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2010

Lango Allen H, Estrada K, Lettre G, Berndt S, Weedon M, Rivadeneira F, Willer C, Jackson A, Vedantam S, Raychaudhuri S, Ferreira T, Wood A, Weyant R, Segrè A, Speliotes E, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa N, Randall J, Qi L, Smith AV, Mägi R, Pastinen T, Liang L, Heid I, Luan J, Thorleifsson G, Winkler T, Goddard M, Sin Lo K, Palmer C, Workalemahu T, Aulchenko Y, Johansson Å, Zillikens C, Feitosa M, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer N, Hayward C, Hottenga JJ, Jacobs K, Knowles J, Kutalik Z, Monda K, Polašek O, Preuss M, Rayner N, Robertson N, Steinthorsdottir V, Tyrer J, Voight B, Wiklund F, Xu J, Zhao JH, Nyholt D, Pellikka N, Perola M, Perry J, Surakka I, Tammesoo ML, Altmaier E, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman D, Chen C, Coin L, Cooper M, Dixon A, Gibson Q, Grundberg E, Hao K, Juhani Junttila, Kaplan L, Kettunen J, König I, Kwan T, Lawrence R, Levinson D, Lorentzon M, McKnight B, Morris A, Müller M, Suh Ngwa J, Purcell S, Rafelt S, Salem R, Salvi E, Sanna S, Shi J, Sovio U, Thompson J, Turchin M, Vandenput L, Verlaan D, Vitart V, White C, Ziegler A, Almgren P, Balmforth A, Campbell H, Citterio L, De Grandi A, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson J, Freimer N, Geus E, Glorioso N, Haiqing S, Hartikainen AL, Havulinna A, Hicks A, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpeläinen T, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki ML, Marusic A, Maschio A, Meitinger T, Mulas A, Paré G, Parker A, Peden J, Petersmann A, Pichler I, Pietiläinen K, Pouta A, Ridderstråle M, Rotter J, Sambrook J, Sanders A, Schmidt CO, Sinisalo J, Smit J, Stringham H, Bragi Walters, Widen E, Wild S, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle W, Nelis M, Peters M, Ripatti S, Meurs J, Aben K, Ardlie K, Beckmann J, Beilby J, Bergman R, Bergmann S, Collins F, Cusi D, Heijer M, Eiriksdottir G, Gejman P, Hall A, Hamsten A, Huikuri H, Iribarren C, Kähönen M, Kaprio J, Kathiresan S, Kiemeney L, Köcher T, Launer L, Lehtimäki T, Melander O, Mosley T, Musk A, Nieminen M, O’Donnell C, Ohlsson C, Oostra B, Palmer L, Raitakari O, Ridker P, Rioux J, Rissanen A, Rivolta C, Schunkert H, Shuldiner A, Siscovick D, Stumvoll M, Tönjes A, Tuomilehto J, Ommen GJ, Viikari J, Heath A, Martin N, Montgomery G, Province M, Kayser M, Arnold A, Atwood L, Boerwinkle E, Chanock S, Deloukas P, Gieger C, Gronberg H, Hall P, Hattersley A, Hengstenberg C, Hoffman W, Lathrop M, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright A, Assimes T, Barroso I, Hofman A, Mohlke K, Boomsma D, Caulfield M, Cupples A, Erdmann J, Fox C, Gudnason V, Gyllensten U, Harris T, Hayes R, Jarvelin MR, Mooser V, Munroe P, Ouwehand W, Penninx B, Pramstaller P, Quertermous T, Rudan I, Samani N, Spector T, Völzke H, Watkins H, Wilson J, Groop L, Haritunians T, Hu F, Kaplan R, Metspalu A, North K, Schlessinger D, Wareham N, Hunter D, O’Connell J, Strachan D, Wichmann HE, Borecki I, Duijn C, Schadt E, Thorsteinsdottir U, Peltonen L, Uitterlinden A, Visscher P, Chatterjee N, Loos R, Boehnke M, McCarthy M, Ingelsson E, Lindgren C, Abecasis G, Stefansson K, Frayling T, Hirschhorn J. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467(7317):832–8.
