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Title: Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
Authors: Hu YJ,  Berndt SI,  Gustafsson S,  Ganna A,  Genetic Investigation of ANthropometric Traits (GIANT) Consortium,  Hirschhorn J,  North KE,  Ingelsson E,  Lin DY,  Berndt SI,  Gustafsson S,  Mägi R,  Ganna A,  Wheeler E,  Feitosa MF,  Justice AE,  Monda KL,  Croteau-Chonka DC,  Day FR,  Esko T,  Fall T,  Ferreira T,  Gentilini D,  Jackson AU,  Luan J,  Randall JC,  Vedantam S,  Willer CJ,  Winkler TW,  Wood AR,  Workalemahu T,  Hu YJ,  Lee SH,  Liang L,  Lin DY,  Min JL,  Neale BM,  Thorleifsson G,  Yang J,  Albrecht E,  Amin N,  Bragg-Gresham JL,  Cadby G,  den Heijer M,  Eklund N,  Fischer K,  Goel A,  Hottenga JJ,  Huffman JE,  Jarick I,  Johansson Å,  Johnson T,  Kanoni S,  Kleber ME,  König IR,  Kristiansson K,  Kutalik Z,  Lamina C,  Lecoeur C,  Li G,  Mangino M,  McArdle WL,  Medina-Gomez C,  Müller-Nurasyid M,  Ngwa JS,  Nolte IM,  Paternoster L,  Pechlivanis S,  Perola M,  Peters MJ,  Preuss M,  Rose LM,  Shi J,  Shungin D,  Smith AV,  Strawbridge RJ,  Surakka I,  Teumer A,  Trip MD,  Tyrer J,  Van Vliet-Ostaptchouk JV,  Vandenput L,  Waite LL,  Zhao JH,  Absher D,  Asselbergs FW,  Atalay M,  Attwood AP,  Balmforth AJ,  Basart H,  Beilby J,  Bonnycastle LL,  Brambilla P,  Bruinenberg M,  Campbell H,  Chasman DI,  Chines PS,  Collins FS,  Connell JM,  Cookson W,  de Faire U,  de Vegt F,  Dei M,  Dimitriou M,  Edkins S,  Estrada K,  Evans DM,  Farrall M,  Ferrario MM,  Ferrières J,  Franke L,  Frau F,  Gejman PV,  Grallert H,  Grönberg H,  Gudnason V,  Hall AS,  Hall P,  Hartikainen AL,  Hayward C,  Heard-Costa NL,  Heath AC,  Hebebrand J,  Homuth G,  Hu FB,  Hunt SE,  Hyppönen E,  Iribarren C,  Jacobs KB,  Jansson JO,  Jula A,  Kähönen M,  Kathiresan S,  Kee F,  Khaw KT,  Kivimaki M,  Koenig W,  Kraja AT,  Kumari M,  Kuulasmaa K,  Kuusisto J,  Laitinen JH,  Lakka TA,  Langenberg C,  Launer LJ,  Lind L,  Lindström J,  Liu J,  Liuzzi A,  Lokki ML,  Lorentzon M,  Madden PA,  Magnusson PK,  Manunta P,  Marek D,  März W,  Mateo Leach I,  McKnight B,  Medland SE,  Mihailov E,  Milani L,  Montgomery GW,  Mooser V,  Mühleisen TW,  Munroe PB,  Musk AW,  Narisu N,  Navis G,  Nicholson G,  Nohr EA,  Ong KK,  Oostra BA,  Palmer CN,  Palotie A,  Peden JF,  Pedersen N,  Peters A,  Polasek O,  Pouta A,  Pramstaller PP,  Prokopenko I,  Pütter C,  Radhakrishnan A,  Raitakari O,  Rendon A,  Rivadeneira F,  Rudan I,  Saaristo TE,  Sambrook JG,  Sanders AR,  Sanna S,  Saramies J,  Schipf S,  Schreiber S,  Schunkert H,  Shin SY,  Signorini S,  Sinisalo J,  Skrobek B,  Soranzo N,  Stančáková A,  Stark K,  Stephens JC,  Stirrups K,  Stolk RP,  Stumvoll M,  Swift AJ,  Theodoraki EV,  Thorand B,  Tregouet DA,  Tremoli E,  Van der Klauw MM,  van Meurs JB,  Vermeulen SH,  Viikari J,  Virtamo J,  Vitart V,  Waeber G,  Wang Z,  Widén E,  Wild SH,  Willemsen G,  Winkelmann BR,  Witteman JC,  Wolffenbuttel BH,  Wong A,  Wright AF,  Zillikens MC,  Amouyel P,  Boehm BO,  Boerwinkle E,  Boomsma DI,  Caulfield MJ,  Chanock SJ,  Cupples LA,  Cusi D,  Dedoussis GV,  Erdmann J,  Eriksson JG,  Franks PW,  Froguel P,  Gieger C,  Gyllensten U,  Hamsten A,  Harris TB,  Hengstenberg C,  Hicks AA,  Hingorani A,  Hinney A,  Hofman A,  Hovingh KG,  Hveem K,  Illig T,  Jarvelin MR,  Jöckel KH,  Keinanen-Kiukaanniemi SM,  Kiemeney LA,  Kuh D,  Laakso M,  Lehtimäki T,  Levinson DF,  Martin NG,  Metspalu A,  Morris AD,  Nieminen MS,  Njølstad I,  Ohlsson C,  Oldehinkel AJ,  Ouwehand WH,  Palmer LJ,  Penninx B,  Power C,  Province MA,  Psaty BM,  Qi L,  Rauramaa R,  Ridker PM,  Ripatti S,  Salomaa V,  Samani NJ,  Snieder H,  Sørensen TI,  Spector TD,  Stefansson K,  Tönjes A,  Tuomilehto J,  Uitterlinden AG,  Uusitupa M,  van der Harst P,  Vollenweider P,  Wallaschofski H,  Wareham NJ,  Watkins H,  Wichmann HE,  Wilson JF,  Abecasis GR,  Assimes TL,  Barroso I,  Boehnke M,  Borecki IB,  Deloukas P,  Fox CS,  Frayling T,  Groop LC,  Haritunian T,  Heid IM,  Hunter D,  Kaplan RC,  Karpe F,  Moffatt M,  Mohlke KL,  O'Connell JR,  Pawitan Y,  Schadt EE,  Schlessinger D,  Steinthorsdottir V,  Strachan DP,  Thorsteinsdottir U,  van Duijn CM,  Visscher PM,  Di Blasio AM,  Hirschhorn JN,  Lindgren CM,  Morris AP,  Meyre D,  Scherag A,  McCarthy MI,  Speliotes EK,  North KE,  Loos RJ,  Ingelsson E
Journal: Am J Hum Genet
Date: 2013 Aug 8
Branches: BB, CGR, LTG, OD, OEEB
PubMed ID: 23891470
PMC ID: PMC3738834
Abstract: Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.