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      Association of modifiers and other genetic factors explain Marfan syndrome clinical variability

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          Abstract

          <p class="first" id="Par1">Marfan syndrome (MFS) is a rare autosomal dominant connective tissue disorder related to variants in the <i>FBN1</i> 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 <i>FBN1</i> disease-causing variant carriers: (1) an <i>FBN1</i> eQTL analysis in 80 fibroblasts of <i>FBN1</i> 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. </p><p id="Par2">Three genetic mechanisms of variability were found. A new genotype/phenotype correlation with an excess of loss-of-cysteine variants ( <i>P</i> = 0.004) in severely affected subjects. A second pathogenic event in another thoracic aortic aneurysm gene or the <i>COL4A1</i> 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 <i>PRKG1</i>, 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. </p>

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          Most cited references 11

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          Genetics of gene expression and its effect on disease.

          Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
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            QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data

            Array-based technologies have been used to detect chromosomal copy number changes (aneuploidies) in the human genome. Recent studies identified numerous copy number variants (CNV) and some are common polymorphisms that may contribute to disease susceptibility. We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray™ SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. Other parameters are set via maximum marginal likelihood to prior training data of known structure. QuantiSNP provides probabilistic quantification of state classifications and significantly improves the accuracy of segmental aneuploidy identification and mapping, relative to existing analytical tools (Beadstudio, Illumina), as demonstrated by validation of breakpoint boundaries. QuantiSNP identified both novel and validated CNVs. QuantiSNP was developed using BeadArray™ SNP data but it can be adapted to other platforms and we believe that the OB-HMM framework has widespread applicability in genomic research. In conclusion, QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies.
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              Revised diagnostic criteria for the Marfan syndrome.

              In 1986, the diagnosis of the Marfan syndrome was codified on the basis of clinical criteria in the Berlin nosology [Beighton et al., 1988]. Over time, weaknesses have emerged in these criteria, a problem accentuated by the advent of molecular testing. In this paper, we propose a revision of diagnostic criteria for Marfan syndrome and related conditions. Most notable are: more stringent requirements for diagnosis of the Marfan syndrome in relatives of an unequivocally affected individual; skeletal involvement as a major criterion if at least 4 of 8 typical skeletal manifestations are present; potential contribution of molecular analysis to the diagnosis of Marfan syndrome; and delineation of initial criteria for diagnosis of other heritable conditions with partially overlapping phenotypes.
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                Author and article information

                Journal
                European Journal of Human Genetics
                Eur J Hum Genet
                Springer Nature America, Inc
                1018-4813
                1476-5438
                August 7 2018
                Article
                10.1038/s41431-018-0164-9
                6244213
                30087447
                © 2018

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