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.

Strategies for phasing and imputation in a population isolate.

In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies. We simulated 1,115,604 variants on chromosome 10 for 477 members of the large complex pedigree of Campora, a village within the established isolate of Cilento in southern Italy. We assessed the phasing performance of identical by descent based software ALPHAPHASE and SLRP, LD-based software SHAPEIT2, SHAPEIT3, and BEAGLE, and new software EAGLE that combines both methodologies. For imputation we compared IMPUTE2, IMPUTE4, MINIMAC3, BEAGLE, and new software PBWT. Genotyping errors and missing genotypes were simulated to observe their effects on the performance of each software. Highly accurate phased data were achieved by all software with SHAPEIT2, SHAPEIT3, and EAGLE2 providing the most accurate results. MINIMAC3, IMPUTE4, and IMPUTE2 all performed strongly as imputation software and our study highlights the considerable gain in imputation accuracy provided by a genome sequenced reference panel specific to the population isolate.

Variation in worldwide incidence of amyotrophic lateral sclerosis: a meta-analysis.

Background: To assess the worldwide variation of amyotrophic lateral sclerosis (ALS) incidence, we performed a systematic review and meta-analysis of population-based data published to date. Methods: We reviewed Medline and Embase up to June 2015 and included all population-based studies of newly diagnosed ALS cases, using multiple sources for case ascertainment. ALS crude and standardized incidence (on age and sex using the US 2010 population) were calculated. Random effect meta-analysis and meta-regression were performed using the subcontinent as the main study level covariate. Sources of heterogeneity related to the characteristics of the study population and the study methodology were investigated. Results: Among 3216 records, 44 studies were selected, covering 45 geographical areas in 11 sub-continents. A total of 13 146 ALS cases and 825 million person-years of follow-up (PYFU) were co-nsidered. The overall pooled worldwide crude ALS incidence was at 1.75 (1.55-1.96)/100 000 PYFU; 1.68 (1.50-1.85)/100 000 PYFU after standardization. Heterogeneity was identified in ALS standardized incidence between North Europe [1.89 (1.46-2.32)/100 000 PYFU] and East Asia [0.83 (0.42-1.24)/100 000 PYFU, China and Japan P = 0.001] or South Asia [0.73 (0.58-0.89)/100 000/PYFU Iran, P = 0.02]. Conversely, homogeneous rates have been reported in populations from Europe, North America and New Zealand [pooled ALS standardized incidence of 1.81 (1.66-1.97)/100 000 PYFU for those areas]. Conclusion: This review confirms a heterogeneous distribution worldwide of ALS, and sets the scene to sustain a collaborative study involving a wide international consortium to investigate the link between ancestry, environment and ALS incidence.

Clinical and demographic factors and outcome of amyotrophic lateral sclerosis in relation to population ancestral origin.

BACKGROUND: To review how the phenotype and outcome of amyotrophic lateral sclerosis (ALS) change with variations in population ancestral origin (PAO). Knowledge of how PAO modifies ALS phenotype may provide important insight into the risk factors and pathogenic mechanisms of the disease. METHODS: We performed a systematic review and meta-analysis of the literature concerning differences in phenotype and outcome of ALS that relate to PAO. RESULTS: A review of 3111 records identified 78 population-based studies. The 40 that were included covered 40 geographical areas in 10 subcontinents. Around 12,700 ALS cases were considered. The results highlight the phenotypic heterogeneity of ALS at time of onset [age, sex ratio (SR), bulbar onset], age at diagnosis, occurrence of comorbidities in the first year after diagnosis, and outcome (survival). Subcontinent is a major explanatory factor for the variability of the ALS phenotype in population-based studies. Some markers of ALS phenotype were homogeneously distributed in western countries (SR, mean age at onset/diagnosis) but their distributions in other subcontinents were remarkably different. Other markers presented variations in European subcontinents (familial ALS, bulbar onset) and in other continents. As a consequence, ALS outcome strongly varied, with a median survival time from onset ranging from 24 months (Northern Europe) to 48 months (Central Asia). DISCUSSION: This review sets the scene for a collaborative study involving a wide international consortium to investigate, using a standard methodology, the link between ancestry, environment, and ALS phenotype.