Publisher's Version
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
Speliotes E, Willer C, Berndt S, Monda K, Thorleifsson G, Jackson A, Lango Allen H, Lindgren C, Luan J, Mägi R, Randall J, Vedantam S, Winkler T, Qi L, Workalemahu T, Heid I, Steinthorsdottir V, Stringham H, Weedon M, Wheeler E, Wood A, Ferreira T, Weyant R, Segrè A, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpeläinen T, Yang J, Bouatia-Naji N, Esko T, Feitosa M, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben K, Absher D, Amin N, Dixon A, Fisher E, Glazer N, Goddard M, Heard-Costa N, Hoesel V, Hottenga JJ, Johansson Å, Johnson T, Ketkar S, Lamina C, Li S, Moffatt M, Myers R, Narisu N, Perry J, Peters M, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott L, Timpson N, Tyrer J, Wingerden S, Watanabe R, White C, Wiklund F, Barlassina C, Chasman D, Cooper M, Jansson JO, Lawrence R, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi M, Almgren P, Arnold A, Aspelund T, Atwood L, Balkau B, Balmforth A, Bennett A, Ben-Shlomo Y, Bergman R, Bergmann S, Biebermann H, Blakemore A, Boes T, Bonnycastle L, Bornstein S, Brown M, Buchanan T, Busonero F, Campbell H, Cappuccio F, Cavalcanti-Proença C, Chen YDI, Chen CM, Chines P, Clarke R, Coin L, Connell J, Day I, Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos M, Eriksson J, Facheris M, Felix S, Fischer-Posovszky P, Folsom A, Friedrich N, Freimer N, Fu M, Gaget S, Gejman P, Geus E, Gieger C, Gjesing A, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt D, Groves C, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall A, Havulinna A, Hayward C, Heath A, Hengstenberg C, Hicks A, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs K, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan L, Kathiresan S, Kettunen J, Kinnunen L, Knowles J, Kolcic I, König I, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer L, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben R, Ludwig B, MAGIC, Manunta P, Marek D, Marre M, Martin N, McArdle W, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery G, Morken M, Morris A, Mulic R, Ngwa J, Nelis M, Neville M, Nyholt D, O’Donnell C, O’Rahilly S, Ong K, Oostra B, Paré G, Parker A, Perola M, Pichler I, Pietiläinen K, Platou C, Polašek O, Pouta A, Rafelt S, Raitakari O, Rayner N, Ridderstråle M, Rief W, Ruokonen A, Robertson N, Rzehak P, Salomaa V, Sanders A, Sandhu M, Sanna S, Saramies J, Savolainen M, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick D, Smit J, Soranzo N, Sovio U, Stephens J, Surakka I, Swift A, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich T, Thompson J, Thomson B, Tönjes A, Tuomi T, Meurs J, Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel C, Voight B, Waite L, Wallaschofski H, Walters B, Widen E, Wiegand S, Wild S, Willemsen G, Witte D, Witteman J, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby J, Farooqi S, Hebebrand J, Huikuri H, James A, Kähönen M, Levinson D, Macciardi F, Nieminen M, Ohlsson C, Palmer L, Ridker P, Stumvoll M, Beckmann J, Boeing H, Boerwinkle E, Boomsma D, Caulfield M, Chanock S, Collins F, Cupples A, Smith GD, Erdmann J, Froguel P, Gronberg H, Gyllensten U, Hall P, Hansen T, Harris T, Hattersley A, Hayes R, Heinrich J, Hu F, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney L, Krude H, Laakso M, Lawlor D, Metspalu A, Munroe P, Ouwehand W, Pedersen O, Penninx B, Peters A, Pramstaller P, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani N, Schwarz P, Shuldiner A, Spector T, Tuomilehto J, Uda M, Uitterlinden A, Valle T, Wabitsch M, Waeber G, Wareham N, Watkins H, Procardis Consortium, Wilson J, Wright A, Zillikens C, Chatterjee N, McCarroll S, Purcell S, Schadt E, Visscher P, Assimes T, Borecki I, Deloukas P, Fox C, Groop L, Haritunians T, Hunter D, Kaplan R, Mohlke K, O’Connell J, Peltonen L, Schlessinger D, Strachan D, Duijn C, Wichmann HE, Frayling T, Thorsteinsdottir U, Abecasis G, Barroso I, Boehnke M, Stefansson K, North K, McCarthy M, Hirschhorn J, Ingelsson E, Loos R. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48.
