus.com/inward/record.url?scp=24044444786&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=24044444786&partnerID=8YFLogxK

M3 - Article

C2 - 16004724

AN - SCOPUS:24044444786

VL - 2

SP - 81

EP - 89

JO - Human Genomics

JF - Human Genomics

SN - 1473-9542

IS - 2

ER -

Shriver M, Mei R, Parra EJ, Sonpar V, Halder I, Tishkoff SA et al. Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation. Human Genomics. 2005 Jun 1;2(2):81-89.