Phage display of environmental protein toxins and virulence factors reveals the prevalence, persistence, and genetics of antibody responses.

Microbial exposures are crucial environmental factors that impact healthspan by sculpting the immune system and microbiota. Antibody profiling via Phage ImmunoPrecipitation Sequencing (PhIP-Seq) provides a high-throughput, cost-effective approach for detecting exposure and response to microbial protein products. We designed and constructed a library of 95,601 56-amino acid peptide tiles spanning 14,430 proteins with "toxin" or "virulence factor" keyword annotations. We used PhIP-Seq to profile the antibodies of ∼1,000 individuals against this "ToxScan" library. In addition to enumerating immunodominant antibody epitopes, we studied the age-dependent stability of the ToxScan profile and used a genome-wide association study to find that the MHC-II locus modulates bacterial epitope selection. We detected previously described anti-flagellin antibody responses in a Crohn's disease cohort and identified an association between anti-flagellin antibodies and juvenile dermatomyositis. PhIP-Seq with the ToxScan library is thus an effective tool for studying the environmental determinants of health and disease at cohort scale.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

Pleiotropy method reveals genetic overlap between orofacial clefts at multiple novel loci from GWAS of multi-ethnic trios.

Based on epidemiologic and embryologic patterns, nonsyndromic orofacial clefts- the most common craniofacial birth defects in humans- are commonly categorized into cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP), which are traditionally considered to be etiologically distinct. However, some evidence of shared genetic risk in IRF6, GRHL3 and ARHGAP29 regions exists; only FOXE1 has been recognized as significantly associated with both CL/P and CP in genome-wide association studies (GWAS). We used a new statistical approach, PLACO (pleiotropic analysis under composite null), on a combined multi-ethnic GWAS of 2,771 CL/P and 611 CP case-parent trios. At the genome-wide significance threshold of 5 × 10-8, PLACO identified 1 locus in 1q32.2 (IRF6) that appears to increase risk for one OFC subgroup but decrease risk for the other. At a suggestive significance threshold of 10-6, we found 5 more loci with compelling candidate genes having opposite effects on CL/P and CP: 1p36.13 (PAX7), 3q29 (DLG1), 4p13 (LIMCH1), 4q21.1 (SHROOM3) and 17q22 (NOG). Additionally, we replicated the recognized shared locus 9q22.33 (FOXE1), and identified 2 loci in 19p13.12 (RAB8A) and 20q12 (MAFB) that appear to influence risk of both CL/P and CP in the same direction. We found locus-specific effects may vary by racial/ethnic group at these regions of genetic overlap, and failed to find evidence of sex-specific differences. We confirmed shared etiology of the two OFC subtypes comprising CL/P, and additionally found suggestive evidence of differences in their pathogenesis at 2 loci of genetic overlap. Our novel findings include 6 new loci of genetic overlap between CL/P and CP; 3 new loci between pairwise OFC subtypes; and 4 loci not previously implicated in OFCs. Our in-silico validation showed PLACO is robust to subtype-specific effects, and can achieve massive power gains over existing approaches for identifying genetic overlap between disease subtypes. In summary, we found suggestive evidence for new genetic regions and confirmed some recognized OFC genes either exerting shared risk or with opposite effects on risk to OFC subtypes.

Epidermal differentiation complex genetic variation in atopic dermatitis and peanut allergy.

BACKGROUND: Deleterious variation in the epidermal differentiation complex (EDC) on chromosome 1 is a well-known genetic determinant of atopic dermatitis (AD) and has been associated with risk of peanut allergy (PA) in population-based studies. OBJECTIVE: Our aim was to determine the effect of genetic variation in the EDC on AD trajectory and risk of PA in early life. METHODS: Genome sequencing was used to measure genetic variation in the EDC in the Learning Early about Peanut Allergy (LEAP) study participants. Association tests were done to identify gene- and variant-level predicted deleterious variation associated with AD severity by using the Scoring Atopic Dermatitis (SCORAD) tool (n = 559) at baseline and each follow-up visit, as well as PA and food allergy in peanut avoiders (n = 275). Predicted deleterious variants included missense variants that were frameshift insertions, frameshift deletions, stop-gain mutations, or stop-loss mutations. Associations between variant load, SCORAD score, and PA were tested by using linear and generalized linear regression models. RESULTS: The genes FLG, FLG2, HRNR, and TCHH1 harbored the most predicted deleterious variation (30, 6, 3, and 1 variant, respectively). FLG variants were associated with SCORAD score at all time points; 4 variants (R1798X, R501X, S126X, and S761fs) drove the association with SCORAD score at each time point, and higher variant load was associated with greater AD severity over time. There was an association between these variants and PA, which remained significant independent of baseline AD severity (odds ratio = 2.63 [95% CI = 1.11-6.01] [P = .02]). CONCLUSIONS: Variation in FLG predicted to be deleterious is associated with AD severity at baseline and longitudinally and has an association with PA independent of baseline severity.

