Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

JEM: A joint test to estimate the effect of multiple genetic variants on DNA methylation.

Multiple methods have been proposed to aggregate genetic variants in a gene or a region and jointly test their association with a trait of interest. However, these joint tests do not provide estimates of the individual effect of each variant. Moreover, few methods have evaluated the joint association of multiple variants with DNA methylation. We propose a method based on linear mixed models to estimate the joint and individual effect of multiple genetic variants on DNA methylation leveraging genomic annotations. Our approach is flexible, can incorporate covariates and annotation features, and takes into account relatedness and linkage disequilibrium (LD). Our method had correct Type-I error and overall high power for different simulated scenarios where we varied the number and specificity of functional annotations, number of causal and total genetic variants, frequency of genetic variants, LD, and genetic variant effect. Our method outperformed the family Sequence Kernel Association Test and had more stable estimations of effects than a classical single-variant linear mixed-effect model. Applied genome-wide to the Framingham Heart Study data, our method identified 921 DNA methylation sites influenced by at least one rare or low-frequency genetic variant located within 50 kilobases (kb) of the DNA methylation site.

Whole-Genome Sequencing Association Analyses of Stroke and Its Subtypes in Ancestrally Diverse Populations From Trans-Omics for Precision Medicine Project.

BACKGROUND AND PURPOSE: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). METHODS: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. RESULTS: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance (P<5.00×10-9) and 4 novel loci at genome-wide significance (P<5.00×10-8), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke (P<3.11×10-6). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2, HDAC9, ZFHX3, and LRCH1. CONCLUSIONS: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

Leveraging family history in genetic association analyses of binary traits.

BACKGROUND: Considering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS. RESULTS: In our simulation scenario of trait with modest T2D heritability (h2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10-8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits. CONCLUSIONS: Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Interactive effect between ATPase-related genes and early-life tobacco smoke exposure on bronchial hyper-responsiveness detected in asthma-ascertained families.

BACKGROUND: A positional cloning study of bronchial hyper-responsiveness (BHR) at the 17p11 locus in the French Epidemiological study on the Genetics and Environment of Asthma (EGEA) families showed significant interaction between early-life environmental tobacco smoke (ETS) exposure and genetic variants located in DNAH9. This gene encodes the heavy chain subunit of axonemal dynein, which is involved with ATP in the motile cilia function.Our goal was to identify genetic variants at other genes interacting with ETS in BHR by investigating all genes belonging to the 'ATP-binding' and 'ATPase activity' pathways which include DNAH9, are targets of cigarette smoke and play a crucial role in the airway inflammation. METHODS: Family-based interaction tests between ETS-exposed and unexposed BHR siblings were conducted in 388 EGEA families. Twenty single-nucleotide polymorphisms (SNP) showing interaction signals (p≤5.10-3) were tested in the 253 Saguenay-Lac-Saint-Jean (SLSJ) families. RESULTS: One of these SNPs was significantly replicated for interaction with ETS in SLSJ families (p=0.003). Another SNP reached the significance threshold after correction for multiple testing in the combined analysis of the two samples (p=10-5). Results were confirmed using both a robust log-linear test and a gene-based interaction test. CONCLUSION: The SNPs showing interaction with ETS belong to the ATP8A1 and ABCA1 genes, which play a role in the maintenance of asymmetry and homeostasis of lung membrane lipids.

Genome-wide interaction study of early-life smoking exposure on time-to-asthma onset in childhood.

