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2015

Won HH, Natarajan P, Dobbyn A, Jordan D, Roussos P, Lage K, Raychaudhuri S, Stahl E, Do R. Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease. PLoS Genet. 2015;11(10):e1005622.
Publisher's Version
Large genome-wide association studies (GWAS) have identified many genetic loci associated with risk for myocardial infarction (MI) and coronary artery disease (CAD). Concurrently, efforts such as the National Institutes of Health (NIH) Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) Consortium have provided unprecedented data on functional elements of the human genome. In the present study, we systematically investigate the biological link between genetic variants associated with this complex disease and their impacts on gene function. First, we examined the heritability of MI/CAD according to genomic compartments. We observed that single nucleotide polymorphisms (SNPs) residing within nearby regulatory regions show significant polygenicity and contribute between 59-71% of the heritability for MI/CAD. Second, we showed that the polygenicity and heritability explained by these SNPs are enriched in histone modification marks in specific cell types. Third, we found that a statistically higher number of 45 MI/CAD-associated SNPs that have been identified from large-scale GWAS studies reside within certain functional elements of the genome, particularly in active enhancer and promoter regions. Finally, we observed significant heterogeneity of this signal across cell types, with strong signals observed within adipose nuclei, as well as brain and spleen cell types. These results suggest that the genetic etiology of MI/CAD is largely explained by tissue-specific regulatory perturbation within the human genome.
Ermann J, Rao D, Teslovich N, Brenner M, Raychaudhuri S. Immune cell profiling to guide therapeutic decisions in rheumatic diseases. Nat Rev Rheumatol. 2015;11(9):541–51.
Publisher's Version
Biomarkers are needed to guide treatment decisions for patients with rheumatic diseases. Although the phenotypic and functional analysis of immune cells is an appealing strategy for understanding immune-mediated disease processes, immune cell profiling currently has no role in clinical rheumatology. New technologies, including mass cytometry, gene expression profiling by RNA sequencing (RNA-seq) and multiplexed functional assays, enable the analysis of immune cell function with unprecedented detail and promise not only a deeper understanding of pathogenesis, but also the discovery of novel biomarkers. The large and complex data sets generated by these technologies--big data--require specialized approaches for analysis and visualization of results. Standardization of assays and definition of the range of normal values are additional challenges when translating these novel approaches into clinical practice. In this Review, we discuss technological advances in the high-dimensional analysis of immune cells and consider how these developments might support the discovery of predictive biomarkers to benefit the practice of rheumatology and improve patient care.

2014

Cui, Taylor, Lee, Källberg, Weinblatt, Coblyn, Klareskog, Criswell, Gregersen, Shadick, Plenge, Karlson E. The influence of polygenic risk scores on heritability of anti-CCP level in RA. Genes Immun. 2014;15(2):107–14.
Publisher's Version
The objective of this study was to study genetic factors that influence quantitative anticyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. We carried out a genome-wide association study (GWAS) meta-analysis using 1975 anti-CCP+ RA patients from three large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC) and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. GWAS-meta-analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a P-value of 2 × 10(-11) for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5 × 10(-8), and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r(2) in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a P-value of 3 × 10(-7). None of the known RA risk alleles (∼52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. In summary, anti-CCP level is a heritable trait, and HLA-DR3 and GP2 are associated with lower anti-CCP levels.
DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Consortium AGENTD (AGEN T, Consortium SATD (SAT2D), Consortium MATD (MAT2D), Consortium TDGEN generation S (T2D G, Mahajan A, Go MJ, Zhang W, Below J, Gaulton K, Ferreira T, Horikoshi M, Johnson A, Ng M, Prokopenko I, Saleheen D, Wang X, Zeggini E, Abecasis G, Adair L, Almgren P, Atalay M, Aung T, Baldassarre D, Balkau B, Bao Y, Barnett A, Barroso I, Basit A, Been L, Beilby J, Bell G, Benediktsson R, Bergman R, Boehm B, Boerwinkle E, Bonnycastle L, Burtt N, Cai Q, Campbell H, Carey J, Cauchi S, Caulfield M, Chan J, Chang LC, Chang TJ, Chang YC, Charpentier G, Chen CH, Chen H, Chen YT, Chia KS, Chidambaram M, Chines P, Cho N, Cho YM, Chuang LM, Collins F, Cornelis M, Couper D, Crenshaw A, Dam R, Danesh J, Das D, Faire U, Dedoussis G, Deloukas P, Dimas A, Dina C, Doney A, Donnelly P, Dorkhan M, Duijn C, Dupuis J, Edkins S, Elliott P, Emilsson V, Erbel R, Eriksson J, Escobedo J, Esko T, Eury E, Florez J, Fontanillas P, Forouhi N, Forsen T, Fox C, Fraser R, Frayling T, Froguel P, Frossard P, Gao Y, Gertow K, Gieger C, Gigante B, Grallert H, Grant G, Grrop L, Groves C, Grundberg E, Guiducci C, Hamsten A, Han BG, Hara K, Hassanali N, Hattersley A, Hayward C, Hedman A, Herder C, Hofman A, Holmen O, Hovingh K, Hreidarsson A, Hu C, Hu F, Hui J, Humphries S, Hunt S, Hunter D, Hveem K, Hydrie Z, Ikegami H, Illig T, Ingelsson E, Islam M, Isomaa B, Jackson A, Jafar T, James A, Jia W, Jöckel KH, Jonsson A, Jowett J, Kadowaki T, Kang HM, Kanoni S, Kao WH, Kathiresan S, Kato N, Katulanda P, Keinanen-Kiukaanniemi K, Kelly A, Khan H, Khaw KT, Khor CC, Kim HL, Kim S, Kim YJ, Kinnunen L, Klopp N, Kong A, Korpi-Hyövälti E, Kowlessur S, Kraft P, Kravic J, Kristensen M, Krithika, Kumar A, Kumate J, Kuusisto J, Kwak SH, Laakso M, Lagou V, Lakka T, Langenberg C, Langford C, Lawrence R, Leander K, Lee JM, Lee N, Li M, Li X, Li Y, Liang J, Liju S, Lim WY, Lind L, Lindgren C, Lindholm E, Liu CT, Liu J, Lobbens S, Long J, Loos R, Lu W, Luan J, Lyssenko V, Ma R, Maeda S, Mägi R, Männistö S, Matthews D, Meigs J, Melander O, Metspalu A, Meyer J, Mirza G, Mihailov E, Moebus S, Mohan V, Mohlke K, Morris A, Mühleisen T, Müller-Nurasyid M, Musk B, Nakamura J, Nakashima E, Navarro P, Ng PK, Nica A, Nilsson P, Njølstad I, Nöthen M, Ohnaka K, Ong TH, Owen K, Palmer C, Pankow J, Park KS, Parkin M, Pechlivanis S, Pedersen N, Peltonen L, Perry J, Peters A, Pinidiyapathirage J, Platou C, Potter S, Price J, Qi L, Radha V, Rallidis L, Rasheed A, Rathman W, Rauramaa R, Raychaudhuri S, Rayner W, Rees S, Rehnberg E, Ripatti S, Robertson N, Roden M, Rossin E, Rudan I, Rybin D, Saaristo T, Salomaa V, Saltevo J, Samuel M, Sanghera D, Saramies J, Scott J, Scott L, Scott R, Segrè A, Sehmi J, Sennblad B, Shah N, Shah S, Shera S, Ou Shu X, Shuldiner A, Sigurđsson G, Sijbrands E, Silveira A, Sim X, Sivapalaratnam S, Small K, So WY, Stančáková A, Stefansson K, Steinbach G, Steinthorsdottir V, Stirrups K, Strawbridge R, Stringham H, Sun Q, Suo C, Syvänen AC, Takayanagi R, Takeuchi F, Tay WT, Teslovich T, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tikkanen E, Trakalo J, Tremoli E, Trip M, Tsai FJ, Tuomi T, Tuomilehto J, Uitterlinden A, Valladares-Salgado A, Vedantam S, Veglia F, Voight B, Wang C, Wareham N, Wennauer R, Wickremasinghe A, Wilsgaard T, Wilson J, Wiltshire S, Winckler W, Wong TY, Wood A, Wu JY, Wu Y, Yamamoto K, Yamauchi T, Yang M, Yengo L, Yokota M, Young R, Zabaneh D, Zhang F, Zhang R, Zheng W, Zimmet P, Altshuler D, Bowden D, Cho YS, Cox N, Cruz M, Hanis C, Kooner J, Lee JY, Seielstad M, Teo YY, Boehnke M, Parra E, Chambers J, Tai S, McCarthy M, Morris A. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44.
Publisher's Version
To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
Lee M, Ye C, Villani AC, Raj T, Li W, Eisenhaure T, Imboywa S, Chipendo P, Ran A, Slowikowski K, Ward L, Raddassi K, McCabe C, Lee M, Frohlich I, Hafler D, Kellis M, Raychaudhuri S, Zhang F, Stranger B, Benoist C, De Jager P, Regev A, Hacohen N. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science. 2014;343(6175):1246980.
