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1.  Protein array–based profiling of CSF identifies RBPJ as an autoantigen in multiple sclerosis 
Neurology  2013;81(11):956-963.
Objective:
To profile the reactivity of CSF-derived immunoglobulin from patients with multiple sclerosis (MS) against a large panel of antigens, to identify disease-specific reactivities.
Methods:
CSF from subjects with MS with elevated immunoglobulin G and CSF from control subjects presenting with other inflammatory neurologic disease were screened against a protein array consisting of 9,393 proteins. Reactivity to a candidate protein identified using these arrays was confirmed with ELISA and immunocytochemistry.
Results:
Autoantibodies against one protein on the array, recombination signal binding protein for immunoglobulin kappa J region (RBPJ), discriminated between patients with MS and controls (p = 0.0052). Using a large validation cohort, we found a higher prevalence of autoantibodies against RBPJ in the CSF of patients with MS (12.5%) compared with the CSF of patients with other neurologic diseases (1.6%; p = 0.02) by ELISA. This difference in reactivity was restricted to the CSF as serum reactivity against RBPJ did not differ between patients and controls. The presence of CSF autoantibodies against RBPJ was further confirmed by immunocytochemistry.
Conclusions:
These data indicate that RBPJ, a ubiquitous protein of the Notch signaling pathway that plays an important role in Epstein-Barr virus infection, is a novel MS autoantigen candidate that is recognized by CSF-derived immunoglobulin G in a subset of patients with MS.
doi:10.1212/WNL.0b013e3182a43b48
PMCID: PMC3888197  PMID: 23921886
2.  Pleiotropy in complex traits: challenges and strategies 
Nature reviews. Genetics  2013;14(7):483-495.
Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
doi:10.1038/nrg3461
PMCID: PMC4104202  PMID: 23752797
3.  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis 
Beecham, Ashley H | Patsopoulos, Nikolaos A | Xifara, Dionysia K | Davis, Mary F | Kemppinen, Anu | Cotsapas, Chris | Shahi, Tejas S | Spencer, Chris | Booth, David | Goris, An | Oturai, Annette | Saarela, Janna | Fontaine, Bertrand | Hemmer, Bernhard | Martin, Claes | Zipp, Frauke | D’alfonso, Sandra | Martinelli-Boneschi, Filippo | Taylor, Bruce | Harbo, Hanne F | Kockum, Ingrid | Hillert, Jan | Olsson, Tomas | Ban, Maria | Oksenberg, Jorge R | Hintzen, Rogier | Barcellos, Lisa F | Agliardi, Cristina | Alfredsson, Lars | Alizadeh, Mehdi | Anderson, Carl | Andrews, Robert | Søndergaard, Helle Bach | Baker, Amie | Band, Gavin | Baranzini, Sergio E | Barizzone, Nadia | Barrett, Jeffrey | Bellenguez, Céline | Bergamaschi, Laura | Bernardinelli, Luisa | Berthele, Achim | Biberacher, Viola | Binder, Thomas M C | Blackburn, Hannah | Bomfim, Izaura L | Brambilla, Paola | Broadley, Simon | Brochet, Bruno | Brundin, Lou | Buck, Dorothea | Butzkueven, Helmut | Caillier, Stacy J | Camu, William | Carpentier, Wassila | Cavalla, Paola | Celius, Elisabeth G | Coman, Irène | Comi, Giancarlo | Corrado, Lucia | Cosemans, Leentje | Cournu-Rebeix, Isabelle | Cree, Bruce A C | Cusi, Daniele | Damotte, Vincent | Defer, Gilles | Delgado, Silvia R | Deloukas, Panos | di Sapio, Alessia | Dilthey, Alexander