Search

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and population structure. Although France occupies a particular location at the western part of Europe and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2184 individuals born in France who were enrolled in two independent population cohorts. Using FineSTRUCTURE, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters correspond closely to geographic, historical, and linguistic divisions of France, and contain different proportions of ancestry from Stone and Bronze Age populations. By modeling the relationship between genetics and geography using EEMS, we were able to detect gene flow barriers that are similar across the two cohorts and correspond to major rivers and mountain ranges. Estimations of effective population sizes also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the 14 th century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in genetic studies of Europe that will be useful to medical geneticists, historians, and archeologists.

About 10 years ago, after the first large-scale genome-wide association studies (GWAS) were conducted to find genes associated with common complex diseases, investigators were surprised to find that the amount of heritability explained by the significant hits was very low for almost all the studied traits. Indeed, when compared to heritability estimates expected from the observed trait concordance within families, the heritability explained by the associated variants was always much smaller, more than ten times smaller for some traits. There was thus a problem of "missing heritability" and different hypotheses were proposed to help find this "missing heritability". These hypotheses involved novel research strategies in which different groups engaged including among others increasing sample sizes of GWAS or looking for rare variants and structural variations that were not captured by the SNP-chips used in GWAS. How successful have these efforts been in finding the "missing heritability"? Could it be that the problem of "missing heritability" was ill-defined? These are the questions that will be addressed in this paper by taking some different examples of complex traits.

Background: Neuropathic pain following surgery could be a useful model for the study of the genetic mechanisms of peripheral neuropathic pain., Objective: The aim of this study was to identify genetic predictors of persistent postsurgical neuropathic pain., Design: An ancillary study from a prospective cohort., Setting: Eighteen French university hospitals., Patients: Five hundred and sixty-one patients at risk of persistent postoperative pain who underwent scheduled surgery were classified as 159 cases and 402 controls., Intervention: Pre-operative blood sampling for DNA analysis and questionnaires sent at the third and sixth month after surgery., Main Outcome Measures: The phenotype was the report of pain at the site of surgery with a positive response in the DN4 questionnaire within 6 months after surgery. Out of a list of 126 candidate genes involved in the initial processes of peripheral neuropathic pain, a set of 4599 single nucleotide polymorphisms was tested on an Illumina chip. We carried out the association tests, based on an additive model, on 4422 single nucleotide polymorphisms., Results: After correcting for type-I error inflation, only one suggestive association was reached for one single nucleotide polymorphism, the rs2286614, which we had selected to tag KCNK4. This gene encodes for TRAAK, a two-pore domain background K channel involved in the modulation of the primary thermoreceptors of the transient receptor potential channels family., Conclusion: This is the first genetic association study specifically investigating the occurrence of persistent postsurgical neuropathic pain. Its results help target future research to better understand the mechanisms of peripheral neuropathic pain., Trial Registration: ClinicalTrials.gov (ref. NCT00812734).

Marfan syndrome (MFS) is a rare autosomal dominant connective tissue disorder related to variants in the FBN1 gene. Prognosis is related to aortic risk of dissection following aneurysm. MFS clinical variability is notable, for age of onset as well as severity and number of clinical manifestations. To identify genetic modifiers, we combined genome-wide approaches in 1070 clinically well-characterized FBN1 disease-causing variant carriers: (1) an FBN1 eQTL analysis in 80 fibroblasts of FBN1 stop variant carriers, (2) a linkage analysis, (3) a kinship matrix association study in 14 clinically concordant and discordant sib-pairs, (4) a genome-wide association study and (5) a whole exome sequencing in 98 extreme phenotype samples.Three genetic mechanisms of variability were found. A new genotype/phenotype correlation with an excess of loss-of-cysteine variants (P = 0.004) in severely affected subjects. A second pathogenic event in another thoracic aortic aneurysm gene or the COL4A1 gene (known to be involved in cerebral aneurysm) was found in nine individuals. A polygenic model involving at least nine modifier loci (named gMod-M1-9) was observed through cross-mapping of results. Notably, gMod-M2 which co-localizes with PRKG1, in which activating variants have already been described in thoracic aortic aneurysm, and gMod-M3 co-localized with a metalloprotease (proteins of extra-cellular matrix regulation) cluster. Our results represent a major advance in understanding the complex genetic architecture of MFS and provide the first steps toward prediction of clinical evolution.

