Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations

All authors: Olga Y. Gorlova , Yafang Li, Ivan Gorlov, Jun Ying, Wei V. Chen, Shervin Assassi, John D. Reveille, Frank C. Arnett, Xiaodong Zhou, Lara Bossini-Castillo, Elena Lopez-Isac, Marialbert Acosta-Herrera, Peter K. Gregersen, Annette T. Lee, Virginia D. Steen, Barri J. Fessler, Dinesh Khanna, Elena Schiopu, Richard M. Silver, Jerry A. Molitor, Daniel E. Furst, Suzanne Kafaja, Robert W. Simms, Robert A. Lafyatis, Patricia Carreira, Carmen Pilar Simeon, Ivan Castellvi, Emma Beltran, Norberto Ortego, Christopher I. Amos, Javier Martin, Maureen D. Mayes.Data Availability Statement: Genetic data is available from dbGaP repository (https://www.ncbi. nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_ id=phs000357.v1.p1).Gene-level analysis of ImmunoChip or genome-wide association studies (GWAS) data has not been previously reported for systemic sclerosis (SSc, scleroderma). The objective of this study was to analyze genetic susceptibility loci in SSc at the gene level and to determine if the detected associations were shared in African-American and White populations, using data from ImmunoChip and GWAS genotyping studies. The White sample included 1833 cases and 3466 controls (956 cases and 2741 controls from the US and 877 cases and 725 controls from Spain) and the African American sample, 291 cases and 260 controls. In both Whites and African Americans, we performed a gene-level analysis that integrates association statistics in a gene possibly harboring multiple SNPs with weak effect on disease risk, using Versatile Gene-based Association Study (VEGAS) software. The SNP-level analysis was performed using PLINK v.1.07. We identified 4 novel candidate genes (STAT1, FCGR2C, NIPSNAP3B, and SCT) significantly associated and 4 genes (SERBP1, PINX1, TMEM175 and EXOC2) suggestively associated with SSc in the gene level analysis in White patients. As an exploratory analysis we compared the results on Whites with those from African Americans. Of previously established susceptibility genes identified in Whites, only TNFAIP3 was significant at the nominal level (p = 6.13x10-3) in African Americans in the gene-level analysis of the ImmunoChip data. Among the top suggestive novel genes identified in Whites based on the ImmunoChip data, FCGR2C and PINX1 were only nominally significant in African Americans (p = 0.016 and p = 0.028, respectively), while among the top novel genes identified in the gene-level analysis in African Americans, UNC5C (p = 5.57x10-4) and CLEC16A (p = 0.0463) were also nominally significant in Whites. We also present the gene-level analysis of SSc clinical and autoantibody phenotypes among Whites. Our findings need to be validated by independent studies, particularly due to the limited sample size of African Americans.Funding was provided to MDM by the National Institutes of Health (NIH) the National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIAMS https://www.niams.nih.gov/) Centers of Research Translation (CORT) P50-AR054144, NIH grant N01-AR-02251 and R01-AR-055258, and the Department of Defense (DD) Congressionally Directed Medical Research Program (http://cdmrp.army.mil/) W81XWH-07-1-011 and WX81XWH-13-1-0452 for the collection, analysis and interpretation of the data

Relative density of nsSNVs (<i>rdnsv</i>) in different gene sets as estimated with different SNV datasets.

<p>Nervous system genes (NSG, light grey) show a smaller <i>rdnsv</i> than immune system genes (ISG, medium grey) or randomly sampled genes (RSG, dark grey) in a European diploid genome sequence (A, C) and a pooled set of 200 European exome sequences (B, D). The greater <i>rdnsv</i> in the pooled 200 exomes than the individual genome indicates an enrichment of nsSNVs among rare SNVs.</p

Relative density of nonsynonymous variants (<i>rdnsv</i>).

<p>Candidate genes for the nervous system (NSG) and the immune system (ISG) are defined by tissue specific expression or keyword search and further compared with a set of randomly sampled genes (RSG). A) Overall <i>rdnsv</i> estimates for a diploid genome and 200 exome sequences, which reflect the density of nonsynonymous variants on a mixture of SNVs that range from rare to common in their population frequency. B) SNVs from the 200 exome dataset are additionally stratified by their derived allele frequency and a regression model is fitted to the values of <i>rdnsv</i>. The predicted value for the allele frequency of 0 is referred to as <i>rdnsv₀</i>, whereas the predicted value for the allele frequency of 1 is referred to as <i>rdnsv₁</i>. The interval in brackets shows the 2.5% and 97.5% quantiles from 10.000 random draws of genes.</p

Estimates of <i>rdnsv</i> in the 200 exomes in sets of genes as defined by ontology (GO) annotations.

