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- Aghakhanian, F, et al.
(författare)
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INTEGRATION OF GWAS AND EPIGENETIC STUDIES IDENTIFIES NOVEL GENES THAT ALTER EXPRESSION IN THE MINOR SALIVARY GLAND IN SJOGREN'S DISEASE
- 2022
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 72-73
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Sjogren’s disease (SjD) is an autoimmune disease characterized by reduced function of exocrine glands (i.e., salivary and lacrimal glands). Epithelial cell damage resulting from lymphocytic infiltration has been implicated in SjD etiology [1]. How genetic and epigenetic changes influence epithelial-immune cell interactions in SjD pathogenesis remain understudied.ObjectivesEvaluate the role of SjD risk loci in salivary gland tissue to gain insights into the potential genes involved in salivary gland dysfunction.MethodsSNPs from 16 regions with SNP-SjD associations (P<5x10-8) in our GWAS study (3232 SjD cases) and meta-analysis of ImmunoChip data (619 SjD cases) [2] were interrogated for eQTLs using Genotype-Tissue Expression (GTEx) minor salivary gland data. Subsequent analysis identified genes that were both eQTLs in the minor salivary gland and significantly expressed in RNA-seq and ATAC-seq data from the submaxillary salivary gland epithelial cell line, A253. Pathway enrichment analysis was performed using gProfiler on the genes where coalescence of eQTL, RNA-seq, and ATAC-seq data was observed. To further validate the results, we performed transcriptome-wide association study (TWAS) analysis using GWAS summary statistics and minor salivary gland eQTL GTEx data.ResultsIn total, 5884 genome-wide significant SNPs from 16 SjD risk loci were identified as potential minor salivary gland eQTLs using two discovery thresholds: p(FDR)<0.05 provided by eQTL study (3566 SNPs) and p(FDR)>0.05 and p<0.05 in eQTL study (2318 SNPs). Further analysis revealed 10 SjD risk loci with SNPs that were minor salivary gland eQTLs for a total of 155 unique genes that had a coalescence of RNA- and ATAC-seq data in A253 cells. Many SNPs altered the expression of the nearest gene to the risk allele (i.e., index gene), such as IRF5 and TNPO3 on chromosome 7 at 128Mb; however, this locus had 12 additional genes that were eQTLs in minor salivary gland. In contrast, other loci had no reported eQTLs for the index gene, but several reported eQTLs for other genes, such TYK2 on chromosome 19 at 10Mb that showed no change in TYK2 expression but eQTLs for 8 distant genes, including ICAM1. Pathway enrichment analysis revealed an enrichment in Butyrophilin (BTN) family interactions (R-HSA-8851) (PAdj=1.564x10-5), including the BTN2A1, BTN2A2, BTN3A1, BTN3A2 and BTN3A3 gene cluster in the MHC region. In further support, TWAS of the minor salivary gland and the SjD GWAS summary statistics (after Bonferroni correction) showed association between SjD and BTN3A2 (p=1.24x10-42), as well as many other loci in the MHC region. In addition, several long non-coding (lnc) RNAs on chromosome 17 were significant, peaking at RP11-259G18.1 (p=4.43x10-10).ConclusionThis study shows that SjD-associated risk alleles influence disease by altering gene expression in immune cells and minor salivary glands. Further, our analysis suggests that altered gene expression in the minor salivary gland expands beyond effects on the index gene to several genes on each locus. Interestingly, we observed minor salivary gland eQTLs for several BTN family genes, which act as cell-surface binding partners to regulate cell-cell interactions, including interactions between epithelial cells and activated T cells [3]. Future work will assess chromatin-chromatin-interactions within the 10 SjD risk loci in salivary gland cells and tissues to map local chromatin regulatory networks that regulate gene expression. Additional transcriptional studies of SjD minor salivary gland tissues will provide further insights into how altered gene expression in the salivary gland influences SjD pathology.References[1]Verstappen. Nat Rev Rheumatol 2021;17(6):333-348.[2]Khatri, et al. Annals of Rheumatic Diseases 2020;79:30-31.[3]Arnett HA, Viney JL. Nature Reviews Immunology 2014;14:559-569.Disclosure of InterestsFarhang Aghakhanian: None declared, Mandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Astrid Rasmussen: None declared, Simon J. Bowman Consultant of: Abbvie, Galapagos, and Novartis in 2020-2021., Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James: None declared, Lars Ronnblom: None declared, R Hal Scofield: None declared, Xavier Mariette: None declared, Marta Alarcon-Riquelme: None declared, Wan Fai Ng: None declared, Kathy Sivils Employee of: Current employee of Janssen, Gunnel Nordmark: None declared, Umesh Deshmukh: None declared, A Darise Farris: None declared, Christopher Lessard: None declared
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- Bailey-Wilson, Joan E, et al.
(författare)
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Analysis of Xq27-28 linkage in the international consortium for prostate cancer genetics (ICPCG) families
- 2012
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Ingår i: BMC Medical Genetics. - London : BioMed Central. - 1471-2350. ; 13, s. 46-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive.Methods: Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed.Results: Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2-3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded.Conclusions: Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2-3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.
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- Beecham, Ashley H, et al.
(författare)
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Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.
- 2013
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Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:11, s. 1353-60
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Tidskriftsartikel (refereegranskat)abstract
- Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 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 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 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 catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.
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