The high incidence of cystic fibrosis (CF) is due to the frequency of the c.1521_1523delCTT variant in the cystic fibrosis transmembrane conductance regulator (CFTR), but its age and origin are uncertain. This gap limits attempts to shed light on the presumed heterozygote selective advantage that accounts for the variant's high prevalence among Caucasian Europeans and Europe-derived populations. In addition, explaining the nature of heterozygosity to screened individuals with one c.1521_1523delCTT variant is challenging when families raise questions about these issues. To address this gap, we obtained DNA samples from 190 patients bearing c.1521_1523delCTT and their parents residing in geographically distinct European populations plus a Germany-derived population in the USA. We identified microsatellites spanning CFTR and reconstructed haplotypes at 10 loci to estimate the time/age of the most recent common ancestor (tMRCA) with the Estiage program. We found that the age estimates differ between northwestern populations, where the mean tMRCA values vary between 4600 and 4725 years, and the southeastern populations where c.1521_1523delCTT seems to have been introduced only about 1000 years ago. The tMRCA values of Central Europeans were intermediate. Thus, our data resolve a controversy by establishing an early Bronze Age origin of the c.1521_1523delCTT allele and demonstrating its likely spread from northwest to southeast during ancient migrations. Moreover, taking the archeological record into account, our results introduce a novel concept by suggesting that Bell Beaker folk were the probable migrating population responsible for the early dissemination of c.1521_1523delCTT in prehistoric Europe.

Objective: To quantify the effect of genetic factors and generations influencing the age at onset (AAO) in families with frontotemporal lobar dementia (FTD) due to C9ORF72 hexanucleotide repeat expansions and GRN mutations., Methods: We studied 504 affected individuals from 133 families with C9ORF72 repeat expansions and 90 FTD families with mutations in GRN , 2 major genes responsible for FTD and/or amyotrophic lateral sclerosis. Intrafamilial correlations of AAO were analyzed, and variance component methods were used for heritability estimates. Generational effects on hazard rates for AAO were assessed using mixed-effects Cox proportional hazard models., Results: A generational effect influencing AAO was detected in both C9ORF72 and GRN families. Nevertheless, the estimated proportion of AAO variance explained by genetic factors was high in FTD caused by C9ORF72 repeat expansions (44%; p = 1.10e-4), 62% when the AAO of dementia was specifically taken into account ( p = 8.10e-5), and to a lesser degree in GRN families (26%; p = 0.17). Intrafamilial correlation analyses revealed a significant level of correlations in C9ORF72 families according to the degree of kinship. A pattern of intrafamilial correlations also suggested potential X-linked modifiers acting on AAO. Nonsignificant correlation values were observed in GRN families., Conclusions: Our results provide original evidence that genetic modifiers strongly influence the AAO in C9ORF72 carriers, while their effects seem to be weaker in GRN families. This constitutes a rational to search for genetic biomarkers, which could help to improve genetic counseling, patient care, and monitoring of therapeutic trials.

