Publications

2011
Raychaudhuri S. VIZ-GRAIL: visualizing functional connections across disease loci [Internet]. Bioinformatics 2011;27(11):1589-90. Publisher's VersionAbstract
MOTIVATION: As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci. Previously, we defined a statistical strategy, Gene Relationships Across Implicated Loci (GRAIL), to identify whether pair-wise gene relationships defined using PubMed text similarity are enriched across loci. Here, we have implemented VIZ-GRAIL, a software tool to display those relationships and to depict the underlying biological patterns. RESULTS: Our tool can seamlessly interact with the GRAIL web site to obtain the results of analyses and create easy to read visual displays. To most clearly display results, VIZ-GRAIL arranges genes and genetic loci to minimize intersecting pair-wise gene connections. VIZ-GRAIL can be easily applied to other types of functional connections, beyond those from GRAIL. This method should help investigators appreciate the presence of potentially important common functions across loci. AVAILABILITY: The GRAIL algorithm is implemented online at http://www.broadinstitute.org/mpg/grail/grail.php. VIZ-GRAIL source-code is at http://www.broadinstitute.org/mpg/grail/vizgrail.html.
2010
Raychaudhuri S, Korn JM, McCarroll SA, McCarroll SA, Altshuler D, Sklar P, Purcell S, Daly MJ. Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function [Internet]. PLoS Genet 2010;6(9):e1001097. Publisher's VersionAbstract
Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied "pathway" analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package.
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J'an, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, Liang L, Nemesh J, Park J-H, Gustafsson S, Kilpeläinen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga J-J, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JRB, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson J-O, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MTS, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AIF, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proença C, Chen Y-DI, Chen C-M, Chines PS, Clarke R, Coin L, Connell J, Day INM, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJC, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen A-L, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, König IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki M-L, Lorentzon M, Luben RN, Ludwig B, Ludwig B, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Paré G, Parker AN, Perola M, Pichler I, Pietiläinen KH, Platou CGP, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstråle M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo M-L, Tardif J-C, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tönjes A, Tuomi T, van Meurs JBJ, van Ommen G-J, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CIG, Voight BF, Waite LL, Wallaschofski H, Walters BG, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi SI, Hebebrand J, Huikuri HV, James AL, Kähönen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples AL, Smith GD, Erdmann J, Froguel P, Grönberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin M-R, Kaprio J, Karpe F, Khaw K-T, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PEH, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Watkins H, Wilson JF, Wright AF, Zillikens CM, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann H-E, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, Loos RJF. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index [Internet]. Nat Genet 2010;42(11):937-48. Publisher's VersionAbstract
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.
Orozco G, Eyre S, Hinks A, Ke X, consortium Consortium WTCCYEAR, Wilson AG, Bax DE, Morgan AW, Emery P, Steer S, Hocking L, Reid DM, Wordsworth P, Harrison P, Thomson W, Barton A, Worthington J. Association of CD40 with rheumatoid arthritis confirmed in a large UK case-control study [Internet]. Ann Rheum Dis 2010;69(5):813-6. Publisher's VersionAbstract
OBJECTIVE: A recent meta-analysis of published genome-wide association studies (GWAS) in populations of European descent reported novel associations of markers mapping to the CD40, CCL21 and CDK6 genes with rheumatoid arthritis (RA) susceptibility while a large-scale, case-control association study in a Japanese population identified association with multiple single nucleotide polymorphisms (SNPs) in the CD244 gene. The aim of the current study was to validate these potential RA susceptibility markers in a UK population. METHODS: A total of 4 SNPs (rs4810485 in CD40, rs2812378 in CCL21, rs42041 in CDK6 and rs6682654 in CD244) were genotyped in a UK cohort comprising 3962 UK patients with RA and 3531 healthy controls using the Sequenom iPlex platform. Genotype counts in patients and controls were analysed with the chi(2) test using Stata. RESULTS: Association to the CD40 gene was robustly replicated (p=2 x 10(-4), OR 0.86, 95% CI 0.79 to 0.93) and modest evidence was found for association with the CCL21 locus (p=0.04, OR 1.08, 95% CI 1.01 to 1.16). However, there was no evidence for association of rs42041 (CDK6) and rs6682654 (CD244) with RA susceptibility in this UK population. Following a meta-analysis including the original data, association to CD40 was confirmed (p=7.8 x 10(-8), OR 0.87 (95% CI 0.83 to 0.92). CONCLUSION: In this large UK cohort, strong association of the CD40 gene with susceptibility to RA was found, and weaker evidence for association with RA in the CCL21 locus.