T | Donnelly, Peter | Dubois, Bénédicte | Duddy, Martin | Edkins, Sarah | Elovaara, Irina | Esposito, Federica | Evangelou, Nikos | Fiddes, Barnaby | Field, Judith | Franke, Andre | Freeman, Colin | Frohlich, Irene Y | Galimberti, Daniela | Gieger, Christian | Gourraud, Pierre-Antoine | Graetz, Christiane | Graham, Andrew | Grummel, Verena | Guaschino, Clara | Hadjixenofontos, Athena | Hakonarson, Hakon | Halfpenny, Christopher | Hall, Gillian | Hall, Per | Hamsten, Anders | Harley, James | Harrower, Timothy | Hawkins, Clive | Hellenthal, Garrett | Hillier, Charles | Hobart, Jeremy | Hoshi, Muni | Hunt, Sarah E | Jagodic, Maja | Jelčić, Ilijas | Jochim, Angela | Kendall, Brian | Kermode, Allan | Kilpatrick, Trevor | Koivisto, Keijo | Konidari, Ioanna | Korn, Thomas | Kronsbein, Helena | Langford, Cordelia | Larsson, Malin | Lathrop, Mark | Lebrun-Frenay, Christine | Lechner-Scott, Jeannette | Lee, Michelle H | Leone, Maurizio A | Leppä, Virpi | Liberatore, Giuseppe | Lie, Benedicte A | Lill, Christina M | Lindén, Magdalena | Link, Jenny | Luessi, Felix | Lycke, Jan | Macciardi, Fabio | Männistö, Satu | Manrique, Clara P | Martin, Roland | Martinelli, Vittorio | Mason, Deborah | Mazibrada, Gordon | McCabe, Cristin | Mero, Inger-Lise | Mescheriakova, Julia | Moutsianas, Loukas | Myhr, Kjell-Morten | Nagels, Guy | Nicholas, Richard | Nilsson, Petra | Piehl, Fredrik | Pirinen, Matti | Price, Siân E | Quach, Hong | Reunanen, Mauri | Robberecht, Wim | Robertson, Neil P | Rodegher, Mariaemma | Rog, David | Salvetti, Marco | Schnetz-Boutaud, Nathalie C | Sellebjerg, Finn | Selter, Rebecca C | Schaefer, Catherine | Shaunak, Sandip | Shen, Ling | Shields, Simon | Siffrin, Volker | Slee, Mark | Sorensen, Per Soelberg | Sorosina, Melissa | Sospedra, Mireia | Spurkland, Anne | Strange, Amy | Sundqvist, Emilie | Thijs, Vincent | Thorpe, John | Ticca, Anna | Tienari, Pentti | van Duijn, Cornelia | Visser, Elizabeth M | Vucic, Steve | Westerlind, Helga | Wiley, James S | Wilkins, Alastair | Wilson, James F | Winkelmann, Juliane | Zajicek, John | Zindler, Eva | Haines, Jonathan L | Pericak-Vance, Margaret A | Ivinson, Adrian J | Stewart, Graeme | Hafler, David | Hauser, Stephen L | Compston, Alastair | McVean, Gil | De Jager, Philip | Sawcer, Stephen | McCauley, Jacob L
Nature genetics  2013;45(11):10.1038/ng.2770.
Using the ImmunoChip custom genotyping array, we analysed 14,498 multiple sclerosis subjects and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (p-value < 1.0 × 10-4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 multiple sclerosis subjects and 26,703 healthy controls. In these 80,094 individuals of European ancestry we identified 48 new susceptibility variants (p-value < 5.0 × 10-8); three found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants in 103 discrete loci outside of the Major Histocompatibility Complex. With high resolution Bayesian fine-mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalogue of multiple sclerosis risk variants and illustrates the value of fine-mapping in the resolution of GWAS signals.
doi:10.1038/ng.2770
PMCID: PMC3832895  PMID: 24076602
4.  Systematic Localization of Common Disease-Associated Variation in Regulatory DNA 
Science (New York, N.Y.)  2012;337(6099):1190-1195.