Relationship inference in a population is of interest for many areas of research from anthropology to genetics. It is possible to directly infer the relationship between the two individuals in a couple from their genetic data or to indirectly infer it from the genetic data of one of their offspring. For this reason, one can wonder if it is more advantageous to sample couples or single individuals to study relationships of couples in a population. Indeed, sampling two individuals is more informative than sampling one as we are looking at four haplotypes instead of two, but it also doubles the cost of the study and is a more complex sampling scheme. To answer this question, we performed simulations of 1000 trios from 10 different relationships using real human haplotypes to have realistic genome-wide genetic data. Then, we compared the genome sharing coefficients and the relationship inference obtained from either a pair of individuals or one of their offspring using both single-point and multi-point approaches. We observed that for relationships closer than 1st cousin, pairs of individuals were more informative than one of their offspring for relationship inference, and kinship coefficients obtained from single-point methods gave more accurate or equivalent genome sharing estimations. For more remote relationships, offspring were more informative for relationship inference, and inbreeding coefficients obtained from multi-point methods gave more accurate genome sharing estimations. In conclusion, relationship inference on a parental pair or on one of their offspring provides complementary information. When possible, sampling trios should be encouraged as it could allow spanning a wider range of potential relationships., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Most studies on multifactorial diseases are performed under the assumption of a polygenic additive liability. In particular, missing heritability and individual risk scores are estimated under this model. In this paper, we use the example of diabetes to highlight the pitfalls of relying on such a model, when there are reasons to suspect etiological heterogeneity and/or departure from the hypotheses on the environmental factor effects., (© 2016 S. Karger AG, Basel.)

Genome-wide association studies have uncovered hundreds of common genetic variants involved in complex diseases. However, for most complex diseases, these common genetic variants only marginally contribute to disease susceptibility. It is now argued that rare variants located in different genes could in fact play a more important role in disease susceptibility than common variants. These rare genetic variants were not captured by genome-wide association studies using single nucleotide polymorphism-chips but with the advent of next-generation sequencing technologies, they have become detectable. It is now possible to study their contribution to common disease by resequencing samples of cases and controls or by using new genotyping exome arrays that cover rare alleles. In this review, we address the question of the contribution of rare variants in common disease by taking the examples of different diseases for which some resequencing studies have already been performed, and by summarizing the results of simulation studies conducted so far to investigate the genetic architecture of complex traits in human. So far, empirical data have not allowed the exclusion of many models except the most extreme ones involving only a small number of rare variants with large effects contributing to complex disease. To unravel the genetic architecture of complex disease, case-control data will not be sufficient, and alternative study designs need to be proposed together with methodological developments., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background/aims: If the parents of an individual are related, it is possible for the individual to have received at 1 locus 2 identical-by-descent alleles that are copies of a single allele carried by the parents' common ancestor. The inbreeding coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of the genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based inbreeding coefficient f, and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers., Methods: For this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of the ERSA software., Results: Single-point methods were found to have higher standard deviations than other methods. ROHs gave the best estimates provided the correct length threshold is known. HMMs on sparse data gave equivalent or better results than HMMs modeling LD. Provided LD is correctly accounted for, the inbreeding estimates were very similar using the different SNP panels. The HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected inbreeding up to second-cousin offspring. We applied the best method on release 3 of the HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release., Conclusions: We recommend using HMMs on multiple sparse maps to estimate and detect inbreeding in large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500-kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH (Gujarati Indians from Houston in Texas) population., (© 2014 S. Karger AG, Basel)

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., (Copyright © 2013 S. Karger AG, Basel.)

Objectives: In cystic fibrosis, mutations of the CFTR gene lead to diffuse bronchiectasis (DB). DB is also associated with other diseases including rheumatoid arthritis (RA) in which the role of genetic factors in the predisposition to DB remains unclear., Methods: A family-based association study was carried out to determine whether the frequency of CFTR mutations was higher in patients with RA-associated DB and to determine whether a causal relationship could be established between the variant and the disease by evaluating its cosegregation with DB within families. Families of probands with RA-DB were included if one first-degree relative had RA and/or DB. The controls comprised healthy subjects requesting genetic counselling because their partner had cystic fibrosis., Results: The frequency of CFTR mutations was higher in family members with RA-DB or DB only than in unaffected relatives (p<0.005 for each comparison) and in unrelated healthy controls (p<0.001 for each comparison) but not in family members with RA only. CFTR mutations were more frequent in family members with RA-DB than in those with RA only (OR 5.30, 95% CI 2.48 to 11.33; p<5×10(-5)). They cosegregated with RA-DB in the families (sib-TDT=10.82, p=0.005)., Conclusions: RA-DB should be added to the list of phenotypes in which CFTR mutations are pathogenic. CFTR mutation is the first genetic defect linked to an extra-articular feature of RA to be described. CFTR mutations in patients with RA appear to be an important marker of the risk of associated DB, which has been linked to a less favourable prognosis.