Raychaudhuri S, Ripke S, Li M, Neale BM, Fagerness J, Reynolds R, Sobrin L, Swaroop A, Abecasis G, Seddon JM, Daly MJ. Associations of CFHR1-CFHR3 deletion and a CFH SNP to age-related macular degeneration are not independent [Internet]. Nat Genet 2010;42(7):553-5; author reply 555-6. Publisher's Version
Karlson EW, Chibnik LB, Kraft P, Cui J, Keenan BT, Ding B, Raychaudhuri S, Klareskog L, Alfredsson L, Plenge RM. Cumulative association of 22 genetic variants with seropositive rheumatoid arthritis risk [Internet]. Ann Rheum Dis 2010;69(6):1077-85. Publisher's VersionAbstract
BACKGROUND: Recent discoveries of risk alleles have made it possible to define genetic risk profiles for patients with rheumatoid arthritis (RA). This study examined whether a cumulative score based on 22 validated genetic risk alleles for seropositive RA would identify high-risk, asymptomatic individuals who might benefit from preventive interventions. METHODS: Eight human leucocyte antigen (HLA) alleles and 14 single-nucleotide polymorphisms representing 13 validated RA risk loci were genotyped among 289 white seropositive cases and 481 controls from the US Nurses' Health Studies (NHS) and 629 white cyclic-citrullinated peptide antibody-positive cases and 623 controls from the Swedish Epidemiologic Investigation of Rheumatoid Arthritis (EIRA). A weighted genetic risk score (GRS) was created, in which the weight for each risk allele is the log of the published odds ratio (OR). Logistic regression was used to study associations with incident RA. Area under the curve (AUC) statistics were compared from a clinical-only model and clinical plus genetic model in each cohort. RESULTS: Patients with GRS >1.25 SD of the mean had a significantly higher OR of seropositive RA in both NHS (OR=2.9, 95%CI 1.8 to 4.6) and EIRA (OR 3.4, 95% CI 2.3 to 5.0) referent to the population average. In NHS, the AUC for a clinical model was 0.57 and for a clinical plus genetic model was 0.66, and in EIRA was 0.63 and 0.75, respectively. CONCLUSION: The combination of 22 risk alleles into a weighted GRS significantly stratifies individuals for RA risk beyond clinical risk factors alone. Given the low incidence of RA, the clinical utility of a weighted GRS is limited in the general population.
Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, Tan PL, Oh EC, Merriam JE, Souied E, Bernstein PS, Li B, Frederick JM, Zhang K, Brantley MA, Lee AY, Zack DJ, Campochiaro B, Campochiaro P, Ripke S, Smith TR, Barile GR, Katsanis N, Allikmets R, Daly MJ, Seddon JM. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC) [Internet]. Proc Natl Acad Sci U S A 2010;107(16):7395-400. Publisher's VersionAbstract
Advanced age-related macular degeneration (AMD) is the leading cause of late onset blindness. We present results of a genome-wide association study of 979 advanced AMD cases and 1,709 controls using the Affymetrix 6.0 platform with replication in seven additional cohorts (totaling 5,789 unrelated cases and 4,234 unrelated controls). We also present a comprehensive analysis of copy-number variations and polymorphisms for AMD. Our discovery data implicated the association between AMD and a variant in the hepatic lipase gene (LIPC) in the high-density lipoprotein cholesterol (HDL) pathway (discovery P = 4.53e-05 for rs493258). Our LIPC association was strongest for a functional promoter variant, rs10468017, (P = 1.34e-08), that influences LIPC expression and serum HDL levels with a protective effect of the minor T allele (HDL increasing) for advanced wet and dry AMD. The association we found with LIPC was corroborated by the Michigan/Penn/Mayo genome-wide association study; the locus near the tissue inhibitor of metalloproteinase 3 was corroborated by our replication cohort for rs9621532 with P = 3.71e-09. We observed weaker associations with other HDL loci (ABCA1, P = 9.73e-04; cholesterylester transfer protein, P = 1.41e-03; FADS1-3, P = 2.69e-02). Based on a lack of consistent association between HDL increasing alleles and AMD risk, the LIPC association may not be the result of an effect on HDL levels, but it could represent a pleiotropic effect of the same functional component. Results implicate different biologic pathways than previously reported and provide new avenues for prevention and treatment of AMD.
Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, Lees CW, Balschun T, Lee J, Roberts R, Anderson CA, Bis JC, Bumpstead S, Ellinghaus D, Festen EM, Georges M, Green T, Haritunians T, Jostins L, Latiano A, Mathew CG, Montgomery GW, Prescott NJ, Raychaudhuri S, Rotter JI, Schumm P, Sharma Y, Simms LA, Taylor KD, Whiteman D, Wijmenga C, Baldassano RN, Barclay M, Bayless TM, Brand S, Büning C, Cohen A, Colombel J-F, 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 SL, Halfvarson J, Verspaget HW, Hugot J-P, Karban A, Laukens D, Lawrance I, Lemann M, Levine A, Libioulle C, Louis E, Mowat C, Newman W, Panés J, Phillips A, Proctor DD, Regueiro M, Russell R, Rutgeerts P, Sanderson J, Sans M, Seibold F, Steinhart HA, Stokkers PCF, Torkvist L, Kullak-Ublick G, Wilson D, Walters T, Targan SR, Brant SR, Rioux JD, D'Amato M, Weersma RK, Kugathasan S, Griffiths AM, Mansfield JC, Vermeire S, Duerr RH, Silverberg MS, Satsangi J, Schreiber S, Cho JH, Annese V, Hakonarson H, Daly MJ, Parkes M. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci [Internet]. Nat Genet 2010;42(12):1118-25. Publisher's VersionAbstract
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.
Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segrè AV, Speliotes EK, Wheeler E, Soranzo N, Park J-H, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Smith AV, Mägi R, Pastinen T, Liang L, Heid IM, Luan J'an, Thorleifsson G, Winkler TW, Goddard ME, Sin Lo K, Palmer C, Workalemahu T, Aulchenko YS, Johansson A, Zillikens CM, Feitosa MF, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer NL, Hayward C, Hottenga J-J, Jacobs KB, Knowles JW, Kutalik Z, Monda KL, Polasek O, Preuss M, Rayner NW, Robertson NR, Steinthorsdottir V, Tyrer JP, Voight BF, Wiklund F, Xu J, Zhao JH, Nyholt DR, Pellikka N, Perola M, Perry JRB, Surakka I, Tammesoo M-L, Altmaier EL, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman DI, Chen C, Coin L, Cooper MN, Dixon AL, Gibson Q, Grundberg E, Hao K, Juhani Junttila M, Kaplan LM, Kettunen J, König IR, Kwan T, Lawrence RW, Levinson DF, Lorentzon M, McKnight B, Morris AP, Müller M, Suh Ngwa J, Purcell S, Rafelt S, Salem RM, Salvi E, Sanna S, Shi J, Sovio U, Thompson JR, Turchin MC, Vandenput L, Verlaan DJ, Vitart V, White CC, Ziegler A, Almgren P, Balmforth AJ, Campbell H, Citterio L, De Grandi A, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson JG, Freimer NB, Geus EJC, Glorioso N, Haiqing S, Hartikainen A-L, Havulinna AS, Hicks AA, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpeläinen TO, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki M-L, Marusic A, Maschio A, Meitinger T, Mulas A, Paré G, Parker AN, Peden JF, Petersmann A, Pichler I, Pietiläinen KH, Pouta A, Ridderstråle M, Rotter JI, Sambrook JG, Sanders AR, Schmidt CO, Sinisalo J, Smit JH, Stringham HM, Bragi Walters G, Widen E, Wild SH, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle WL, Nelis M, Peters MJ, Ripatti S, van Meurs JBJ, Aben KK, Ardlie KG, Beckmann JS, Beilby JP, Bergman RN, Bergmann S, Collins FS, Cusi D, den Heijer M, Eiriksdottir G, Gejman PV, Hall AS, Hamsten A, Huikuri HV, Iribarren C, Kähönen M, Kaprio J, Kathiresan S, Kiemeney L, Kocher T, Launer LJ, Lehtimäki T, Melander O, Mosley TH, Musk AW, Nieminen MS, O'Donnell CJ, Ohlsson C, Oostra B, Palmer LJ, Raitakari O, Ridker PM, Rioux JD, Rissanen A, Rivolta C, Schunkert H, Shuldiner AR, Siscovick DS, Stumvoll M, Tönjes A, Tuomilehto J, van Ommen G-J, Viikari J, Heath AC, Martin NG, Montgomery GW, Province MA, Kayser M, Arnold AM, Atwood LD, Boerwinkle E, Chanock SJ, Deloukas P, Gieger C, Grönberg H, Hall P, Hattersley AT, Hengstenberg C, Hoffman W, Lathrop MG, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright AF, Assimes TL, Barroso I, Hofman A, Mohlke KL, Boomsma DI, Caulfield MJ, Cupples AL, Erdmann J, Fox CS, Gudnason V, Gyllensten U, Harris TB, Hayes RB, Jarvelin M-R, Mooser V, Munroe PB, Ouwehand WH, Penninx BW, Pramstaller PP, Quertermous T, Rudan I, Samani NJ, Spector TD, Völzke H, Watkins H, Wilson JF, Groop LC, Haritunians T, Hu FB, Kaplan RC, Metspalu A, North KE, Schlessinger D, Wareham NJ, Hunter DJ, O'Connell JR, Strachan DP, Wichmann H-E, Borecki IB, van Duijn CM, Schadt EE, Thorsteinsdottir U, Peltonen L, Uitterlinden AG, Visscher PM, Chatterjee N, Loos RJF, Boehnke M, McCarthy MI, Ingelsson E, Lindgren CM, Abecasis GR, Stefansson K, Frayling TM, Hirschhorn JN. Hundreds of variants clustered in genomic loci and biological pathways affect human height [Internet]. Nature 2010;467(7317):832-8. Publisher's VersionAbstract
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.
Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, Mc Henry KT, Pinchback RM, Ligon AH, Cho Y-J, Haery L, Greulich H, Reich M, Winckler W, Lawrence MS, Weir BA, Tanaka KE, Chiang DY, Bass AJ, Loo A, Hoffman C, Prensner J, Liefeld T, Gao Q, Yecies D, Signoretti S, Maher E, Kaye FJ, Sasaki H, Tepper JE, Fletcher JA, Tabernero J, Baselga J, Tsao M-S, Demichelis F, Rubin MA, Janne PA, Daly MJ, Nucera C, Levine RL, Ebert BL, Gabriel S, Rustgi AK, Antonescu CR, Ladanyi M, Letai A, Garraway LA, Loda M, Beer DG, True LD, Okamoto A, Pomeroy SL, Singer S, Golub TR, Lander ES, Getz G, Sellers WR, Meyerson M. The landscape of somatic copy-number alteration across human cancers [Internet]. Nature 2010;463(7283):899-905. Publisher's VersionAbstract
A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-kappaBeta pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types.
Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney ASF, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PRV, Jørgensen T, Kao WHL, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JRB, Petersen A-K, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner WN, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters BG, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CNA, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann H-E, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, McCarthy MI, McCarthy MI. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis [Internet]. Nat Genet 2010;42(7):579-89. Publisher's VersionAbstract
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
Liao KP, Cai T, Gainer V, Goryachev S, Zeng-Treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I, Karlson EW, Plenge RM. Electronic medical records for discovery research in rheumatoid arthritis [Internet]. Arthritis Care Res (Hoboken) 2010;62(8):1120-7. Publisher's VersionAbstract
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.
Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, Li Y, Kurreeman FAS, Zhernakova A, Hinks A, Guiducci C, Chen R, Alfredsson L, Amos CI, Ardlie KG, Ardlie KG, Barton A, Bowes J, Brouwer E, Burtt NP, Catanese JJ, Coblyn J, Coenen MJ, Costenbader KH, Criswell LA, Crusius BJA, Cui J, de Bakker PIW, De Jager PL, Ding B, Emery P, Flynn E, Harrison P, Hocking LJ, Huizinga TWJ, Kastner DL, Ke X, Lee AT, Liu X, Martin P, Morgan AW, Padyukov L, Posthumus MD, Radstake TRDJ, Reid DM, Seielstad M, Seldin MF, Shadick NA, Steer S, Tak PP, Thomson W, van der Helm-van Mil AHM, van der Horst-Bruinsma IE, van der Schoot EC, van Riel PLCM, Weinblatt ME, Wilson AG, Wolbink GJ, Wordsworth PB, Wordsworth PB, Wijmenga C, Karlson EW, Toes REM, de Vries N, Begovich AB, Worthington J, Siminovitch KA, Gregersen PK, Klareskog L, Plenge RM. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci [Internet]. Nat Genet 2010;42(6):508-14. Publisher's VersionAbstract
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.
Cui J, Saevarsdottir S, Thomson B, Padyukov L, van der Helm-van Mil AHM, Nititham J, Hughes LB, de Vries N, Raychaudhuri S, Alfredsson L, Askling J, Wedrén S, Ding B, Guiducci C, Wolbink GJ, Crusius BJA, van der Horst-Bruinsma IE, Herenius M, Weinblatt ME, Shadick NA, Worthington J, Batliwalla F, Kern M, Morgan AW, Wilson AG, Isaacs JD, Hyrich K, Seldin MF, Moreland LW, Behrens TW, Allaart CF, Criswell LA, Huizinga TWJ, Tak PP, Bridges LS, Toes REM, Barton A, Klareskog L, Gregersen PK, Karlson EW, Plenge RM. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy [Internet]. Arthritis Rheum 2010;62(7):1849-61. Publisher's VersionAbstract
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.