Secondary analyses for genome-wide association studies using expression quantitative trait loci.

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

Genome-wide Enrichment of De Novo Coding Mutations in Orofacial Cleft Trios.

Although de novo mutations (DNMs) are known to increase an individual's risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 child-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to an individual's OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

Detecting and quantifying antibody reactivity in PhIP-Seq data with BEER.

SUMMARY: Because of their high abundance, easy accessibility in peripheral blood, and relative stability ex vivo, antibodies serve as excellent records of environmental exposures and immune responses. Phage Immuno-Precipitation Sequencing (PhIP-Seq) is the most efficient technique available for assessing antibody binding to hundreds of thousands of peptides at a cohort scale. PhIP-Seq is a high-throughput approach for assessing antibody reactivity to hundreds of thousands of candidate epitopes. Accurate detection of weakly reactive peptides is particularly important for characterizing the development and decline of antibody responses. Here, we present BEER (Bayesian Enrichment Estimation in R), a software package specifically developed for the quantification of peptide reactivity from PhIP-Seq experiments. BEER implements a hierarchical model and produces posterior probabilities for peptide reactivity and a fold change estimate to quantify the magnitude. BEER also offers functionality to infer peptide reactivity based on the edgeR package, though the improvement in speed is offset by slightly lower sensitivity compared to the Bayesian approach, specifically for weakly reactive peptides. AVAILABILITY AND IMPLEMENTATION: BEER is implemented in R and freely available from the Bioconductor repository at https://bioconductor.org/packages/release/bioc/html/beer.html.

Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing.

Genome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

A Bayesian hierarchical model for signal extraction from protein microarrays.

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data.

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR (Robinson et al., Bioinformatics 26(1):139-140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).

Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment.

BACKGROUND: Multi-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL. METHODS: Samples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL). RESULTS: The incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected. CONCLUSIONS: The LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

Multiethnic genome-wide and HLA association study of total serum IgE level.

BACKGROUND: Total serum IgE (tIgE) is an important intermediate phenotype of allergic disease. Whole genome genetic association studies across ancestries may identify important determinants of IgE. OBJECTIVE: We aimed to increase understanding of genetic variants affecting tIgE production across the ancestry and allergic disease spectrum by leveraging data from the National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine program; the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA); and the Atopic Dermatitis Research Network (N = 21,901). METHODS: We performed genome-wide association within strata of study, disease, and ancestry groups, and we combined results via a meta-regression approach that models heterogeneity attributable to ancestry. We also tested for association between HLA alleles called from whole genome sequence data and tIgE, assessing replication of associations in HLA alleles called from genotype array data. RESULTS: We identified 6 loci at genome-wide significance (P < 5 × 10-9), including 4 loci previously reported as genome-wide significant for tIgE, as well as new regions in chr11q13.5 and chr15q22.2, which were also identified in prior genome-wide association studies of atopic dermatitis and asthma. In the HLA allele association study, HLA-A∗02:01 was associated with decreased tIgE level (Pdiscovery = 2 × 10-4; Preplication = 5 × 10-4; Pdiscovery+replication = 4 × 10-7), and HLA-DQB1∗03:02 was strongly associated with decreased tIgE level in Hispanic/Latino ancestry populations (PHispanic/Latino discovery+replication = 8 × 10-8). CONCLUSION: We performed the largest genome-wide association study and HLA association study of tIgE focused on ancestrally diverse populations and found several known tIgE and allergic disease loci that are relevant in non-European ancestry populations.

A flexible and nearly optimal sequential testing approach to randomized testing: QUICK-STOP.

In the analysis of current life science datasets, we often encounter scenarios in which the application of asymptotic theory to hypothesis testing can be problematic. Besides improved asymptotic results, permutation/simulation-based tests are a general approach to address this issue. However, these randomized tests can impose a massive computational burden, for example, in scenarios in which large numbers of statistical tests are computed, and the specified significance level is very small. Stopping rules aim to assess significance with the smallest possible number of draws while controlling the probabilities of errors due to statistical uncertainty. In this communication, we derive a general stopping rule, QUICK-STOP, based on the sequential testing theory that is easy to implement, controls the error probabilities rigorously, and is nearly optimal in terms of expected draws. In a simulation study, we show that our approach outperforms current stopping approaches for general randomized tests by factor 10 and does not impose an additional computational burden. We illustrate our approach by applying our stopping rule to a single-variant analysis of a whole-genome sequencing study for lung function.

