459 research outputs found

    Clinical application of high throughput molecular screening techniques for pharmacogenomics.

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    Genetic analysis is one of the fastest-growing areas of clinical diagnostics. Fortunately, as our knowledge of clinically relevant genetic variants rapidly expands, so does our ability to detect these variants in patient samples. Increasing demand for genetic information may necessitate the use of high throughput diagnostic methods as part of clinically validated testing. Here we provide a general overview of our current and near-future abilities to perform large-scale genetic testing in the clinical laboratory. First we review in detail molecular methods used for high throughput mutation detection, including techniques able to monitor thousands of genetic variants for a single patient or to genotype a single genetic variant for thousands of patients simultaneously. These methods are analyzed in the context of pharmacogenomic testing in the clinical laboratories, with a focus on tests that are currently validated as well as those that hold strong promise for widespread clinical application in the near future. We further discuss the unique economic and clinical challenges posed by pharmacogenomic markers. Our ability to detect genetic variants frequently outstrips our ability to accurately interpret them in a clinical context, carrying implications both for test development and introduction into patient management algorithms. These complexities must be taken into account prior to the introduction of any pharmacogenomic biomarker into routine clinical testing

    LABRAD : Vol 46, Issue 4 - October 2021

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    Role of Barcoding in a Clinical Laboratory to Reduce Pre-Analytical Errors Congenital Dyserythropoietic Anemia: The Morphological Diagnosis Digital Imaging in Hematology: A New Beginning Metabolomics: Identification of Fatty Acid Oxidation (FAO) Disorders Next-Generation Sequencing for HLA Genotyping Urine Metabolomics to identify Organic Academia Next-Generation Sequencing (NGS) of Solid Tumor Importance of using Genomic Tool in Microbial Identification Radiology Practice in 21st Century: Role of Artificial Intelligence Case Quiz Best of the Recent Past Polaroidhttps://ecommons.aku.edu/labrad/1036/thumbnail.jp

    A rapid allele-specific assay for HLA-A*32:01 to identify patients at risk for vancomycin-induced Drug Reaction with Eosinophilia and systemic symptoms

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    Human leukocyte antigen (HLA) alleles have been implicated as risk factors for immune-mediated adverse drug reactions. We recently reported a strong association between HLA-A*32:01 and vancomycin-induced drug reaction with eosinophilia and systemic symptoms (DRESS). Identification of individuals with the risk allele prior to or shortly after the initiation of vancomycin therapy is of great clinical importance to prevent morbidity and mortality, improve drug safety and antibiotic treatment options. A prerequisite to the success of a pharmacogenetic screening tests is the development of simple, robust, cost-effective single HLA allele test that can be implemented in routine diagnostic laboratories. In this study, we developed a simple, real-time allele-specific PCR for typing the HLA-A*32:01 allele. Four-hundred and fifty-eight DNA samples including thirty HLA-A*32:01-positive samples were typed by allele-specific PCR. Compared to ASHI accredited sequence-based high-resolution, full allelic HLA typing, this assay demonstrates 100% accuracy, sensitivity of 100% (95% CI: 88.43% to 100%) and specificity of 100% (95% CI: 99.14% to 100%). The lowest limit of detection of this assay using the Power Up SYBR Green is 10 ng of template DNA. The assay demonstrates a sensitivity and specificity to differentiate HLA-A*32:01 allele from closely related non-HLA-A*32 alleles and may be used in clinical settings to identify individuals with the risk allele prior or during the course of vancomycin therapy

    A novel genotyping approach to improve transfusion support for patients with HLA and/or HPA alloantibodies

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    Patients who require platelet transfusion support but have become sensitised to Human Leucocyte Antigens (HLA) or Human Platelet Antigens (HPA) require suitably matched or selected products to avoid adverse transfusion reactions resulting from antibodies reacting with the transfused product. Provision of compatible products for these patients is often challenging, and requires significant resources from the blood service. This study set out to develop and implement next generation sequencing (NGS) technology to enhance the HLA and HPA definition of both platelet donors and recipients.An NGS based method was designed and developed for high throughput, allele level HLA class I genotyping and used to evaluate the impact of NGS technology on the selection of platelet donors using HLA epitope matching (HEM). In addition, an alternative NGS approach was designed to simultaneously sequence the six genes that code for glycoproteins expressing HPA in order to define all known HPA systems in both donor and patient samples.Allele level HLA-A, -B and –C genotypes were generated for 519 platelet donors by NGS. A critical evaluation of algorithms used to predict alleles from low to medium resolution HLA types demonstrated that NGS was more accurate when determining HLA epitopes for the selection of platelets by HEM. The HLA genotyping data obtained was used to establish previously undefined HLA allele and haplotype frequencies at third field resolution in the English platelet donor population. This thesis also includes the first reported NGS based method for the simultaneous genotyping of HPA-1 to HPA-29, with the additional capability of novel HPA detection. NGS has been shown to significantly improve the definition of both HLA and HPA genetic systems and will provide a number of future benefits for laboratories and the patients they support, including provision of well matched transfusion products, the detection of rare or novel polymorphisms and increased knowledge of HLA and HPA frequencies