BACKGROUND: Asthma, a heterogeneous disease with variable age of onset, results from the interplay between genetic and environmental factors. Early-life tobacco smoke (ELTS) exposure is a major asthma risk factor. Only a few genetic loci have been reported to interact with ELTS exposure in asthma. OBJECTIVE: Our aim was to identify new loci interacting with ELTS exposure on time-to-asthma onset (TAO) in childhood. METHODS: We conducted genome-wide interaction analyses of ELTS exposure on time-to-asthma onset in childhood in five European-ancestry studies (totalling 8273 subjects) using Cox proportional-hazard model. The results of all five genome-wide analyses were meta-analysed. RESULTS: The 13q21 locus showed genome-wide significant interaction with ELTS exposure (P = 4.3 × 10-8 for rs7334050 within KLHL1 with consistent results across the five studies). Suggestive interactions (P < 5 × 10-6 ) were found at three other loci: 20p12 (rs13037508 within MACROD2; P = 4.9 × 10-7 ), 14q22 (rs7493885 near NIN; P = 2.9 × 10-6 ) and 2p22 (rs232542 near CYP1B1; P = 4.1 × 10-6 ). Functional annotations and the literature showed that the lead SNPs at these four loci influence DNA methylation in the blood and are located nearby CpG sites reported to be associated with exposure to tobacco smoke components, which strongly support our findings. CONCLUSIONS AND CLINICAL RELEVANCE: We identified novel candidate genes interacting with ELTS exposure on time-to-asthma onset in childhood. These genes have plausible biological relevance related to tobacco smoke exposure. Further epigenetic and functional studies are needed to confirm these findings and to shed light on the underlying mechanisms.

A novel role for ciliary function in atopy: ADGRV1 and DNAH5 interactions.

BACKGROUND: Atopy, an endotype underlying allergic diseases, has a substantial genetic component. OBJECTIVE: Our goal was to identify novel genes associated with atopy in asthma-ascertained families. METHODS: We implemented a 3-step analysis strategy in 3 data sets: the Epidemiological Study on the Genetics and Environment of Asthma (EGEA) data set (1660 subjects), the Saguenay-Lac-Saint-Jean study data set (1138 subjects), and the Medical Research Council (MRC) data set (446 subjects). This strategy included a single nucleotide polymorphism (SNP) genome-wide association study (GWAS), the selection of related gene pairs based on statistical filtering of GWAS results, and text-mining filtering using Gene Relationships Across Implicated Loci and SNP-SNP interaction analysis of selected gene pairs. RESULTS: We identified the 5q14 locus, harboring the adhesion G protein-coupled receptor V1 (ADGRV1) gene, which showed genome-wide significant association with atopy (rs4916831, meta-analysis P value = 6.8 × 10-9). Statistical filtering of GWAS results followed by text-mining filtering revealed relationships between ADGRV1 and 3 genes showing suggestive association with atopy (P ≤ 10-4). SNP-SNP interaction analysis between ADGRV1 and these 3 genes showed significant interaction between ADGRV1 rs17554723 and 2 correlated SNPs (rs2134256 and rs1354187) within the dynein axonemal heavy chain 5 (DNAH5) gene (Pmeta-int = 3.6 × 10-5 and 6.1 × 10-5, which met the multiple-testing corrected threshold of 7.3 × 10-5). Further conditional analysis indicated that rs2134256 alone accounted for the interaction signal with rs17554723. CONCLUSION: Because both DNAH5 and ADGRV1 contribute to ciliary function, this study suggests that ciliary dysfunction might represent a novel mechanism underlying atopy. Combining GWAS and epistasis analysis driven by statistical and knowledge-based evidence represents a promising approach for identifying new genes involved in complex traits.

SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network.

MOTIVATION: Apart from single marker-based tests classically used in genome-wide association studies (GWAS), network-assisted analysis has become a promising approach to identify a set of genes associated with disease. To date, most network-assisted methods aim at finding genes connected in a background network, whatever the density or strength of their connections. This can hamper the findings as sparse connections are non-robust against noise from either the GWAS results or the network resource. RESULTS: We present SigMod, a novel and efficient method integrating GWAS results and gene network to identify a strongly interconnected gene module enriched in high association signals. Our method is formulated as a binary quadratic optimization problem, which can be solved exactly through graph min-cut algorithms. Compared to existing methods, SigMod has several desirable properties: (i) edge weights quantifying confidence of connections between genes are taken into account, (ii) the selection path can be computed rapidly, (iii) the identified gene module is strongly interconnected, hence includes genes of high functional relevance, and (iv) the method is robust against noise from either the GWAS results or the network resource. We applied SigMod to both simulated and real data. It was found to outperform state-of-the-art network-assisted methods in identifying disease-associated genes. When SigMod was applied to childhood-onset asthma GWAS results, it successfully identified a gene module enriched in consistently high association signals and made of functionally related genes that are biologically relevant for asthma. AVAILABILITY AND IMPLEMENTATION: An R package SigMod is available at: https://github.com/YuanlongLiu/SigMod. CONTACT: yuanlong.liu@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Investigation of parent-of-origin effects induced by fenofibrate treatment on triglycerides levels.