Genome-wide association studies (GWAS) have identified many noncoding variants associated with common diseases and traits. We show that these variants are concentrated in regulatory DNA marked by DNase I hypersensitive sites (DHSs). 88% of such DHSs are active during fetal development, and are enriched for gestational exposure-related phenotypes. We identify distant gene targets for hundreds of DHSs that may explain phenotype associations. Disease-associated variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrate tissue-selective enrichment of more weakly disease-associated variants within DHSs, and the de novo identification of pathogenic cell types for Crohn’s disease, multiple sclerosis, and an electrocardiogram trait, without prior knowledge of physiological mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease, and provide pathogenic insights into diverse disorders.
doi:10.1126/science.1222794
PMCID: PMC3771521  PMID: 22955828
6.  Pervasive Sharing of Genetic Effects in Autoimmune Disease 
PLoS Genetics  2011;7(8):e1002254.
Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases—as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple—but not all—immune-mediated diseases (SNP-wise PCPMA<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.
Author Summary
Over the last five years we have found over 100 genetic variants predisposing to common diseases affecting the immune system. In this study we analyze 107 such variants across seven diseases and find that almost half are shared across diseases. We also find that the patterns of sharing across diseases cluster these variants into groups; proteins encoded near variants in the same group tend to interact. This suggests that genetic variation may influence entire pathways to create risk to multiple diseases.
doi:10.1371/journal.pgen.1002254
PMCID: PMC3154137  PMID: 21852963
7.  Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology 
PLoS Genetics  2011;7(1):e1001273.
Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.
Author Summary
Genome-wide association studies have uncovered hundreds of DNA changes associated with complex disease. The ultimate promise of these studies is the understanding of disease biology; this goal, however, is not easily achieved because each disease has yielded numerous associations, each one pointing to a region of the genome, rather than a specific causal mutation. Presumably, the causal variants affect components of common molecular processes, and a first step in understanding the disease biology perturbed in patients is to identify connections among regions associated to disease. Since it has been reported in numerous Mendelian diseases that protein products of causal genes tend to physically bind each other, we chose to approach this problem using known protein–protein interactions to test whether any of the products of genes in five complex trait-associated loci bind each other. We applied several permutation methods and find robustly significant connectivity within four of the traits. In Crohn's disease and rheumatoid arthritis, we are able to show that these genes are co-expressed and that other proteins emerging in the network are enriched for association to disease. These findings suggest that, for the complex traits studied here, associated loci contain variants that affect common molecular processes, rather than distinct mechanisms specific to each association.
doi:10.1371/journal.pgen.1001273
PMCID: PMC3020935  PMID: 21249183
8.  Common body mass index-associated variants confer risk of extreme obesity 
Human Molecular Genetics  2009;18(18):3502-3507.
To investigate the genetic architecture of severe obesity, we performed a genome-wide association study of 775 cases and 3197 unascertained controls at ∼550 000 markers across the autosomal genome. We found convincing association to the previously described locus including the FTO gene. We also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. We found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. Our results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.
doi:10.1093/hmg/ddp292
PMCID: PMC2729668  PMID: 19553259
9.  Genetic Variants Near TNFAIP3 on 6q23 are Associated with Systemic Lupus Erythematosus (SLE) 
Nature genetics  2008;40(9):1059-1061.
SLE is an autoimmune disease influenced by genetic and environmental components. We performed a genome-wide association scan (GWAS) and observed novel association evidence with a variant inTNFAIP3(rs5029939, P = 2.89×10−12, OR = 2.29). We also found evidence of two independent signals of association to SLE risk, including one described in Rheumatoid Arthritis. These results establish that genetic variation inTNFAIP3contributes to differential risk for SLE and RA.
doi:10.1038/ng.200
PMCID: PMC2772171  PMID: 19165918
10.  Intra- and inter-individual genetic differences in gene expression 
Mammalian Genome  2009;20(5):281-295.