Can whole-exome sequencing data be used for linkage analysis?

Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage of the genome and the lack of knowledge on the power of this strategy have largely precluded its use so far. To compare the performance of linkage analysis conducted with WES and SNP chip data in different situations, we performed simulations on two pedigree structures with, respectively, a dominant and a recessive trait segregating. We found that the performance of the two sets of markers at excluding regions of the genome were very similar, and there was no real gain at using SNP chip data compared with using the common SNPs extracted from WES data. When analyzing the real WES data available for these two pedigrees, we found that the linkage information derived from the WES common polymorphisms was able to reduce by half the list of candidate variants identified by a simple filtering approach. Conducting linkage analysis with WES data available on pedigrees and excluding among the candidate variants those that fall in excluded linkage regions is thus a powerful and cost-effective strategy to reduce the number of false-positive candidate variants.

High level of inbreeding in final phase of 1000 Genomes Project.

The 1000 Genomes Project provides a unique source of whole genome sequencing data for studies of human population genetics and human diseases. The last release of this project includes more than 2,500 sequenced individuals from 26 populations. Although relationships among individuals have been investigated in some of the populations, inbreeding has never been studied. In this article, we estimated the genomic inbreeding coefficient of each individual and found an unexpected high level of inbreeding in 1000 Genomes data: nearly a quarter of the individuals were inbred and around 4% of them had inbreeding coefficients similar or greater than the ones expected for first-cousin offspring. Inbred individuals were found in each of the 26 populations, with some populations showing proportions of inbred individuals above 50%. We also detected 227 previously unreported pairs of close relatives (up to and including first-cousins). Thus, we propose subsets of unrelated and outbred individuals, for use by the scientific community. In addition, because admixed populations are present in the 1000 Genomes Project, we performed simulations to study the robustness of inbreeding coefficient estimates in the presence of admixture. We found that our multi-point approach (FSuite) was quite robust to admixture, unlike single-point methods (PLINK).

A New Correction for Multiple Testing in Gene-Gene Interaction Studies.

A major problem in gene-gene interaction studies in large marker panels is how to correct for multiple testing while accounting for the dependence between marker pairs due to the presence of linkage disequilibrium. The "gold standard" approach is to perform permutations of case/control labels. However, this is often not feasible in practice, due to computational demands. Here, we propose a correction based on the effective number of independent tests of interaction between marker pairs. This number depends on the effective number of independent single-marker tests. We tested its validity using simulated samples, as well as that of another correction of marker pair tests. We showed that our approach was valid while the other correction strongly underestimated the effective number of independent tests. Our method provides estimates of the effective number of independent tests close to those reported in the literature for a Genome-Wide Interaction Study on a 550K chip. Our correction method is quick and simple, and can be applied whatever the marker panel and the underlying linkage disequilibrium pattern.

Homozygous STIL mutation causes holoprosencephaly and microcephaly in two siblings.

Holoprosencephaly (HPE) is a frequent congenital malformation of the brain characterized by impaired forebrain cleavage and midline facial anomalies. Heterozygous mutations in 14 genes have been identified in HPE patients that account for only 30% of HPE cases, suggesting the existence of other HPE genes. Data from homozygosity mapping and whole-exome sequencing in a consanguineous Turkish family were combined to identify a homozygous missense mutation (c.2150G>A; p.Gly717Glu) in STIL, common to the two affected children. STIL has a role in centriole formation and has previously been described in rare cases of microcephaly. Rescue experiments in U2OS cells showed that the STIL p.Gly717Glu mutation was not able to fully restore the centriole duplication failure following depletion of endogenous STIL protein indicating the deleterious role of the mutation. In situ hybridization experiments using chick embryos demonstrated that expression of Stil was in accordance with a function during early patterning of the forebrain. It is only the second time that a STIL homozygous mutation causing a recessive form of HPE was reported. This result also supports the genetic heterogeneity of HPE and increases the panel of genes to be tested for HPE diagnosis.

Genetic variants in DNA repair pathways and risk of upper aerodigestive tract cancers: combined analysis of data from two genome-wide association studies in European populations.