Publisher's Version
Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
Franke A, McGovern D, Barrett J, Wang K, Radford-Smith G, Ahmad T, Lees C, Balschun T, Lee J, Roberts R, Anderson C, Bis J, Bumpstead S, Ellinghaus D, Festen E, Georges M, Green T, Haritunians T, Jostins L, Latiano A, Mathew C, Montgomery G, Prescott N, Raychaudhuri S, Rotter J, Schumm P, Sharma Y, Simms L, Taylor K, Whiteman D, Wijmenga C, Baldassano R, Barclay M, Bayless T, Brand S, Büning C, Cohen A, Colombel JF, Cottone M, Stronati L, Denson T, De Vos M, D’Inca R, Dubinsky M, Edwards C, Florin T, Franchimont D, Gearry R, Glas J, Van Gossum A, Guthery S, Halfvarson J, Verspaget H, Hugot JP, Karban A, Laukens D, Lawrance I, Lemann M, Levine A, Libioulle C, Louis E, Mowat C, Newman W, Panés J, Phillips A, Proctor D, Regueiro M, Russell R, Rutgeerts P, Sanderson J, Sans M, Seibold F, Steinhart H, Stokkers P, Torkvist L, Kullak-Ublick G, Wilson D, Walters T, Targan S, Brant S, Rioux J, D’Amato M, Weersma R, Kugathasan S, Griffiths A, Mansfield J, Vermeire S, Duerr R, Silverberg M, Satsangi J, Schreiber S, Cho J, Annese V, Hakonarson H, Daly M, Parkes M. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. 2010;42(12):1118–25.
Publisher's Version
We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.
Liao K, Cai T, Gainer V, Goryachev S, Zeng-Treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I, Karlson E, Plenge R. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken). 2010;62(8):1120–7.
Publisher's Version
OBJECTIVE: Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone. METHODS: Subjects with > or =1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms. RESULTS: A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions). CONCLUSION: We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.
Cui J, Saevarsdottir S, Thomson B, Padyukov L, Helm-van Mil A, Nititham J, Hughes L, Vries N, Raychaudhuri S, Alfredsson L, Askling J, Wedrén S, Ding B, Guiducci C, Wolbink GJ, Crusius B, Horst-Bruinsma I, Herenius M, Weinblatt M, Shadick N, Worthington J, Batliwalla F, Kern M, Morgan A, Wilson A, Isaacs J, Hyrich K, Seldin M, Moreland L, Behrens T, Allaart C, Criswell L, Huizinga T, Tak P, Bridges L, Toes R, Barton A, Klareskog L, Gregersen P, Karlson E, Plenge R. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy. Arthritis Rheum. 2010;62(7):1849–61.
Publisher's Version
OBJECTIVE: Anti-tumor necrosis factor alpha (anti-TNF) therapy is a mainstay of treatment in rheumatoid arthritis (RA). The aim of the present study was to test established RA genetic risk factors to determine whether the same alleles also influence the response to anti-TNF therapy. METHODS: A total of 1,283 RA patients receiving etanercept, infliximab, or adalimumab therapy were studied from among an international collaborative consortium of 9 different RA cohorts. The primary end point compared RA patients with a good treatment response according to the European League Against Rheumatism (EULAR) response criteria (n = 505) with RA patients considered to be nonresponders (n = 316). The secondary end point was the change from baseline in the level of disease activity according to the Disease Activity Score in 28 joints (triangle upDAS28). Clinical factors such as age, sex, and concomitant medications were tested as possible correlates of treatment response. Thirty-one single-nucleotide polymorphisms (SNPs) associated with the risk of RA were genotyped and tested for any association with treatment response, using univariate and multivariate logistic regression models. RESULTS: Of the 31 RA-associated risk alleles, a SNP at the PTPRC (also known as CD45) gene locus (rs10919563) was associated with the primary end point, a EULAR good response versus no response (odds ratio [OR] 0.55, P = 0.0001 in the multivariate model). Similar results were obtained using the secondary end point, the triangle upDAS28 (P = 0.0002). There was suggestive evidence of a stronger association in autoantibody-positive patients with RA (OR 0.55, 95% confidence interval [95% CI] 0.39-0.76) as compared with autoantibody-negative patients (OR 0.90, 95% CI 0.41-1.99). CONCLUSION: Statistically significant associations were observed between the response to anti-TNF therapy and an RA risk allele at the PTPRC gene locus. Additional studies will be required to replicate this finding in additional patient collections.
Stahl E, Raychaudhuri S, Remmers E, Xie G, Eyre S, Thomson B, Li Y, Kurreeman F, Zhernakova A, Hinks A, Guiducci C, Chen R, Alfredsson L, Amos C, Ardlie K, BIRAC Consortium, Barton A, Bowes J, Brouwer E, Burtt N, Catanese J, Coblyn J, Coenen MJ, Costenbader K, Criswell L, Crusius B, Cui J, Bakker P, De Jager P, Ding B, Emery P, Flynn E, Harrison P, Hocking L, Huizinga T, Kastner D, Ke X, Lee A, Liu X, Martin P, Morgan A, Padyukov L, Posthumus M, Radstake T, Reid D, Seielstad M, Seldin M, Shadick N, Steer S, Tak P, Thomson W, Helm-van Mil A, Horst-Bruinsma I, Schoot E, Riel P, Weinblatt M, Wilson A, Wolbink GJ, Wordsworth P, YEAR Consortium, Wijmenga C, Karlson E, Toes R, Vries N, Begovich A, Worthington J, Siminovitch K, Gregersen P, Klareskog L, Plenge R. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet. 2010;42(6):508–14.