Genetic variation is known to influence the amount of mRNA produced by a gene. Because molecular machines control mRNA levels of multiple genes, we expect genetic variation in components of these machines would influence multiple genes in a similar fashion. We show that this assumption is correct by using correlation of mRNA levels measured from multiple tissues in mouse strain panels to detect shared genetic influences. These correlating groups of genes (CGGs) have collective properties that on average account for 52–79% of the variability of their constituent genes and can contain genes that encode functionally related proteins. We show that the genetic influences are essentially tissue-specific and, consequently, the same genetic variations in one animal may upregulate a CGG in one tissue but downregulate the CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. Thus, this class of genetic variation can result in complex inter- and intraindividual differences. This will create substantial challenges in humans, where multiple tissues are not readily available.
Electronic supplementary material
The online version of this article (doi:10.1007/s00335-009-9181-x) contains supplementary material, which is available to authorized users.
doi:10.1007/s00335-009-9181-x
PMCID: PMC2690833  PMID: 19424753
11.  Genome-wide detection and characterization of positive selection in human populations 
Nature  2007;449(7164):913-918.
With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2)1. We used ‘long-range haplotype’ methods, which were developed to identify alleles segregating in a population that have undergone recent selection2, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population: LARGE and DMD, both related to infection by the Lassa virus3, in West Africa; SLC24A5 and SLC45A2, both involved in skin pigmentation4,5, in Europe; and EDAR and EDA2R, both involved in development of hair follicles6, in Asia.
doi:10.1038/nature06250
PMCID: PMC2687721  PMID: 17943131
12.  Two independent alleles at 6q23 associated with risk of rheumatoid arthritis 
Nature genetics  2007;39(12):1477-1482.
To identify susceptibility alleles associated with rheumatoid arthritis, we genotyped 397 individuals with rheumatoid arthritis for 116,204 SNPs and carried out an association analysis in comparison to publicly available genotype data for 1,211 related individuals from the Framingham Heart Study1. After evaluating and adjusting for technical and population biases, we identified a SNP at 6q23 (rs10499194, ∼150 kb from TNFAIP3 and OLIG3) that was reproducibly associated with rheumatoid arthritis both in the genome-wide association (GWA) scan and in 5,541 additional case-control samples (P = 10−3, GWA scan; P < 10−6, replication; P = 10−9, combined). In a concurrent study, the Wellcome Trust Case Control Consortium (WTCCC) has reported strong association of rheumatoid arthritis susceptibility to a different SNP located 3.8 kb from rs10499194 (rs6920220; P = 5 × 10−6 in WTCCC)2. We show that these two SNP associations are statistically independent, are each reproducible in the comparison of our data and WTCCC data, and define risk and protective haplotypes for rheumatoid arthritis at 6q23.
doi:10.1038/ng.2007.27
PMCID: PMC2652744  PMID: 17982456
13.  Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines 
PLoS Genetics  2008;4(11):e1000287.
Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype–phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance—i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.
Author Summary
The use of lymphoblastoid cell lines (LCLs) has evolved from a renewable source of DNA to an in vitro model system to study the genetics of gene expression, drug response, and other traits in a controlled laboratory setting. While convincing relationships between SNPs and mRNA levels (eQTLs) have been described, the degree to which non-genetic variables also influence phenotypes in LCLs is less well characterized. In the course of attempting to map genes for drug responses in vitro, we evaluated the reproducibility of in vitro traits across replicates, the impact of the EBV virus used to transform B cells into cell lines, and the effect of in vitro culture conditions. We found that responses to at least some drugs and levels of many mRNAs can be technically well measured, but vary both across experiments and with non-genetic confounders such as growth rates, EBV levels, and ATP levels. The influence of such non-genetic factors can both decrease power to detect true relationships between DNA variation and traits and create the potential for non-genetic confounding and spurious associations between DNA variants and traits.
doi:10.1371/journal.pgen.1000287
PMCID: PMC2583954  PMID: 19043577

Results 1-13 (13)