Publisher's Version
Little is known about how human genetic variation affects the responses to environmental stimuli in the context of complex diseases. Experimental and computational approaches were applied to determine the effects of genetic variation on the induction of pathogen-responsive genes in human dendritic cells. We identified 121 common genetic variants associated in cis with variation in expression responses to Escherichia coli lipopolysaccharide, influenza, or interferon-β (IFN-β). We localized and validated causal variants to binding sites of pathogen-activated STAT (signal transducer and activator of transcription) and IRF (IFN-regulatory factor) transcription factors. We also identified a common variant in IRF7 that is associated in trans with type I IFN induction in response to influenza infection. Our results reveal common alleles that explain interindividual variation in pathogen sensing and provide functional annotation for genetic variants that alter susceptibility to inflammatory diseases.
Brownstein C, Beggs A, Homer N, Merriman B, Yu T, Flannery K, DeChene E, Towne M, Savage S, Price E, Holm I, Luquette L, Lyon E, Majzoub J, Neupert P, McCallie D, Szolovits P, Willard H, Mendelsohn N, Temme R, Finkel R, Yum S, Medne L, Sunyaev SR, Adzhubey I, Cassa C, Bakker P, Duzkale H, Dworzyński P, Fairbrother W, Francioli L, Funke B, Giovanni M, Handsaker R, Lage K, Lebo M, Lek M, Leshchiner I, MacArthur D, McLaughlin H, Murray M, Pers T, Polak P, Raychaudhuri S, Rehm H, Soemedi R, Stitziel N, Vestecka S, Supper J, Gugenmus C, Klocke B, Hahn A, Schubach M, Menzel M, Biskup S, Freisinger P, Deng M, Braun M, Perner S, Smith R, Andorf J, Huang J, Ryckman K, Sheffield V, Stone E, Bair T, Black-Ziegelbein A, Braun T, Darbro B, DeLuca A, Kolbe D, Scheetz T, Shearer A, Sompallae R, Wang K, Bassuk A, Edens E, Mathews K, Moore S, Shchelochkov O, Trapane P, Bossler A, Campbell C, Heusel J, Kwitek A, Maga T, Panzer K, Wassink T, Van Daele D, Azaiez H, Booth K, Meyer N, Segal M, Williams M, Tromp G, White P, Corsmeier D, Fitzgerald-Butt S, Herman G, Lamb-Thrush D, McBride K, Newsom D, Pierson C, Rakowsky A, Maver A, Lovrečić L, Palandačić A, Peterlin B, Torkamani A, Wedell A, Huss M, Alexeyenko A, Lindvall J, Magnusson M, Nilsson D, Stranneheim H, Taylan F, Gilissen C, Hoischen A, Bon B, Yntema H, Nelen M, Zhang W, Sager J, Zhang L, Blair K, Kural D, Cariaso M, Lennon G, Javed A, Agrawal S, Ng P, Sandhu K, Krishna S, Veeramachaneni V, Isakov O, Halperin E, Friedman E, Shomron N, Glusman G, Roach J, Caballero J, Cox H, Mauldin D, Ament S, Rowen L, Richards D, San Lucas A, Gonzalez-Garay M, Caskey T, Bai Y, Huang Y, Fang F, Zhang Y, Wang Z, Barrera J, Garcia-Lobo J, González-Lamuño D, Llorca J, Rodriguez M, Varela I, Reese M, De La Vega F, Kiruluta E, Cargill M, Hart R, Sorenson J, Lyon G, Stevenson D, Bray B, Moore B, Eilbeck K, Yandell M, Zhao H, Hou L, Chen X, Yan X, Chen M, Li C, Yang C, Gunel M, Li P, Kong Y, Alexander A, Albertyn Z, Boycott K, Bulman D, Gordon P, Innes M, Knoppers B, Majewski J, Marshall C, Parboosingh J, Sawyer S, Samuels M, Schwartzentruber J, Kohane I, Margulies D. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol. 2014;15(3):R53.
Publisher's Version
BACKGROUND: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
Reynolds R, Ahmed A, Danila M, Hughes L, Investigators CLEAAERA, Gregersen P, Raychaudhuri S, Plenge R, Bridges L. HLA-DRB1-associated rheumatoid arthritis risk at multiple levels in African Americans: hierarchical classification systems, amino acid positions, and residues. Arthritis Rheumatol. 2014;66(12):3274–82.