BACKGROUND: Genome-wide association studies performed on triglycerides (TGs) have not accounted for epigenetic mechanisms that may partially explain trait heritability. RESULTS: Parent-of-origin (POO) effect association analyses using an agnostic approach or a candidate approach were performed for pretreatment TG levels, posttreatment TG levels, and pre- and posttreatment TG-level differences in the real GAW20 family data set. We detected 22 genetic variants with suggestive POO effects with at least 1 phenotype (P ≤ 10- 5). We evaluated the association of these 22 significant genetic variants showing POO effects with close DNA methylation probes associated with TGs. A total of 18 DNA methylation probes located in the vicinity of the 22 SNPs were associated with at least 1 phenotype and 6 SNP-probe pairs were associated with DNA methylation probes at the nominal level of P < 0.05, among which 1 pair presented evidence of POO effect. Our analyses identified a paternal effect of SNP rs301621 on the difference between pre- and posttreatment TG levels (P = 1.2 × 10- 5). This same SNP showed evidence for a maternal effect on methylation levels of a nearby probe (cg10206250; P = 0.01). Using a causal inference test we established that the observed POO effect of rs301621 was not mediated by DNA methylation at cg10206250. CONCLUSIONS: We performed POO effect association analyses of SNPs with TGs, as well as association analyses of SNPs with DNA methylation probes. These analyses, which were followed by a causal inference test, established that the paternal effect at the SNP rs301621 is induced by treatment and is not mediated by methylation level at cg10206250.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20.

BACKGROUND: The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods. RESULTS: Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis. CONCLUSIONS: A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

Do changes in DNA methylation mediate or interact with SNP variation? A pharmacoepigenetic analysis.

BACKGROUND: In studies with multi-omics data available, there is an opportunity to investigate interdependent mechanisms of biological causality. The GAW20 data set includes both DNA genotype and methylation measures before and after fenofibrate treatment. Using change in triglyceride (TG) levels pre- to posttreatment as outcome, we present a mediation analysis that incorporates methylation. This approach allows us to simultaneously consider a mediation hypothesis that genotype affects change in TG level by means of its effect on methylation, and an interaction hypothesis that the effect of change in methylation on change in TG levels differs by genotype. We select 322 single-nucleotide polymorphism-cytosine-phosphate-guanine (SNP-CpG) site pairs for mediation analysis on the basis of proximity and marginal genome-wide association study (GWAS) and epigenome-wide association study (EWAS) significance, and present results from the real-data sample of 407 individuals with complete genotype, methylation, TG levels, and covariate data. RESULTS: We identified 3 SNP-CpG site pairs with significant interaction effects at a Bonferroni-corrected significance threshold of 1.55E-4. None of the analyzed sites showed significant evidence of mediation. Power analysis by simulation showed that a sample size of at least 19,500 is needed to detect nominally significant indirect effects with true effect sizes equal to the point estimates at the locus with strongest evidence of mediation. CONCLUSIONS: These results suggest that there is stronger evidence for interaction between genotype and methylation on change in triglycerides than for methylation mediating the effect of genotype.

Comparison of novel and existing methods for detecting differentially methylated regions.

BACKGROUND: Single-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring probes. Methods to detect differentially methylated regions (DMRs) (clusters of neighboring probes associated with a phenotype) may provide more power to detect associations between DNA methylation and diseases or phenotypes of interest. RESULTS: We proposed a novel approach, GlobalP, and perform comparisons with 3 methods-DMRcate, Bumphunter, and comb-p-to identify DMRs associated with log triglycerides (TGs) in real GAW20 data before and after fenofibrate treatment. We applied these methods to the summary statistics from an EWAS performed on the methylation data. Comb-p, DMRcate, and GlobalP detected very similar DMRs near the gene CPT1A on chromosome 11 in both the pre- and posttreatment data. In addition, GlobalP detected 2 DMRs before fenofibrate treatment in the genes ETV6 and ABCG1. Bumphunter identified several DMRs on chromosomes 1 and 20, which did not overlap with DMRs detected by other methods. CONCLUSIONS: Our novel method detected the same DMR identified by two existing methods and detected two additional DMRs not identified by any of the existing methods we compared.