<p>The 10 GO-categories with the smallest (A) and the greatest (B) mean values of <i>rdnsv</i> are shown. The full list of all GO-categories with at least 1000 coding SNVs is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038087#pone.0038087.s005" target="_blank">Table S3</a>. For each category the number of annotated genes, nonsynonymous and synonymous SNVs, the mean and standard deviation of individual <i>rdnsv</i> estimates across the 200 exomes, as well as the values of <i>rdnsv₀</i> and <i>rdnsv₁</i>, are shown.</p

Distribution of <i>rdnsv</i> estimates over 200 individual exomes.

<p>A) expression-based candidate genes and B) keyword-based candidate genes. The value of <i>rdnsv</i> is estimated separately for each of the 200 exomes and found consistently smaller for NSGs (light grey) are than ISGs (medium grey). In addition, smaller estimates of <i>rdnsv</i> for expression-based ISGs than keyword-based ISGs are seen. No difference exists between expression-based NSGs and keyword-based NSGs.</p

Heritability of known autoimmune disease loci.

<p>Components of heritability inferred at previously known autoimmune trait loci not identified for focal trait. Local expectation computed based on fraction of genome analyzed. (*) indicates statistically significant increase over expectation after accounting for nine tests, respectively. Error bars show analytical standard error of estimate. All analyzed autoimmune traits (Crohn's Disease, Rheumatoid Arthritis, Type 1 Diabetes, Multiple Sclerosis, and Ulcerative Colitis) all exhibit significant increase in local where non-autoimmune traits (Bipolar Disorder, Coronary Artery Disease, Hypertension, Type 2 Diabetes) exhibit no significant increase. Autoimmune traits excluded the well-studied MHC region.</p

Heritability of genome-wide SNPs for nine complex traits.

<p>Components of heritability for typed markers (blue) over nine traits and imputed markers (green) over seven WTCCC1 traits shown. Light bars correspond to estimates from the standard variance-component and dark bars correspond to estimate from LD-adjusted variance-component. Two control sub-groups (NBS and 58C) tested against each other as negative control; diseases tested are Bipolar Disorder (BD), Coronary Artery Disease (CAD), Crohn's Disease (CD), Hypertension (HT), Rheumatoid Arthritis (RA), Type 1 Diabetes (T1D), Type 2 Diabetes (T2D), Multiple Sclerosis (MS), Ulcerative Colitis (UC). Autoimmune traits (CD, RA, T1D, UC, and MS) excluded the well-studied MHC region. All traits exhibit an increase after LD adjustment, indicative of a genetic architecture that is shifted towards low-frequency causal variants. Error bars show analytical standard error of estimate.</p

Local heritability around known GWAS loci.

<p>Components of heritability inferred at previously known GWAS loci. computed from leading SNP effect-size; computed from joint model of all known and significant SNPs in region; local expectation computed from and fraction of genome analyzed; and computed from LD adjusted variance component over all loci. (*) indicates statistically significant increase over expectation after accounting for nine tests. Error bars show analytical standard error of estimate. Autoimmune traits (CD, RA, T1D, UC, and MS) excluded the well-studied MHC region.</p

Fraction of simulated local heritability explained in WTCCC2 genotypes.

<p>Analysis of simulated disease architecture with 180 causal 1Mbp loci yielding a true . In each locus, 1–10 causal variants were sampled from either low-frequency () of common (MAF) WTCCC2 SNPs. For each of four methods tested, the fraction of local heritability identified by the method is reported over 50 simulations (with standard error in parenthesis). Top two panels correspond to experiments with observed causal variants and bottom two panels to experiments with causal variants hidden. In A and B only (where causals are typed), bold-faced and represents significant difference from 100% by z-score at (accounting for 5 architectures tested). The ratio of to is reported in the bottom row of each panel (with bold-face indicating significance by t-test at ).</p

Secondary association signals in the HLA locus.

<p>Combined <i>P</i> values are shown for all SNPs and imputed HLA alleles, following conditioning on the HLA-B*0801-DRB1*0301-DQB1*02 (red diamonds), the HLA-B*0801-DRB1*0301-DQB1*02+HLA-DRB1*0701-DQB1*02 (yellow triangles) and the HLA-B*0801-DRB1*0301-DQB1*02+HLA-DRB1*0701-DQB1*02+HLA-DRB1*0102 (green circles) haplotypes. All association results are represented as the −log₁₀ of the combined <i>P</i> values (left y-axis). The most associated SNP and HLA allele in each step of the stepwise conditional logistic regression analysis are indicated by squares. Recombination rates from the HapMap CEU are depicted in blue (right y-axis). Genomic positions on the x-axis are based on the NCBI Build 36 (hg 18) assembly. Mb, megabase pairs.</p