Correction: The genetic history of France.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The genetic history of France.

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 14th 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.

Rare variant association testing for multicategory phenotype.

Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context. To address this issue, we propose an extension of burden tests by using a multinomial regression model, which enables association tests between rare variants and multicategory phenotypes. We evaluated the type I error and the power of two burden tests, CAST and WSS, by simulating data under different scenarios. In the case of genetic heterogeneity between case subgroups, we showed an advantage of multinomial regression over logistic regression, which considers all the cases against the controls. We replicated these results on real data from Moyamoya disease where the burden tests performed better when cases were stratified according to age-of-onset. We implemented the functions for association tests in the R package "Ravages" available on Github.

Buserelin alleviates chloride transport defect in human cystic fibrosis nasal epithelial cells.

Cystic fibrosis (CF) is the most common autosomal recessive disease in Caucasians caused by mutations in the gene encoding the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) chloride (Cl-) channel regulated by protein kinases, phosphatases, divalent cations and by protein-protein interactions. Among protein-protein interactions, we previously showed that Annexin A5 (AnxA5) binds to CFTR and is involved in the channel localization within membranes and in its Cl- channel function. The deletion of phenylalanine at position 508 (F508del) is the most common mutation in CF which leads to an altered protein (F508del-CFTR) folding with a nascent protein retained within the ER and is quickly degraded. We previously showed that AnxA5 binds to F508del-CFTR and that its increased expression due to a Gonadoliberin (GnRH) augments Cl- efflux in cells expressing F508del-CFTR. The aim of the present work was to use the GnRH analog buserelin which is already used in medicine. Human nasal epithelial cells from controls and CF patients (F508del/F508del) were treated with buserelin and we show here that the treatment alleviates Cl- channel defects in CF cells. Using proteomics we highlighted some proteins explaining this result. Finally, we propose that buserelin is a potential new pharmaceutical compound that can be used in CF and that bronchus can be targeted since we show here that they express GnRH-R.

A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer.

BACKGROUND: Colon cancer occurrence is increasing worldwide, making it the third most frequent cancer. Although many therapeutic options are available and quite efficient at the early stages, survival is strongly decreased when the disease has spread to other organs. The identification of molecular markers of colon cancer is likely to help understanding its course and, eventually, to uncover novel genes to be targeted by drugs. In this study, we compared gene expression in a set of 95 human colon cancer samples to that in 19 normal colon mucosae, focusing on 401 genes from 5 selected pathways (Apoptosis, Cancer, Cholesterol metabolism and lipoprotein signaling, Drug metabolism, Wnt/beta-catenin). Deregulation of mRNA levels largely matched that of proteins, leading us to build in silico protein networks, starting from mRNA levels, to identify key proteins central to network activity. RESULTS: Among the analyzed genes, 10.5% (42) had no reported link with colon cancer, including the SFRP1, IGF1 and ADH1B (down), and MYC and IL8 (up), whose encoded proteins were most interacting with other proteins from the same or even distinct networks. Analyzing all pathways globally led us to uncover novel functional links between a priori unrelated or rather remotely connected pathways, such as the Drug metabolism and the Cancer pathways or, even more strikingly, between the Cholesterol metabolism and lipoprotein signaling and the Cancer pathways. In addition, we analyzed the responsiveness of some of the deregulated genes essential to network activities, to chemotherapeutic agents used alone or in presence of Lovastatin, a lipid-lowering drug. Some of these treatments could oppose the deregulations occurring in cancer samples, including those of the CHECK2, CYP51A1, HMGCS1, ITGA2, NME1 or VEGFA genes. CONCLUSIONS: Our network-based approach allowed discovering genes not previously known to play regulatory roles in colon cancer. Our results also showed that selected drug treatments might revert the cancer-specific deregulation of genes playing prominent roles within the networks operating to maintain colon homeostasis. Among those genes, some could constitute novel testable targets to eliminate colon cancer cells, either directly or, potentially, through the use of lipid-lowering drugs such as statins, in association with selected anticancer drugs.

Meta-analysis of genome-wide association studies identifies two loci associated with circulating osteoprotegerin levels.