Publisher's Version
To identify new genetic risk factors for rheumatoid arthritis, we conducted a genome-wide association study meta-analysis of 5,539 autoantibody-positive individuals with rheumatoid arthritis (cases) and 20,169 controls of European descent, followed by replication in an independent set of 6,768 rheumatoid arthritis cases and 8,806 controls. Of 34 SNPs selected for replication, 7 new rheumatoid arthritis risk alleles were identified at genome-wide significance (P < 5 x 10(-8)) in an analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5 and PXK. We also refined associations at two established rheumatoid arthritis risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed rheumatoid arthritis risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P < 0.05, many of which are validated autoimmune risk alleles, suggesting that most represent genuine rheumatoid arthritis risk alleles.
Raychaudhuri S. Recent advances in the genetics of rheumatoid arthritis. Curr Opin Rheumatol. 2010;22(2):109–18.
Publisher's Version
PURPOSE OF REVIEW: To review the recently discovered genetic risk loci in rheumatoid arthritis (RA), the pathways they implicate, and the genetic architecture of RA. RECENT FINDINGS: Since 2008 investigators have identified many common genetic variants that confer disease risk through single nucleotide polymorphism genotyping studies; the list of variants will no doubt continue to expand at a rapid rate as genotyping technologies evolve and case-control sample collections continue to grow. In aggregate, these variants implicate pathways leading to NF-kappaB (nuclear factor kappa-light-chain-enhancer of activated B cells) activation, the interluekin-2 signaling pathway, and T-cell activation. SUMMARY: Although the effect of any individual variant is modest and even in aggregate considerably less than that of the major histocompatability complex, discovery of recent risk variants suggests immunological processes that are involved in disease pathogenesis.
Plenge R, Raychaudhuri S. Leveraging human genetics to develop future therapeutic strategies in rheumatoid arthritis. Rheum Dis Clin North Am. 2010;36(2):259–70.
Publisher's Version
The purpose of this article is to place these genetic discoveries in the context of current and future therapeutic strategies for patients with RA. More specifically, this article focuses on (1) a brief overview of genetic studies, (2) human genetics as an approach to identify the Achilles heel of disease pathways, (3) humans as the model organism for functional studies of human mutations, (4) pharmacogenetic studies to gain insight into the mechanism of action of drugs, and (5) next-generation patient registries to enable large-scale genotype-phenotype studies.

2009

Raychaudhuri S, Plenge R, Rossin E, Ng A, International Schizophrenia Consortium, Purcell S, Sklar P, Scolnick E, Xavier R, Altshuler D, Daly M. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 2009;5(6):e1000534.
Publisher's Version
Translating a set of disease regions into insight about pathogenic mechanisms requires not only the ability to identify the key disease genes within them, but also the biological relationships among those key genes. Here we describe a statistical method, Gene Relationships Among Implicated Loci (GRAIL), that takes a list of disease regions and automatically assesses the degree of relatedness of implicated genes using 250,000 PubMed abstracts. We first evaluated GRAIL by assessing its ability to identify subsets of highly related genes in common pathways from validated lipid and height SNP associations from recent genome-wide studies. We then tested GRAIL, by assessing its ability to separate true disease regions from many false positive disease regions in two separate practical applications in human genetics. First, we took 74 nominally associated Crohn's disease SNPs and applied GRAIL to identify a subset of 13 SNPs with highly related genes. Of these, ten convincingly validated in follow-up genotyping; genotyping results for the remaining three were inconclusive. Next, we applied GRAIL to 165 rare deletion events seen in schizophrenia cases (less than one-third of which are contributing to disease risk). We demonstrate that GRAIL is able to identify a subset of 16 deletions containing highly related genes; many of these genes are expressed in the central nervous system and play a role in neuronal synapses. GRAIL offers a statistically robust approach to identifying functionally related genes from across multiple disease regions--that likely represent key disease pathways. An online version of this method is available for public use (http://www.broad.mit.edu/mpg/grail/).