Evaluating and improving power in whole-genome association studies using fixed marker sets

ABSTRACT Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical

genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome,

and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to

improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction

of common variants captured by 25–100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of

putative causal alleles to which it is correlated. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS

Access through your institution Subscribe to this journal Receive 12 print issues and online access $209.00 per year only $17.42 per issue Learn more Buy this article * Purchase on

SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about

institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS REPRODUCIBILITY IN THE UK BIOBANK OF GENOME-WIDE SIGNIFICANT SIGNALS DISCOVERED

IN EARLIER GENOME-WIDE ASSOCIATION STUDIES Article Open access 20 September 2021 CONTROLLING FOR BACKGROUND GENETIC EFFECTS USING POLYGENIC SCORES IMPROVES THE POWER OF GENOME-WIDE

ASSOCIATION STUDIES Article Open access 01 October 2021 EFFICIENT PHASING AND IMPUTATION OF LOW-COVERAGE SEQUENCING DATA USING LARGE REFERENCE PANELS Article 07 January 2021 REFERENCES *

Devlin, B. & Risch, N. A comparison of linkage disequilibrium measures for fine-scale mapping. _Genomics_ 29, 311–322 (1995). Article  CAS  Google Scholar  * Risch, N. & Merikangas,

K. The future of genetic studies of complex human diseases. _Science_ 273, 1516–1517 (1996). Article  CAS  Google Scholar  * Collins, F.S., Brooks, L.D. & Chakravarti, A.A. DNA

polymorphism discovery resource for research on human genetic variation. _Genome Res._ 8, 1229–1231 (1998). Article  CAS  Google Scholar  * Wheeler, D.L. et al. Database resources of the

National Center for Biotechnology Information. _Nucleic Acids Res._ 34, D173–D180 (2006). Article  CAS  Google Scholar  * Gunderson, K.L., Steemers, F.J., Lee, G., Mendoza, L.G. & Chee,

M.S. A genome-wide scalable SNP genotyping assay using microarray technology. _Nat. Genet._ 37, 549–554 (2005). Article  CAS  Google Scholar  * Matsuzaki, H. et al. Genotyping over 100,000

SNPs on a pair of oligonucleotide arrays. _Nat. Methods_ 1, 109–111 (2004). Article  CAS  Google Scholar  * Reich, D.E. et al. Linkage disequilibrium in the human genome. _Nature_ 411,

199–204 (2001). Article  CAS  Google Scholar  * Hirschhorn, J.N. & Daly, M.J. Genome-wide association studies for common diseases and complex traits. _Nat. Rev. Genet._ 6, 95–108 (2005).

Article  CAS  Google Scholar  * Altshuler, D. et al. A haplotype map of the human genome. _Nature_ 437, 1299–1320 (2005). Article  Google Scholar  * Kruglyak, L. Power tools for human

genetics. _Nat. Genet._ 37, 1299–1300 (2005). Article  CAS  Google Scholar  * Carlson, C.S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association

analyses using linkage disequilibrium. _Am. J. Hum. Genet._ 74, 106–120 (2004). Article  CAS  Google Scholar  * Kruglyak, L. & Nickerson, D.A. Variation is the spice of life. _Nat.

Genet._ 27, 234–236 (2001). Article  CAS  Google Scholar  * Pe'er, I. et al. Biases and reconciliation in estimates of linkage disequilibrium in the human genome. _Am. J. Hum. Genet._

78, 588–603 (2006). Article  CAS  Google Scholar  * Purcell, S., Cherny, S.S. & Sham, P.C. Genetic power calculator: design of linkage and association genetic mapping studies of complex

traits. _Bioinformatics_ 19, 149–150 (2003). Article  CAS  Google Scholar  * Pritchard, J.K. & Przeworski, M. Linkage disequilibrium in humans: models and data. _Am. J. Hum. Genet._ 69,

1–14 (2001). Article  CAS  Google Scholar  * Sham, P.C., Cherny, S.S., Purcell, S. & Hewitt, J.K. Power of linkage versus association analysis of quantitative traits, by use of

variance-components models, for sibship data. _Am. J. Hum. Genet._ 66, 1616–1630 (2000). Article  CAS  Google Scholar  * Crawford, D.C. et al. Haplotype diversity across 100 candidate genes

for inflammation, lipid metabolism, and blood pressure regulation in two populations. _Am. J. Hum. Genet._ 74, 610–622 (2004). Article  CAS  Google Scholar  * Chapman, J.M., Cooper, J.D.,

Todd, J.A. & Clayton, D.G. Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. _Hum. Hered._

56, 18–31 (2003). Article  Google Scholar  * Weale, M.E. et al. Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene _SCN1A_: implications for linkage-disequilibrium

gene mapping. _Am. J. Hum. Genet._ 73, 551–565 (2003). Article  CAS  Google Scholar  * de Bakker, P.I. et al. Efficiency and power in genetic association studies. _Nat. Genet._ 37, 1217–1223

(2005). Article  CAS  Google Scholar  * Clark, A.G., Hubisz, M.J., Bustamante, C.D., Williamson, S.H. & Nielsen, R. Ascertainment bias in studies of human genome-wide polymorphism.