DNA repair pathways are good candidates for upper aerodigestive tract cancer susceptibility because of their critical role in maintaining genome integrity. We have selected 13 pathways involved in DNA repair representing 212 autosomal genes. To assess the role of these pathways and their associated genes, two European data sets from the International Head and Neck Cancer Epidemiology consortium were pooled, totaling 1954 cases and 3121 controls, with documented demographic, lifetime alcohol and tobacco consumption information. We applied an innovative approach that tests single nucleotide polymorphism (SNP)-sets within DNA repair pathways and then within genes belonging to the significant pathways. We showed an association between the polymerase pathway and oral cavity/pharynx cancers (P-corrected = 4.45 × 10(-) (2)), explained entirely by the association with one SNP, rs1494961 (P = 2.65 × 10(-) (4)), a missense mutation V306I in the second exon of HELQ gene. We also found an association between the cell cycle regulation pathway and esophagus cancer (P-corrected = 1.48 × 10(-) (2)), explained by three SNPs located within or near CSNK1E gene: rs1534891 (P = 1.27 × 10(-) (4)), rs7289981 (P = 3.37 × 10(-) (3)) and rs13054361 (P = 4.09 × 10(-) (3)). As a first attempt to investigate pathway-level associations, our results suggest a role of specific DNA repair genes/pathways in specific upper aerodigestive tract cancer sites.

FSuite: exploiting inbreeding in dense SNP chip and exome data.

UNLABELLED: FSuite is a user-friendly pipeline developed for exploiting inbreeding information derived from human genomic data. It can make use of single nucleotide polymorphism chip or exome data. Compared with other software, the advantage of FSuite is that it provides a complete suite of scripts to describe and use the inbreeding information. It includes a module to detect inbred individuals and estimate their inbreeding coefficient, a module to describe the proportion of different mating types in the population and the individual probability to be offspring of different mating types that can be useful for population genetic studies. It also allows the identification of shared regions of homozygosity between affected individuals (homozygosity mapping) that can be used to identify rare recessive mutations involved in monogenic or multifactorial diseases. AVAILABILITY AND IMPLEMENTATION: FSuite is developed in Perl and uses R functions to generate graphical outputs. This pipeline is freely available under GNU GPL license at: http://genestat.cephb.fr/software/index.php/FSuite.

Rare and low frequency variant stratification in the UK population: description and impact on association tests.

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.

Comparative power of family-based association strategies to detect disease-causing variants under two-locus models.

Not accounting for interaction in association analyses may reduce the power to detect the variants involved. We investigate the powers of different designs to detect under two-locus models the effect of disease-causing variants among several hundreds of markers using family-based association tests by simulation. This setting reflects realistic situations of exploration of linkage regions or of biological pathways. We define four strategies: (S1) single-marker analysis of all Single Nucleotide Polymorphisms (SNPs), (S2) two-marker analysis of all possible SNPs pairs, (S3) lax preliminary selection of SNPs followed by a two-marker analysis of all selected SNP pairs, (S4) stringent preliminary selection of SNPs, each being later paired with all the SNPs for two-marker analysis. Strategy S2 is never the best design, except when there is an inversion of the gene effect (flip-flop model). Testing individual SNPs (S1) is the most efficient when the two genes act multiplicatively. Designs S3 and S4 are the most powerful for nonmultiplicative models. Their respective powers depend on the level of symmetry of the model. Because the true genetic model is unknown, we cannot conclude that one design outperforms another. The optimal approach would be the two-step strategy (S3 or S4) as it is often the most powerful, or the second best.

Lung cancer and DNA repair genes: multilevel association analysis from the International Lung Cancer Consortium.

Lung cancer (LC) is the leading cause of cancer-related death worldwide and tobacco smoking is the major associated risk factor. DNA repair is an important process, maintaining genome integrity and polymorphisms in DNA repair genes may contribute to susceptibility to LC. To explore the role of DNA repair genes in LC, we conducted a multilevel association study with 1655 single nucleotide polymorphisms (SNPs) in 211 DNA repair genes using 6911 individuals pooled from four genome-wide case-control studies. Single SNP association corroborates previous reports of association with rs3131379, located on the gene MSH5 (P = 3.57 × 10-5) and returns a similar risk estimate. The effect of this SNP is modulated by histological subtype. On the log-additive scale, the odds ratio per allele is 1.04 (0.84-1.30) for adenocarcinomas, 1.52 (1.28-1.80) for squamous cell carcinomas and 1.31 (1.09-1.57) for other histologies (heterogeneity test: P = 9.1 × 10(-)(3)). Gene-based association analysis identifies three repair genes associated with LC (P < 0.01): UBE2N, structural maintenance of chromosomes 1L2 and POLB. Two additional genes (RAD52 and POLN) are borderline significant. Pathway-based association analysis identifies five repair pathways associated with LC (P < 0.01): chromatin structure, DNA polymerases, homologous recombination, genes involved in human diseases with sensitivity to DNA-damaging agents and Rad6 pathway and ubiquitination. This first international pooled analysis of a large dataset unravels the role of specific DNA repair pathways in LC and highlights the importance of accounting for gene and pathway effects when studying LC.

Could inbred cases identified in GWAS data succeed in detecting rare recessive variants where affected sib-pairs have failed?

To detect fully penetrant rare recessive variants that could constitute Mendelian subentities of complex diseases, we propose a novel strategy, the HBD-GWAS strategy, which can be applied to genome-wide association study (GWAS) data. This strategy first involves the identification of inbred individuals among cases using the genome-wide SNP data and then focuses on these inbred affected individuals and searches for genomic regions of shared homozygosity by descent that could harbor rare recessive disease-causing variants. In this second step, analogous to homozygosity mapping, a heterogeneity lod-score, HFLOD, is computed to quantify the evidence of linkage provided by the data. In this paper, we evaluate this strategy theoretically under different scenarios and compare its performances with those of linkage analysis using affected sib-pair (ASP) data. If cases affected by these Mendelian subentities are not enriched in the sample of cases, the HBD-GWAS strategy has almost no power to detect them, unless they explain an important part of the disease prevalence. The HBD-GWAS strategy outperforms the ASP linkage strategy only in a very limited number of situations where there exists a strong allelic heterogeneity. When several rare recessive variants within the same gene are involved, the ASP design indeed often fails to detect the gene, whereas, by focusing on inbred individuals using the HBD-GWAS strategy, the gene might be detected provided very large samples of cases are available.

Determination of the real effect of genes identified in GWAS: the example of IL2RA in multiple sclerosis.

Genome-wide association studies (GWAS), although efficient to detect genes involved in complex diseases, are not designed to measure the real effect of the genes. This is illustrated here by the example of IL2RA in multiple sclerosis (MS). Association between IL2RA and MS is clearly established, although the functional variation is still unknown: the effect of IL2RA might be better described by several SNPs than by a single one. This study investigates whether a pair of SNPs better explains the observed linkage and association data than a single SNP. In total, 522 trio families and 244 affected sib-pairs were typed for 26 IL2RA SNPs. For each SNP and pairs of SNPs, the phased genotypes of patients and controls were compared to determine the SNP set offering the best risk discrimination. Consistency between the genotype risks provided by the retained set and the identical by descent allele sharing in affected sib-pairs was assessed. After controlling for multiple testing, the set of SNPs rs2256774 and rs3118470, provides the best discrimination between the case and control genotype distributions (P-corrected=0.009). The relative risk between the least and most at-risk genotypes is 3.54 with a 95% confidence interval of [2.14-5.94]. Furthermore, the linkage information provided by the allele sharing between affected sibs is consistent with the retained set (P=0.80) but rejects the SNP reported in the literature (P=0.006). Establishing a valid modeling of a disease gene is essential to test its potential interaction with other genes and to reconstruct the pathophysiological pathways.

Genetic association and gene-environment interaction: a new method for overcoming the lack of exposure information in controls.