Osteoprotegerin (OPG) is involved in bone homeostasis and tumor cell survival. Circulating OPG levels are also important biomarkers of various clinical traits, such as cancers and atherosclerosis. OPG levels were measured in serum or in plasma. In a meta-analysis of genome-wide association studies in up to 10 336 individuals from European and Asian origin, we discovered that variants >100 kb upstream of the TNFRSF11B gene encoding OPG and another new locus on chromosome 17q11.2 were significantly associated with OPG variation. We also identified a suggestive locus on chromosome 14q21.2 associated with the trait. Moreover, we estimated that over half of the heritability of OPG levels could be explained by all variants examined in our study. Our findings provide further insight into the genetic regulation of circulating OPG levels.

How important are rare variants in common disease?

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.

SNP-based linkage analysis in extended pedigrees: comparison between two alternative approaches.

BACKGROUND: Linkage analysis on extended pedigrees is often challenged by the high computational demand of exact identity-by-descent (IBD) matrix reconstruction. When such an analysis becomes not feasible, two alternative solutions are contrasted: a full pedigree analysis based on approximate IBD estimation versus a pedigree splitting followed by exact IBD estimation. A multiple splitting (MS) approach, which combines linkage results across different splitting configurations, has been proposed to increase the power of single-split solutions. METHODS: To assess whether MS can achieve a comparable power to a full pedigree analysis, we compared the power of linkage on a very large pedigree in both simulated and real-case scenarios, using variance components linkage analysis of a dense SNP array. RESULTS: Our results confirm that the power to detect linkage is affected by the pedigree size. The MS approach showed higher power than the single-split analysis, but it was substantially less powerful than the full pedigree approach in both scenarios, at any level of significance and variance explained by a quantitative trait locus. CONCLUSION: The MS approach should always be preferred to analyses based on a single split but, when adequate computational resources are available, a full pedigree analysis is better than the MS analysis. Rather than focusing on how to best split a pedigree, it might be more valuable to identify computational solutions that can make the IBD estimation of dense-marker maps practically feasible, thus allowing a full pedigree analysis.

Family-based studies to the rescue of genome-wide association studies in renal function.

Contrary to the apparent impossibility of replicating linkage results across studies on renal outcomes, and denying the general difficulty of identifying meaningful association signals under previously identified linkage peaks, a new study on an isolated Mongolian population could replicate two previously reported linkage peaks and corroborate them by significant associations at multiple single-nucleotide polymorphisms. Although the two genetic loci are not novel, the study sheds light on key aspects of the genetic analysis of kidney function in the general population.

Bivariate association analysis in selected samples: application to a GWAS of two bone mineral density phenotypes in males with high or low BMD.

Our specific aims were to evaluate the power of bivariate analysis and to compare its performance with traditional univariate analysis in samples of unrelated subjects under varying sampling selection designs. Bivariate association analysis was based on the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We conducted extensive simulations for the case of two correlated quantitative phenotypes, with the quantitative trait locus making equal or unequal contributions to each phenotype. Our simulation results confirmed that the power of bivariate analysis is affected by the size, direction and source of the phenotypic correlations between traits. They also showed that the optimal sampling scheme depends on the size and direction of the induced genetic correlation. In addition, we demonstrated the efficacy of SUR-based bivariate test by applying it to a real Genome-Wide Association Study (GWAS) of Bone Mineral Density (BMD) values measured at the lumbar spine (LS) and at the femoral neck (FN) in a sample of unrelated males with low BMD (LS Z-scores ≤ -2) and with high BMD (LS and FN Z-scores >0.5). A substantial amount of top hits in bivariate analysis did not reach nominal significance in any of the two single-trait analyses. Altogether, our studies suggest that bivariate analysis is of practical significance for GWAS of correlated phenotypes.

Genome-wide association study confirms BST1 and suggests a locus on 12q24 as the risk loci for Parkinson's disease in the European population.