_Genome Res._ 15, 1496–1502 (2005). Article  CAS  Google Scholar  * Pritchard, J.K. & Cox, N.J. The allelic architecture of human disease genes: common disease-common variant or not?

_Hum. Mol. Genet._ 11, 2417–2423 (2002). Article  CAS  Google Scholar  * Cohen, J. et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in

_PCSK9_. _Nat. Genet._ 37, 161–165 (2005). Article  CAS  Google Scholar  * Cohen, J.C. et al. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. _Science_ 305, 869–872

(2004). Article  CAS  Google Scholar  * Stram, D.O. et al. Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example

from the Multiethnic Cohort Study. _Hum. Hered._ 55, 27–36 (2003). Article  Google Scholar  * Lin, S., Chakravarti, A. & Cutler, D.J. Exhaustive allelic transmission disequilibrium tests

as a new approach to genome-wide association studies. _Nat. Genet._ 36, 1181–1188 (2004). Article  CAS  Google Scholar  * Botstein, D. & Risch, N. Discovering genotypes underlying human

phenotypes: past successes for mendelian disease, future approaches for complex disease. _Nat. Genet._ 33(Suppl.), 228–237 (2003). Article  CAS  Google Scholar  * Roeder, K., Bacanu, S.A.,

Wasserman, L. & Devlin, B. Using linkage genome scans to improve power of association in genome scans. _Am. J. Hum. Genet._ 78, 243–252 (2006). Article  CAS  Google Scholar  * Excoffier,

L. & Slatkin, M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. _Mol. Biol. Evol._ 12, 921–927 (1995). CAS  Google Scholar  * Barrett, J.C.,

Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. _Bioinformatics_ 21, 263–265 (2005). Article  CAS  Google Scholar  Download references

ACKNOWLEDGEMENTS We acknowledge Affymetrix, Inc. and Illumina, Inc. for sharing product data. We also thank Affymetrix, Inc. for making public genotype data of the HapMap samples generated

by the GeneChip Mapping 500K Array. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Center for Human Genetic Research, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA Itsik

Pe'er, Paul I W de Bakker, Julian Maller, Roman Yelensky, David Altshuler & Mark J Daly * Department of Molecular Biology, Massachusetts General Hospital, Boston, 02114,

Massachusetts, USA Paul I W de Bakker, Roman Yelensky & David Altshuler * Diabetes Unit, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA David Altshuler * Broad

Institute of M.I.T. and Harvard, Cambridge, 02142, Massachusetts, USA Itsik Pe'er, Paul I W de Bakker, David Altshuler & Mark J Daly * Department of Medicine, Harvard Medical

School, Boston, 02115, Massachusetts, USA Itsik Pe'er, David Altshuler & Mark J Daly * Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA Paul I W de

Bakker & David Altshuler * Harvard-M.I.T. Division of Health Sciences and Technology, Cambridge, 02139, Massachusetts, USA Roman Yelensky Authors * Itsik Pe'er View author

publications You can also search for this author inPubMed Google Scholar * Paul I W de Bakker View author publications You can also search for this author inPubMed Google Scholar * Julian

Maller View author publications You can also search for this author inPubMed Google Scholar * Roman Yelensky View author publications You can also search for this author inPubMed Google

Scholar * David Altshuler View author publications You can also search for this author inPubMed Google Scholar * Mark J Daly View author publications You can also search for this author

inPubMed Google Scholar CORRESPONDING AUTHORS Correspondence to David Altshuler or Mark J Daly. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial interests.

SUPPLEMENTARY INFORMATION SUPPLEMENTARY FIG. 1 Power of a Bayesian approach versus the existing frequentist approach. (PDF 21 kb) SUPPLEMENTARY FIG. 2 Genotype relative risk as a function of

the frequency of the causal variant. (PDF 20 kb) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Pe'er, I., de Bakker, P., Maller, J. _et al._

Evaluating and improving power in whole-genome association studies using fixed marker sets. _Nat Genet_ 38, 663–667 (2006). https://doi.org/10.1038/ng1816 Download citation * Received: 21

February 2006 * Accepted: 02 May 2006 * Published: 21 May 2006 * Issue Date: 01 June 2006 * DOI: https://doi.org/10.1038/ng1816 SHARE THIS ARTICLE Anyone you share the following link with

will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt

content-sharing initiative