Impact of Genetic Determinants of HbA1c on Type 2 Diabetes Risk and Diagnosis.

PURPOSE OF REVIEW: Glycated hemoglobin (A1c) is used to diagnose type 2 diabetes and monitor glycemic control. Specific genetic variants interfere with A1c and effects/frequencies of some variants vary by ancestry. In this review, we summarize findings from large trans-ethnic meta-analyses of genome-wide association studies (GWAS) of A1c and describe some variants influencing erythrocyte biology and interfering with A1c. RECENT FINDINGS: Recent GWAS meta-analyses have revealed 60 loci associated with A1c in multi-ethnic populations. The main A1c genetic driver in African Americans is rs1050828 (G6PD). Some identified loci are located in/near genes known as monogenic causes of erythrocytic disorders (ANK1, SPTA1) or iron disorders (TMPRSS6, HFE). Uncommon genetic variants (not revealed by GWAS) that are known to cause hemoglobinopathies may also influence A1C levels, partly by interfering with laboratory assays. Specific genetic variants that have a large impact on A1c levels may influence clinical practice, especially in individuals of African descent. Efforts to reveal novel A1c loci should focus on increasing representation of GWAS in non-European ancestries, and on using better genome-wide coverage of uncommon variants that are specific to each population.

DNA methylation within melatonin receptor 1A (MTNR1A) mediates paternally transmitted genetic variant effect on asthma plus rhinitis.

BACKGROUND: Asthma and allergic rhinitis (AR) are common allergic comorbidities with a strong genetic component in which epigenetic mechanisms might be involved. OBJECTIVE: We aimed to identify novel risk loci for asthma and AR while accounting for parent-of-origin effect. METHODS: We performed a series of genetic analyses, taking into account the parent-of-origin effect in families ascertained through asthma: (1) genome-wide linkage scan of asthma and AR in 615 European families, (2) association analysis with 1233 single nucleotide polymorphisms (SNPs) covering the significant linkage region in 162 French Epidemiological Study on the Genetics and Environment of Asthma families with replication in 154 Canadian Saguenay-Lac-Saint-Jean asthma study families, and (3) association analysis of disease and significant SNPs with DNA methylation (DNAm) at CpG sites in 40 Saguenay-Lac-Saint-Jean asthma study families. RESULTS: We detected a significant paternal linkage of the 4q35 region to asthma and allergic rhinitis comorbidity (AAR; P = 7.2 × 10(-5)). Association analysis in this region showed strong evidence for the effect of the paternally inherited G allele of rs10009104 on AAR (P = 1.1 × 10(-5), reaching the multiple-testing corrected threshold). This paternally inherited allele was also significantly associated with DNAm levels at the cg02303933 site (P = 1.7 × 10(-4)). Differential DNAm at this site was found to mediate the identified SNP-AAR association. CONCLUSION: By integrating genetic and epigenetic data, we identified that a differentially methylated CpG site within the melatonin receptor 1A (MTNR1A) gene mediates the effect of a paternally transmitted genetic variant on the comorbidity of asthma and AR. This study provides a novel insight into the role of epigenetic mechanisms in patients with allergic respiratory diseases.

Identification of a new locus at 16q12 associated with time to asthma onset.