We performed a three-stage genome-wide association study (GWAS) to identify common Parkinson's disease (PD) risk variants in the European population. The initial genome-wide scan was conducted in a French sample of 1039 cases and 1984 controls, using almost 500 000 single nucleotide polymorphisms (SNPs). Two SNPs at SNCA were found to be associated with PD at the genome-wide significance level (P < 3 × 10(-8)). An additional set of promising and new association signals was identified and submitted for immediate replication in two independent case-control studies of subjects of European descent. We first carried out an in silico replication study using GWAS data from the WTCCC2 PD study sample (1705 cases, 5200 WTCCC controls). Nominally replicated SNPs were further genotyped in a third sample of 1527 cases and 1864 controls from France and Australia. We found converging evidence of association with PD on 12q24 (rs4964469, combined P = 2.4 × 10(-7)) and confirmed the association on 4p15/BST1 (rs4698412, combined P = 1.8 × 10(-6)), previously reported in Japanese data. The 12q24 locus includes RFX4, an isoform of which, named RFX4_v3, encodes brain-specific transcription factors that regulate many genes involved in brain morphogenesis and intracellular calcium homeostasis.

A comparative study of three methods for detecting association of quantitative traits in samples of related subjects.

We used Genetic Analysis Workshop 16 Problem 3 Framingham Heart Study simulated data set to compare methods for association analysis of quantitative traits in related individuals. More specifically, we investigated type I error and relative power of three approaches: the measured genotype, the quantitative transmission-disequilibrium test (QTDT), and the quantitative trait linkage-disequilibrium (QTLD) tests. We studied high-density lipoprotein and triglyceride (TG) lipid variables, as measured at Visit 1. Knowing the answers, we selected three true major genes for high-density lipoprotein and/or TG. Empirical distributions of the three association models were derived from the first 100 replicates. In these data, all three models were similar in error rates. Across the three association models, the power was the lowest for the functional SNP with smallest size effects (i.e., alpha2), and for the less heritable trait (i.e., TG). Our results showed that measured genotype outperformed the two orthogonal-based association models (QTLD, QTDT), even after accounting for population stratification. QTDT had the lowest power rates. This is consistent with the amount of marker and trait data used by each association model. While the effective sample sizes varied little across our tested variants, we observed some large power drops and marked differences in performances of the models. We found that the performances contrasted the most for the tightly linked, but not associated, functional variants.

Genome-wide linkage screen of bone mineral density (BMD) in European pedigrees ascertained through a male relative with low BMD values: evidence for quantitative trait loci on 17q21-23, 11q12-13, 13q12-14, and 22q11.

CONTEXT: Bone mass is under strong genetic control, with heritability estimates greater than 50% and is likely determined by complex interactions between genetic and environmental factors. OBJECTIVE: The objective of the study was to localize genes contributing to bone mineral density (BMD) variation. DESIGN: An autosomal genome-wide scan for BMD at the lumbar spine and femoral neck was conducted with variance components linkage methods. PARTICIPANTS: A total of 103 pedigrees (Network in Europe on Male Osteoporosis Family Study) ascertained through a male relative with low (Z-score < or = -2) BMD values at either lumbar spine or femoral neck. MAIN OUTCOME MEASURES: Nonparametric multipoint logarithm of the odds ratio scores for lumbar spine and femoral neck BMD values adjusted for age, gender, and body mass index. RESULTS: We identified a total of eight chromosomal regions with logarithm of the odds ratio score of 1.5 or greater (P < or = 5 x 10(-3)): on 1q42-43, 11q12-13, 12q23-24, 17q21-23, 21q22, and 22q11 for lumbar spine and on 5q31-33 and 13q12-14 for femoral neck BMD. CONCLUSIONS: Four of our detected quantitative trait loci (QTL) reached the genome-wide criteria for significant (17q,21-23, P < or = 2 x 10(-5)) or suggestive (11q12-13, 22q11, and 13q12-14, P < or = 7 x 10(-4)) linkage. Apart from 22q11, which is a novel QTL, all other loci provide consistent replication for previously reported QTLs for BMD and other bone-related traits. Finally, several of our specific-linkage areas encompass prominent candidate genes: type 1 collagen (COL1A1) and the sclerosteosis/van Buchem disease (SOST) genes on 17q21-23; the low-density lipoprotein receptor-related protein 5 (LRP5) gene on 11q12-13; and the rank ligand gene on 13q12-14. Further analysis of these positive regions by fine linkage disequilibrium mapping is thus warranted.