High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases., (© 2011 Wiley-Liss, Inc.)

Substantial clinical variability is observed in many Mendelian diseases, so that patients with the same mutation may develop a very severe form of disease, a mild form or show no symptoms at all. Among the factors that may explain these differences in disease expression are modifier genes. In this paper, we review the different strategies that can be used to identify modifier genes and explain their advantages and limitations. We focus mainly on the statistical aspects but illustrate our points with a variety of examples from the literature.

Background: Disparities in the spectrum of mutations within the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) gene are commonly observed in populations from different ethnical and/or geographical origins. The occurrence of CF in Brittany (western France) is one of the highest in populations from Caucasian origin (<1/2000 in specific areas). The W846X(2), 1078delT and G551D mutations, as well as the I1027T polymorphism in cis with the DeltaF508 mutation (currently referred to as p.F508del) are particularly frequent in this area. We investigated the age of the respective variants in the region of interest., Methods: Several polymorphic markers surrounding the CFTR gene were genotyped. Allele frequencies as well as mutation rates and other parameters were used to calculate the respective age of the most recent common ancestors in the region of interest by a previously employed, simple likelihood-based method., Results: Following haplotype reconstruction and simulation, the ages were estimated to be approximately 600, 1000, 1200 and 600 years, respectively (with a 95% confidence interval)., Conclusions: These datings thus provide historical insights in the context of understanding population migrations. They also underline the usefulness of this method for estimating the age of rare mutations with a limited number of carriers.

Objective: The tagging approach appears as a promising tool to test the association of genetic variants with complex traits such as disease susceptibility or drug response. However, since tag markers are selected only on the basis of inter-marker LD properties, regardless of any phenotypes, it remains unclear to what extent they can be useful to predict variable drug responses, once typed in clinical material. We undertook a study to provide further insights into the usefulness of the tagging approach for selecting phenotype-associated markers relevant to drug response., Methods: Several tagging methods were applied to the genotyping data of two drug-metabolizing enzymes, NAT2 and CYP2D6, and the ability of the selected tagging markers to predict the individual metabolizer status was empirically evaluated. We also assessed the impact of LD levels, tagging thresholds and allele frequencies on tagging efficiency., Results: We found that the functional variation was adequately represented by the selected tagging markers, these latter providing a classification accuracy for the individual metabolizer status close to the maximal 100% value observed with the entire set of polymorphisms., Conclusion: The tagging approach is an interesting approach to select candidate gene markers predictive of drug response in pharmacogenomic studies., (Copyright (c) 2008 S. Karger AG, Basel.)

To test for association between a disease and a set of linked markers, or to estimate relative risks of disease, several different methods have been developed. Many methods for family data require that individuals be genotyped at the full set of markers and that phase can be reconstructed. Individuals with missing data are excluded from the analysis. This can result in an important decrease in sample size and a loss of information. A possible solution to this problem is to use missing-data likelihood methods. We propose an alternative approach, namely the use of multiple imputation. Briefly, this method consists in estimating from the available data all possible phased genotypes and their respective posterior probabilities. These posterior probabilities are then used to generate replicate imputed data sets via a data augmentation algorithm. We performed simulations to test the efficiency of this approach for case/parent trio data and we found that the multiple imputation procedure generally gave unbiased parameter estimates with correct type 1 error and confidence interval coverage. Multiple imputation had some advantages over missing data likelihood methods with regards to ease of use and model flexibility. Multiple imputation methods represent promising tools in the search for disease susceptibility variants.