The use of a reference control panel in genome-wide association studies is an interesting solution to the problem of how to reduce costs. In such designs, data on relevant environmental factors are usually collected only in cases, making it more difficult to deal with potential gene-environment interactions when testing for genetic association. However, under certain circumstances, neglecting an existing interaction with the environment may be detrimental in terms of statistical power to detect the genetic factor. In this paper, the authors propose a novel method based on a multinomial logistic regression model to overcome the lack of environmental exposure information in controls, by contrasting both exposed and unexposed cases with the control sample. For each case group, a genetic effect-size parameter is estimated, and the genetic association and the gene-environment interaction are tested jointly. The authors evaluate the performance of this method through asymptotic computations and simulations of cases and population controls under different models. In the presence of a gene-environment interaction, this approach outperforms other available methods that test for genetic association and gene-environment interaction either separately or jointly. Interestingly, it even has better power than the joint test requiring full knowledge of the environmental information in both cases and controls.

Meta-analysis of genome-wide linkage studies in celiac disease.

OBJECTIVE: A meta-analysis of genome-wide linkage studies allows us to summarize the extensive information available from family-based studies, as the field moves into genome-wide association studies. METHODS: Here we apply the genome scan meta-analysis (GSMA) method, a rank-based, model-free approach, to combine results across eight independent genome-wide linkages performed on celiac disease (CD), including 554 families with over 1,500 affected individuals. We also investigate the agreement between signals we identified from this meta-analysis of linkage studies and those identified from genome-wide association analysis using a hypergeometric distribution. RESULTS: Not surprisingly, the most significant result was obtained in the HLA region. Outside the HLA region, suggestive evidence for linkage was obtained at the telomeric region of chromosome 10 (10q26.12-qter; p = 0.00366), and on chromosome 8 (8q22.2-q24.21; p = 0.00491). Testing signals of association and linkage within bins showed no significant evidence for co-localization of results. CONCLUSION: This meta-analysis allowed us to pool the results from available genome-wide linkage studies and to identify novel regions potentially harboring predisposing genetic variation contributing to CD. This study also shows that linkage and association studies may identify different types of disease-predisposing variants.

The ordered transmission disequilibrium test: detection of modifier genes.

We consider the problem of detection of modifier genes that lead to variations in a disease-related continuous variable (DRCV), such as the age of onset or a measure of disease severity, in a strategy of candidate genes. We propose a novel method, the ordered transmission disequilibrium test (OTDT), to test for a relation between the clinical heterogeneity expressed by a DRCV and marker genotypes of a candidate gene. The OTDT applies to trio families with one patients and his parents, all three genotyped at a bi-allelic marker M. The OTDT aims to find a critical value of the DRCV which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions and compare it with a linear regression analysis.

IFIH1-GCA-KCNH7 locus is not associated with genetic susceptibility to multiple sclerosis in French patients.

A recent investigation reported, for the first time, an association between variants in the IFIH1-GCA-KCNH7 locus and multiple sclerosis (MS). We sought to replicate this genetic association in MS with a new independent MS cohort composed of French Caucasian MS trio families. The two most significant IFIH1 single nucleotide polymorphisms, rs1990760 and rs2068330, reported as involved in MS susceptibility, were genotyped in 591 French Caucasian MS trio families, and analyzed using the transmission/disequilibrium test. No association with MS was found (rs1990760, P=0.45 and rs2068330, P=0.27). Similarly, no significant association was detected after stratification for HLA-DRB1*1501 carriers. Reasons that may explain this discrepancy between the original report and our study are discussed.

Linkage analyses of rheumatoid arthritis and related quantitative phenotypes: the GAW15 experience.

The group that formed on the theme of linkage analyses of rheumatoid arthritis RA and related phenotypes (Group 10) in the Genetic Analysis Workshop 15 comprised 18 sets of investigators. Two data sets were available: one was a real set provided by the North American Rheumatoid Arthritis Consortium and collaborators in Canada, France (European Consortium Of Rheumatoid Arthritis Families) and the UK; the other was a simulated data set modelled after the real data set. Whereas a majority of the investigators analyzed the RA affection status as a binary phenotype, a few contributions considered data on correlated quantitative traits such as anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M. The different investigators applied a wide spectrum of linkage methods. As expected, most methods could identify the human leukocyfeantigen region on chromosome 6 as a major genetic factor for RA. In addition, some novel chromosomal regions provided significant evidence of linkage in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect linkage.