Whole genome sequencing identifies novel genetic mutations in patients with eczema herpeticum.

BACKGROUND: Eczema herpeticum (EH) is a rare complication of atopic dermatitis (AD) caused by disseminated herpes simplex virus (HSV) infection. The role of rare and/or deleterious genetic variants in disease etiology is largely unknown. This study aimed to identify genes that harbor damaging genetic variants associated with HSV infection in AD with a history of recurrent eczema herpeticum (ADEH+). METHODS: Whole genome sequencing (WGS) was performed on 49 recurrent ADEH+ (≥3 EH episodes), 491 AD without a history of eczema herpeticum (ADEH-) and 237 non-atopic control (NA) subjects. Variants were annotated, and a gene-based approach (SKAT-O) was used to identify genes harboring damaging genetic variants associated with ADEH+. Genes identified through WGS were studied for effects on HSV responses and keratinocyte differentiation. RESULTS: Eight genes were identified in the comparison of recurrent ADEH+to ADEH-and NA subjects: SIDT2, CLEC7A, GSTZ1, TPSG1, SP110, RBBP8NL, TRIM15, and FRMD3. Silencing SIDT2 and RBBP8NL in normal human primary keratinocytes (NHPKs) led to significantly increased HSV-1 replication. SIDT2-silenced NHPKs had decreased gene expression of IFNk and IL1b in response to HSV-1 infection. RBBP8NL-silenced NHPKs had decreased gene expression of IFNk, but increased IL1b. Additionally, silencing SIDT2 and RBBP8NL also inhibited gene expression of keratinocyte differentiation markers keratin 10 (KRT10) and loricrin (LOR). CONCLUSION: SIDT2 and RBBP8NL participate in keratinocyte's response to HSV-1 infection. SIDT2 and RBBP8NL also regulate expression of keratinocyte differentiation genes of KRT10 and LOR.

A top scoring pairs classifier for recent HIV infections.

Accurate incidence estimation of HIV infection from cross-sectional biomarker data is crucial for monitoring the epidemic and determining the impact of HIV prevention interventions. A key feature of cross-sectional incidence testing methods is the mean window period, defined as the average duration that infected individuals are classified as recently infected. Two assays available for cross-sectional incidence estimation, the BED capture immunoassay, and the Limiting Antigen (LAg) Avidity assay, measure a general characteristic of antibody response; performance of these assays can be affected and biased by factors such as viral suppression, resulting in sample misclassification and overestimation of HIV incidence. As availability and use of antiretroviral treatment increase worldwide, algorithms that do not include HIV viral load and are not impacted by viral suppression are needed for cross-sectional HIV incidence estimation. Using a phage display system to quantify antibody binding to over 3300 HIV peptides, we present a classifier based on top scoring peptide pairs that identifies recent infections using HIV antibody responses alone. Based on plasma samples from individuals with known dates of seroconversion, we estimated the mean window period for our classifier to be 217 days (95% confidence interval 183 to 257 days), compared to the estimated mean window period for the LAg-Avidity protocol of 106 days (76 to 146 days). Moreover, each of the four peptide pairs correctly classified more of the recent samples than the LAg-Avidity assay alone at the same classification accuracy for non-recent samples.

Association of HLA-DRB1∗09:01 with tIgE levels among African-ancestry individuals with asthma.

BACKGROUND: Asthma is a complex chronic inflammatory disease of the airways. Association studies between HLA and asthma were first reported in the 1970s, and yet, the precise role of HLA alleles in asthma is not fully understood. Numerous genome-wide association studies were recently conducted on asthma, but were always limited to simple genetic markers (single nucleotide polymorphisms) and not complex HLA gene polymorphisms (alleles/haplotypes), therefore not capturing the biological relevance of this complex locus for asthma pathogenesis. OBJECTIVE: To run the first HLA-centric association study with asthma and specific asthma-related phenotypes in a large cohort of African-ancestry individuals. METHODS: We collected high-density genomics data for the Consortium on Asthma among African-ancestry Populations in the Americas (N = 4993) participants. Using computer-intensive machine-learning attribute bagging methods to infer HLA alleles, and Easy-HLA to infer HLA 5-gene haplotypes, we conducted a high-throughput HLA-centric association study of asthma susceptibility and total serum IgE (tIgE) levels in subjects with and without asthma. RESULTS: Among the 1607 individuals with asthma, 972 had available tIgE levels, with a mean tIgE level of 198.7 IU/mL. We could not identify any association with asthma susceptibility. However, we showed that HLA-DRB1∗09:01 was associated with increased tIgE levels (P = 8.5 × 10-4; weighted effect size, 0.51 [0.15-0.87]). CONCLUSIONS: We identified for the first time an HLA allele associated with tIgE levels in African-ancestry individuals with asthma. Our report emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.

Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants.

We previously demonstrated how sharing of rare variants (RVs) in distant affected relatives can be used to identify variants causing a complex and heterogeneous disease. This approach tested whether single RVs were shared by all sequenced affected family members. However, as with other study designs, joint analysis of several RVs (e.g., within genes) is sometimes required to obtain sufficient statistical power. Further, phenocopies can lead to false negatives for some causal RVs if complete sharing among affected is required. Here, we extend our methodology (Rare Variant Sharing, RVS) to address these issues. Specifically, we introduce gene-based analyses, a partial sharing test based on RV sharing probabilities for subsets of affected relatives and a haplotype-based RV definition. RVS also has the desirable feature of not requiring external estimates of variant frequency or control samples, provides functionality to assess and address violations of key assumptions, and is available as open source software for genome-wide analysis. Simulations including phenocopies, based on the families of an oral cleft study, revealed the partial and complete sharing versions of RVS achieved similar statistical power compared with alternative methods (RareIBD and the Gene-Based Segregation Test), and had superior power compared with the pedigree Variant Annotation, Analysis, and Search Tool (pVAAST) linkage statistic. In studies of multiplex cleft families, analysis of rare single nucleotide variants in the exome of 151 affected relatives from 54 families revealed no significant excess sharing in any one gene, but highlighted different patterns of sharing revealed by the complete and partial sharing tests.

Whole exome sequencing analysis in severe chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD), one of the leading causes of death worldwide, is substantially influenced by genetic factors. Alpha-1 antitrypsin deficiency demonstrates that rare coding variants of large effect can influence COPD susceptibility. To identify additional rare coding variants in patients with severe COPD, we conducted whole exome sequencing analysis in 2543 subjects from two family-based studies (Boston Early-Onset COPD Study and International COPD Genetics Network) and one case-control study (COPDGene). Applying a gene-based segregation test in the family-based data, we identified significant segregation of rare loss of function variants in TBC1D10A and RFPL1 (P-value < 2x10-6), but were unable to find similar variants in the case-control study. In single-variant, gene-based and pathway association analyses, we were unable to find significant findings that replicated or were significant in meta-analysis. However, we found that the top results in the two datasets were in proximity to each other in the protein-protein interaction network (P-value = 0.014), suggesting enrichment of these results for similar biological processes. A network of these association results and their neighbors was significantly enriched in the transforming growth factor beta-receptor binding and cilia-related pathways. Finally, in a more detailed examination of candidate genes, we identified individuals with putative high-risk variants, including patients harboring homozygous mutations in genes associated with cutis laxa and Niemann-Pick Disease Type C. Our results likely reflect heterogeneity of genetic risk for COPD along with limitations of statistical power and functional annotation, and highlight the potential of network analysis to gain insight into genetic association studies.

Protective effect of club cell secretory protein (CC-16) on COPD risk and progression: a Mendelian randomisation study.

BACKGROUND: The anti-inflammatory pneumoprotein club cell secretory protein-16 (CC-16) is associated with the clinical expression of chronic obstructive pulmonary disease (COPD). We aimed to determine if there is a causal effect of serum CC-16 level on the risk of having COPD and/or its progression using Mendelian randomisation (MR) analysis. METHODS: We performed a genome-wide association meta-analysis for serum CC-16 in two COPD cohorts (Lung Health Study (LHS), n=3850 and ECLIPSE, n=1702). We then used the CC-16-associated single-nucleotide polymorphisms (SNPs) as instrumental variables in MR analysis to identify a causal effect of serum CC-16 on 'COPD risk' (ie, case status in the International COPD Genetics Consortium/UK-Biobank dataset; n=35 735 COPD cases, n=222 076 controls) and 'COPD progression' (ie, annual change in forced expiratory volume in 1 s in LHS and ECLIPSE). We also determined the associations between SNPs associated with CC-16 and gene expression using n=1111 lung tissue samples from the Lung Expression Quantitative Trait Locus Study. RESULTS: We identified seven SNPs independently associated (p<5×10-8) with serum CC-16 levels; six of these were novel. MR analysis suggested a protective causal effect of increased serum CC-16 on COPD risk (MR estimate (SE) -0.11 (0.04), p=0.008) and progression (LHS only, MR estimate (SE) 7.40 (3.28), p=0.02). Five of the SNPs were also associated with gene expression in lung tissue (at false discovery rate <0.1) of several genes, including the CC-16-encoding gene SCGB1A1. CONCLUSION: We have identified several novel genetic variants associated with serum CC-16 level in COPD cohorts. These genetic associations suggest a potential causal effect of serum CC-16 on the risk of having COPD and its progression, the biological basis of which warrants further investigation.