    Accuracy of Blood Group Typing in the Management and Prevention of Alloimmunization

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    Blood transfusion is an effective therapeutic approach for several hematological conditions including sickle cell disease (SCD), thalassaemia, myelodysplastic syndrome (MDS), and autoimmune hemolytic anemia. It is also often indicated for transplantation and for patients receiving medical treatments for cancer. However, transfusion treatment can lead to the red blood cell (RBC) alloimmunization when an incompatible antigen is inadvertently present in the transfused blood. Alloantibodies can cause RBC destruction and many other complications defeating the purpose of the treatment. The risk of development of multiple alloantibodies increases with the frequency of transfusions in transfusion-dependent patients and can be mitigated by transfusing blood type negative for multiple antigens to prevent hemolysis. This chapter discusses the transfusion’s risk of RBC alloimmunization as an adverse event; consequences of alloimmunization in patients’ care; approaches to prevent and/or mitigate alloimmunization and enhance transfusion efficacy; application of RBC genotyping to supplement serology for preventing alloimmunization. The currently available techniques for RBC genotyping and the importance of reference reagents for determining the genotyping accuracy will also be discussed

    Improving CNV detection from short-read MPS data in neuromuscular disorders

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    Neuromuscular disorders (NMD) are highly heterogenic with around 1000 reported different subtypes. Most are genetic in origin, and some 500 genes are currently identified to cause NMDs. Massively parallel sequencing (MPS) approaches have been widely used to increase the cost-effectiveness and diagnostic yield in the work-up of the genetic molecular diagnosis and to speed up the process. Copy number variants (CNVs), deletions and duplications larger than 50 base pairs, explain approximately 10% of the Mendelian disorders. No best practices pipelines have been developed yet for CNV analysis from MPS data. Therefore, the detection and verification of CNV findings has often involved complementary methods, such as array comparative genomic hybridization (array CGH), multiplex ligation-dependent probe amplification (MLPA) and quantitative PCR approaches. Recently, various CNV detection programs have been developed, but for widely different types of designated research settings, which complicates choosing the correct approach for NMDs. These individual programs have generally exhibited less than ideal sensitivity and specificity for CNV detection. Our aim was to develop a comprehensive pipeline for the detection and annotation of CNVs with high accuracy from targeted gene panel sequencing and whole exome sequencing (WES) data of patients with NMDs. Four different CNV analysis programs were chosen for this study: CoNIFER, XHMM, ExomeDepth and CODEX. The targeted gene panel MYOcap includes 349 genes for myopathic disorders and MNDcap 302 genes for neurogenic disorders in their current panel versions. 2359 samples were sequenced with MYOcap, 942 samples with MNDcap and 262 samples with WES. This included for the targeted gene panels 24 positive control samples with previously characterized CNVs and 31 negative control samples with certain genes verified to not have CNVs. A detection sensitivity of 100% and specificity of 100% were reached for these control samples. Previously undetected CNVs from MYOcap or MNDcap sequenced samples were verified as true positive detections in 36 cases with MLPA, PCR or array CGH, and eight CNVs were verified as false positive detections. These and the positive control samples were utilized in validation of a predictive logistic regression model. In silico CNV generation into MYOcap sequenced samples provided 18,677 specific and 3892 unspecific CNV detections to initially train the model. The model was trained to differentiate true positive detections from false positive detections in order to increase the specificity of the CNV detection pipeline. The advantage of using four different CNV detection programs compared to using them individually, or with any other combination, was demonstrated by CNV detection sensitivity from the set of in silico CNVs. The predictive model with variables from all four programs provided the highest sensitivity (96.6%) and specificity (87.5%) for predicting CNV detections correctly, indicating an accuracy of 95.5% (95% CI 87.3–99.1%). The CNV detection pipeline together with the predictive model was validated for WES samples with control samples with 235 previously characterized CNVs. For CNVs spanning at least three exons, the detection sensitivity was 97.3% and the sensitivity of the predicative model was 99.3% after adjusting the model threshold for WES data. The CNV annotation platform cnvScan was expanded to contain the most