Raychaudhuri S. Recent advances in the genetics of rheumatoid arthritis [Internet]. Curr Opin Rheumatol 2010;22(2):109-18. Publisher's VersionAbstract
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 RM, Raychaudhuri S. Leveraging human genetics to develop future therapeutic strategies in rheumatoid arthritis [Internet]. Rheum Dis Clin North Am 2010;36(2):259-70. Publisher's VersionAbstract
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
van der Linden MPM, Feitsma AL, le Cessie S, Kern M, Olsson LM, Raychaudhuri S, Begovich AB, Chang M, Catanese JJ, Kurreeman FAS, van Nies J, van der Heijde DM, Gregersen PK, Huizinga TWJ, Toes REM, van der Helm-van Mil AHM. Association of a single-nucleotide polymorphism in CD40 with the rate of joint destruction in rheumatoid arthritis [Internet]. Arthritis Rheum 2009;60(8):2242-7. Publisher's VersionAbstract
OBJECTIVE: The severity of joint destruction in rheumatoid arthritis (RA) is highly variable from patient to patient and is influenced by genetic factors. Genome-wide association studies have enormously boosted the field of the genetics of RA susceptibility, but risk loci for RA severity remain poorly defined. A recent meta-analysis of genome-wide association studies identified 6 genetic regions for susceptibility to autoantibody-positive RA: CD40, KIF5A/PIP4K2C, CDK6, CCL21, PRKCQ, and MMEL1/TNFRSF14. The purpose of this study was to investigate whether these newly described genetic regions are associated with the rate of joint destruction. METHODS: RA patients enrolled in the Leiden Early Arthritis Clinic were studied (n=563). Yearly radiographs were scored using the Sharp/van der Heijde method (median followup 5 years; maximum followup 9 years). The rate of joint destruction between genotype groups was compared using a linear mixed model, correcting for age, sex, and treatment strategies. A total of 393 anti-citrullinated protein antibody (ACPA)-positive RA patients from the North American Rheumatoid Arthritis Consortium (NARAC) who had radiographic data available were used for the replication study. RESULTS: The TT and CC/CG genotypes of 2 single-nucleotide polymorphisms, rs4810485 (CD40) and rs42041 (CDK6), respectively, were associated with a higher rate of joint destruction in ACPA-positive RA patients (P=0.003 and P=0.012, respectively), with rs4810485 being significant after Bonferroni correction for multiple testing. The association of the CD40 minor allele with the rate of radiographic progression was replicated in the NARAC cohort (P=0.021). CONCLUSION: A polymorphism in the CD40 locus is associated with the rate of joint destruction in patients with ACPA-positive RA. Our findings provide one of the first non-HLA-related genetic severity factors that has been replicated.
Kraft P, Raychaudhuri S. Complex diseases, complex genes: keeping pathways on the right track [Internet]. Epidemiology 2009;20(4):508-11. Publisher's Version
Raychaudhuri S, Plenge RM, Rossin EJ, Ng ACY, Ng ACY, Purcell SM, Sklar P, Scolnick EM, Xavier RJ, Altshuler D, Daly MJ. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions [Internet]. PLoS Genet 2009;5(6):e1000534. Publisher's VersionAbstract
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/).
Kong A, Steinthorsdottir V, Masson G, Thorleifsson G, Sulem P, Besenbacher S, Jonasdottir A, Sigurdsson A, Kristinsson KT, Jonasdottir A, Frigge ML, Gylfason A, Olason PI, Gudjonsson SA, Sverrisson S, Stacey SN, Sigurgeirsson B, Benediktsdottir KR, Sigurdsson H, Jonsson T, Benediktsson R, Olafsson JH, Johannsson OT, Hreidarsson AB, Sigurdsson G, Sigurdsson G, Ferguson-Smith AC, Gudbjartsson DF, Thorsteinsdottir U, Stefansson K. Parental origin of sequence variants associated with complex diseases [Internet]. Nature 2009;462(7275):868-74. Publisher's VersionAbstract
Effects of susceptibility variants may depend on from which parent they are inherited. Although many associations between sequence variants and human traits have been discovered through genome-wide associations, the impact of parental origin has largely been ignored. Here we show that for 38,167 Icelanders genotyped using single nucleotide polymorphism (SNP) chips, the parental origin of most alleles can be determined. For this we used a combination of genealogy and long-range phasing. We then focused on SNPs that associate with diseases and are within 500 kilobases of known imprinted genes. Seven independent SNP associations were examined. Five-one with breast cancer, one with basal-cell carcinoma and three with type 2 diabetes-have parental-origin-specific associations. These variants are located in two genomic regions, 11p15 and 7q32, each harbouring a cluster of imprinted genes. Furthermore, we observed a novel association between the SNP rs2334499 at 11p15 and type 2 diabetes. Here the allele that confers risk when paternally inherited is protective when maternally transmitted. We identified a differentially methylated CTCF-binding site at 11p15 and demonstrated correlation of rs2334499 with decreased methylation of that site.

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