BACKGROUND: Asthma is a heterogeneous disease in which age of onset plays an important role. OBJECTIVE: We sought to identify the genetic variants associated with time to asthma onset (TAO). METHODS: We conducted a large-scale meta-analysis of 9 genome-wide association studies of TAO (total of 5462 asthmatic patients with a broad range of age of asthma onset and 8424 control subjects of European ancestry) performed by using survival analysis techniques. RESULTS: We detected 5 regions associated with TAO at the genome-wide significant level (P < 5 × 10-8). We evidenced a new locus in the 16q12 region (near cylindromatosis turban tumor syndrome gene [CYLD]) and confirmed 4 asthma risk regions: 2q12 (IL-1 receptor-like 1 [IL1RL1]), 6p21 (HLA-DQA1), 9p24 (IL33), and 17q12-q21 (zona pellucida binding protein 2 [ZPBP2]-gasdermin A [GSDMA]). Conditional analyses identified 2 distinct signals at 9p24 (both upstream of IL33) and 17q12-q21 (near ZPBP2 and within GSDMA). Together, these 7 distinct loci explained 6.0% of the variance in TAO. In addition, we showed that genetic variants at 9p24 and 17q12-q21 were strongly associated with an earlier onset of childhood asthma (P ≤ .002), whereas the 16q12 single nucleotide polymorphism was associated with later asthma onset (P = .04). A high burden of disease risk alleles at these loci was associated with earlier age of asthma onset (4 vs 9-12 years, P = 10-4). CONCLUSION: The new susceptibility region for TAO at 16q12 harbors variants that correlate with the expression of CYLD and nucleotide-binding oligomerization domain 2 (NOD2), 2 strong candidates for asthma. This study demonstrates that incorporating the variability of age of asthma onset in asthma modeling is a helpful approach in the search for disease susceptibility genes.

Interaction between the DNAH9 gene and early smoke exposure in bronchial hyperresponsiveness.

A previous genome-wide linkage scan of bronchial hyperresponsiveness (BHR) in the French Epidemiological study on the Genetics and Environment of Asthma (EGEA) families, performed in the presence of a gene×early-life environmental tobacco smoke (ETS) exposure interaction, showed the strongest interaction in the 17p11 region where linkage was detected only among unexposed siblings. Our goal was to conduct fine-scale mapping of 17p11 to identify single nucleotide polymorphisms (SNPs) interacting with ETS that influence BHR.Analyses were performed in 388 French EGEA asthmatic families, using a two-step strategy: 1) selection of SNPs displaying family-based association test (FBAT) association signals (p≤0.01) with BHR in unexposed siblings, and 2) a FBAT homogeneity test between exposed and unexposed siblings plus a robust log-linear interaction test.A single SNP reached the threshold (p≤3×10(-3)) for significant interaction with ETS using both interaction tests, after accounting for multiple testing. Results were replicated in 253 French-Canadian families, but not in 341 UK families, probably due in part to differences in phenotypic features between datasets.The SNP showing significant interaction with ETS belongs toDNAH9(dynein, axonemal, heavy chain 9), a promising candidate gene involved in respiratory cilia mobility and associated with primary ciliary dyskinesia, a disease associated with abnormalities of pulmonary function.

The nuclear factor I/A (NFIA) gene is associated with the asthma plus rhinitis phenotype.

BACKGROUND: A previous genome-wide linkage scan in 295 families of the French Epidemiological Study on the Genetics and Environment of Asthma (EGEA) showed strong evidence of linkage of the 1p31 region to the combined asthma plus allergic rhinitis (AR) phenotype. OBJECTIVE: Our purpose was to conduct fine-scale mapping of the 1p31 linkage region to identify the genetic variants associated with asthma plus AR. METHODS: Association analyses with the asthma plus rhinitis phenotype were first conducted in the EGEA family sample using the family-based association method (FBAT) and logistic regression. The test of homogeneity of association between asthma plus AR versus asthma alone or AR alone was also applied. Replication of EGEA findings was sought in French-Canadian and United Kingdom family samples. RESULTS: We found a significant association between asthma plus rhinitis and a 1p31 genetic variant (P = 2 × 10(-5) for rs12122228, which reached the multiple testing-corrected threshold) in EGEA using FBAT. There was evidence of heterogeneity of association between asthma plus AR versus asthma alone or AR alone (P = .03). A Meta-analysis of FBAT results from EGEA and French-Canadian families improved evidence for both association and heterogeneity (P = 5 × 10(-6) and P = .008, respectively), whereas a meta-analysis of EGEA, French-Canadian, and United Kingdom samples based on logistic regression slightly increased the evidence for heterogeneity. CONCLUSION: The single nucleotide polymorphism specifically associated to asthma plus rhinitis is located in the flanking 5' untranslated region of the nuclear factor I/A (NFIA) gene, a strong candidate gene for asthma and AR.