Publisher's Version
OBJECTIVE: To evaluate HLA-DRB1 genetic risk of rheumatoid arthritis (RA) in African Americans by 3 validated allele classification systems and by amino acid position and residue, and to compare genetic risk between African American and European ancestries. METHODS: Four-digit HLA-DRB1 genotyping was performed on 561 autoantibody-positive African American cases and 776 African American controls. Association analysis was performed on Tezenas du Montcel (TdM), de Vries (DV), and Mattey classification system alleles and separately by amino acid position and individual residues. RESULTS: TdM S2 and S3P alleles were associated with RA (odds ratio [95% confidence interval] 2.8 [2.0-3.9] and 2.1 [1.7-2.7], respectively). The DV (P = 3.2 × 10(-12)) and Mattey (P = 6.5 × 10(-13)) system alleles were both protective in African Americans. Amino acid position 11 (permutation P < 0.00001) accounted for nearly all variability explained by HLA-DRB1, although conditional analysis demonstrated that position 57 was also significant (0.01 ≤ permutation P ≤ 0.05). The valine and aspartic acid residues at position 11 conferred the highest risk of RA in African Americans. CONCLUSION: With some exceptions, the genetic risk conferred by HLA-DRB1 in African Americans is similar to that in individuals of European ancestry at multiple levels: classification system (e.g., TdM), amino acid position (e.g., 11), and residue (Val11). Unlike that reported for individuals of European ancestry, amino acid position 57 was associated with RA in African Americans, but positions 71 and 74 were not. Asp11 (odds ratio 1 in European ancestry) corresponds to the 4-digit classical allele *09:01, which is also a risk allele for RA in Koreans.
Raj T, Rothamel K, Mostafavi S, Ye C, Lee M, Replogle J, Feng T, Lee M, Asinovski N, Frohlich I, Imboywa S, Von Korff A, Okada Y, Patsopoulos N, Davis S, McCabe C, Paik H il, Srivastava G, Raychaudhuri S, Hafler D, Koller D, Regev A, Hacohen N, Mathis D, Benoist C, Stranger B, De Jager P. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344(6183):519–23.
Publisher's Version
To extend our understanding of the genetic basis of human immune function and dysfunction, we performed an expression quantitative trait locus (eQTL) study of purified CD4(+) T cells and monocytes, representing adaptive and innate immunity, in a multi-ethnic cohort of 461 healthy individuals. Context-specific cis- and trans-eQTLs were identified, and cross-population mapping allowed, in some cases, putative functional assignment of candidate causal regulatory variants for disease-associated loci. We note an over-representation of T cell-specific eQTLs among susceptibility alleles for autoimmune diseases and of monocyte-specific eQTLs among Alzheimer's and Parkinson's disease variants. This polarization implicates specific immune cell types in these diseases and points to the need to identify the cell-autonomous effects of disease susceptibility variants.
Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, Kochi Y, Ohmura K, Suzuki A, Yoshida S, Graham R, Manoharan A, Ortmann W, Bhangale T, Denny J, Carroll R, Eyler A, Greenberg J, Kremer J, Pappas D, Jiang L, Yin J, Ye L, Su DF, Yang J, Xie G, Keystone E, Westra HJ, Esko T, Metspalu A, Zhou X, Gupta N, Mirel D, Stahl E, Diogo D, Cui J, Liao K, Guo M, Myouzen K, Kawaguchi T, Coenen MJ, Riel P, Laar M, Guchelaar HJ, Huizinga T, Dieudé P, Mariette X, Bridges L, Zhernakova A, Toes R, Tak P, Miceli-Richard C, Bang SY, Lee HS, Martin J, González-Gay M, Rodriguez-Rodriguez L, Rantapää-Dahlqvist S, Ärlestig L, Choi H, Kamatani Y, Galan P, Lathrop M, RACI Consortium, GARNET consortium, Eyre S, Bowes J, Barton A, Vries N, Moreland L, Criswell L, Karlson E, Taniguchi A, Yamada R, Kubo M, Liu J, Bae SC, Worthington J, Padyukov L, Klareskog L, Gregersen P, Raychaudhuri S, Stranger B, De Jager P, Franke L, Visscher P, Brown M, Yamanaka H, Mimori T, Takahashi A, Xu H, Behrens T, Siminovitch K, Momohara S, Matsuda F, Yamamoto K, Plenge R. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376–81.
Publisher's Version
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
Okada Y, Diogo D, Greenberg J, Mouassess F, Achkar W, Fulton R, Denny J, Gupta N, Mirel D, Gabriel S, Li G, Kremer J, Pappas D, Carroll R, Eyler A, Trynka G, Stahl E, Cui J, Saxena R, Coenen MJ, Guchelaar HJ, Huizinga T, Dieudé P, Mariette X, Barton A, Canhao H, Fonseca J, Vries N, Tak P, Moreland L, Bridges L, Miceli-Richard C, Choi H, Kamatani Y, Galan P, Lathrop M, Raj T, De Jager P, Raychaudhuri S, Worthington J, Padyukov L, Klareskog L, Siminovitch K, Gregersen P, Mardis E, Arayssi T, Kazkaz L, Plenge R. Integration of sequence data from a Consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk gene. PLoS One. 2014;9(2):e87645.
Publisher's Version
Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2 × 10(-6)). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted.