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Am J Hum Genet. 2016 May 5;98(5):857-868. doi: 10.1016/j.ajhg.2016.02.025. Epub 2016 Apr 14.

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

Collaborators (347)

Barclay M, Peyrin-Biroulet L, Chamaillard M, Colombel JF, Cottone M, Croft A, D'Incà R, Halfvarson J, Hanigan K, Henderson P, Hugot JP, Karban A, Kennedy NA, Khan MA, Lémann M, Levine A, Massey D, Milla M, Montgomery GW, Ng SME, Oikonomou I, Peeters H, Proctor DD, Rahier JF, Roberts R, Rutgeerts P, Seibold F, Stronati L, Taylor KM, Törkvist L, Ublick K, Van Limbergen J, Van Gossum A, Vatn MH, Zhang H, Zhang W, Andrews JM, Bampton PA, Barclay M, Florin TH, Gearry R, Krishnaprasad K, Lawrance IC, Mahy G, Montgomery GW, Radford-Smith G, Roberts RL, Simms LA, Amininijad L, Cleynen I, Dewit O, Franchimont D, Georges M, Laukens D, Peeters H, Rahier JF, Rutgeerts P, Theatre E, Van Gossum A, Vermeire S, Aumais G, Baidoo L, Barrie AM 3rd, Beck K, Bernard EJ, Binion DG, Bitton A, Brant SR, Cho JH, Cohen A, Croitoru K, Daly MJ, Datta LW, Deslandres C, Duerr RH, Dutridge D, Ferguson J, Fultz J, Goyette P, Greenberg GR, Haritunians T, Jobin G, Katz S, Lahaie RG, McGovern DP, Nelson L, Ng SM, Ning K, Oikonomou I, Paré P, Proctor DD, Regueiro MD, Rioux JD, Ruggiero E, Schumm LP, Schwartz M, Scott R, Sharma Y, Silverberg MS, Spears D, Steinhart AH, Stempak JM, Swoger JM, Tsagarelis C, Zhang W, Zhang C, Zhao H, Aerts J, Ahmad T, Arbury H, Attwood A, Auton A, Ball SG, Balmforth AJ, Barnes C, Barrett JC, Barroso I, Barton A, Bennett AJ, Bhaskar S, Blaszczyk K, Bowes J, Brand OJ, Braund PS, Bredin F, Breen G, Brown MJ, Bruce IN, Bull J, Burren OS, Burton J, Byrnes J, Caesar S, Cardin N, Clee CM, Coffey AJ, Connell JMC, Conrad DF, Cooper JD, Dominiczak AF, Downes K, Drummond HE, Dudakia D, Dunham A, Ebbs B, Eccles D, Edkins S, Edwards C, Elliot A, Emery P, Evans DM, Evans G, Eyre S, Farmer A 1st, Ferrier N, Flynn E, Forbes A, Forty L, Franklyn JA, Frayling TM, Freathy RM, Giannoulatou E, Gibbs P, Gilbert P, Gordon-Smith K, Gray E, Green E, Groves CJ, Grozeva D, Gwilliam R, Hall A, Hammond N, Hardy M, Harrison P, Hassanali N, Hebaishi H, Hines S, Hinks A, Hitman GA, Hocking L, Holmes C, Howard E, Howard P, Howson JMM, Hughes D, Hunt S, Isaacs JD, Jain M, Jewell DP, Johnson T, Jolley JD, Jones IR, Jones LA, Kirov G, Langford CF, Lango-Allen H, Lathrop GM, Lee J, Lee KL, Lees C, Lewis K, Lindgren CM, Maisuria-Armer M, Maller J, Mansfield J, Marchini JL, Martin P, Massey DCO, McArdle WL, McGuffin P, McLay KE, McVean G, Mentzer A, Mimmack ML, Morgan AE, Morris AP, Mowat C, Munroe PB, Myers S, Newman W, Nimmo ER, O'Donovan MC, Onipinla A, Ovington NR, Owen MJ, Palin K, Palotie A, Parnell K, Pearson R, Pernet D, Perry JRB, Phillips A, Plagnol V, Prescott NJ, Prokopenko I, Quail MA, Rafelt S, Rayner NW, Reid DM, Renwick A, Ring SM, Robertson N, Robson S, Russell E, St Clair D, Sambrook JG, Sanderson JD, Sawcer SJ, Schuilenburg H, Scott CE, Scott R, Seal S, Shaw-Hawkins S, Shields BM, Simmonds MJ, Smyth DJ, Somaskantharajah E, Spanova K, Steer S, Stephens J, Stevens HE, Stirrups K, Stone MA, Strachan DP, Su Z, Symmons DPM, Thompson JR, Thomson W, Tobin MD, Travers ME, Turnbull C, Vukcevic D, Wain LV, Walker M, Walker NM, Wallace C, Warren-Perry M, Watkins NA, Webster J, Weedon MN, Wilson AG, Woodburn M, Wordsworth BP, Yau C, Young AH, Zeggini E, Brown MA, Burton PR, Caulfield MJ, Compston A, Farrall M, Gough SCL, Hall AS, Hattersley AT, Hill AVS, Mathew CG, Pembrey M, Satsangi J, Stratton MR, Worthington J, Hurles ME, Duncanson A, Ouwehand WH, Parkes M, Rahman N, Todd JA, Samani NJ, Kwiatkowski DP, McCarthy MI, Craddock N, Deloukas P, Donnelly P, Blackwell JM, Bramon E, Casas JP, Corvin A, Jankowski J, Markus HS, Palmer CNA, Plomin R, Rautanen A, Trembath RC, Viswanathan AC, Wood NW, Spencer CCA, Band G, Bellenguez C, Freeman C, Hellenthal G, Giannoulatou E, Pirinen M, Pearson R, Strange A, Blackburn H, Bumpstead SJ, Dronov S, Gillman M, Jayakumar A, McCann OT, Liddle J, Potter SC, Ravindrarajah R, Ricketts M, Waller M, Weston P, Widaa S, Whittaker P.

Author information

1
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
2
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
3
Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Psychiatry and Psychotherapy, Charité, Campus Mitte, 10117 Berlin, Germany.
4
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
5
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: roeder@andrew.cmu.edu.

Abstract

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.

PMID:
27087321
PMCID:
PMC4864319
DOI:
10.1016/j.ajhg.2016.02.025
[Indexed for MEDLINE]
Free PMC Article

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