recent CNV population databases as well as in-house CNV databases for all the sequenced sample sets. CNV detection results were filtered by < 1% frequency with reciprocal overlap of 90% in the common CNV population databases, with both it and < 5% frequency with 50% reciprocal overlap in the in-house CNV database, and by the true positive prediction with the model. These procedures significantly decreased the workload (with 3–13% of the original CNV detections preserved) in evaluating the CNVs further regarding clinical significance. The added value, i.e. the additional diagnostic yield from CNVs for both the targeted gene panel sequenced samples and WES samples was estimated to be 1.9%. Altogether 39 final genetic diagnoses were solved with these CNV findings. In addition, 18 patient cases had a likely pathogenic finding, and five had a heterozygous CNV likely pathogenic for a recessive disease without association to the patient’s phenotype. The clarified cases included six different DMD deletions or duplications causing dystrophinopathies. In three sequenced familial cases, the detected CNVs in CACNA1A, SGCD and TTN genes co-segregated with the disease. One case had two separate genetic diseases, tibial muscular dystrophy (TMD) and BMD, caused by the founder mutation FINmaj in the gene TTN and a deletion in DMD. Some of the solved cases had novel findings: the second ever reported large intragenic deletion in NEB causing dominant disease, and the first CNV, an intragenic deletion, in TIA1 in a patient diagnosed with Welander distal myopathy (WDM). Some of the genes associated with NMDs are challenging to analyze from short-read sequencing data due to homology or repetitive regions. An additional script was thus written to differentiate copy numbers of the highly homologous genes, SMN1 and SMN2. Two SMN1/SMN2 copy number 0/3 control cases were successfully recognized, and five cases were identified with a possible exon 7 conversion in SMN1 and a compatible spinal muscular atrophy phenotype. The latter findings were considered likely pathogenic and are awaiting further validation on the genomic level. Comparison of CNV detections within the in-house CNV database revealed divergences in the CNV detections within the triplicate repetitive region of NEB with potentially clinically significant changes. One array CGH validated change correlated well with the nemaline rod pathology observed in the patient. CNV analysis utilizing MPS data from targeted gene panels and WES samples provided increased diagnostic yield as reported also in other studies on NMDs. Our multi-algorithm and -platform approach decreased the workload in variant analysis and provided more insight into the many difficult to analyze genomic regions involved in NMDs. In the future, whole genome sequencing and long-read sequencing will likely provide higher resolution for CNV detections and reveal an even wider spectrum of structural genomic variants, together with other emerging comprehensive methods, such as optical mapping.Lihastaudit ovat hyvin heterogeenisiä, ja niistä on kuvattu noin tuhat alatyyppiä. Suurin osa on perinnöllisiä tauteja, ja tähän mennessä on tunnistettu noin 500 eri lihastauteja aiheuttavaa geeniä. Massiivista rinnakkaissekvensointia (MPS) on käytetty laajalti perinnöllisten tautien diagnostisen prosessin nopeuttamiseksi, kustannustehokkuuden parantamiseksi ja lopullisen geeniperäisen diagnoosin saavuttamiseksi. Kopiolukumuutokset, yli 50 emäsparin deleetiot tai duplikaatiot, aiheuttavat arviolta 10 % Mendelin mukaisesti periytyvistä taudeista. Kopiolukumuutosten havaitsemiseen sekvensointidatasta ei ole vielä kehitetty yleisesti hyväksyttyjä ja suositeltuja käytänteitä. Kopiolukumuutosten havaitsemiseksi ja varmistamiseksi käytetäänkin usein täydentäviä menetelmiä, kuten vertaileva genominen hybridisaatio sirulla (aCGH), rinnastettu ligaatio-riippuvainen alukemonistus (MLPA) ja kvantitatiivinen PCR. Kopiolukumuutosten havaitsemiseen sekvensointidatasta on kehitetty useita työkaluja vaihtelevissa tutkimusasetelmissa, mikä hankaloittaa oikean lähestymistavan valitsemista lihastaudeille. Yksittäisten ohjelmien on todettu tuottavan usein epätäsmällisiä ja herkkyydeltään vaihtelevia tai riittämättömiä havaintoja. Tämän tutkimuksen tavoitteena oli kehittää kattava menetelmä kopiolukumuutosten havaitsemiseen ja annotointiin suurella tarkkuudella kohdennetun geenipaneelin ja koko eksomin (WES) sekvensointidatasta lihastautipotilailta. Tutkimukseen valittiin neljä kopiolukumuutosanalyysin työkalua: CoNIFER, XHMM, ExomeDepth ja CODEX. Kohdennetuista geenipaneeleista MYOcap kattaa 349 geeniä lihaspainotteisille taudeille ja MNDcap 302 hermopainotteisille taudeille nykyisissä paneeliversioissa. MYOcap:lla sekvensointiin 2359 näytettä, MNDcap:lla 942 ja WES:llä 262. Kohdennetuilla geenipaneeleilla sekvensointiin 24 positiivista kontrollinäytettä, joissa on aiemmin tunnistettu kopiolukumuutos, ja 31 negatiivista kontrollinäytettä, joissa tietyt geenit oli varmistettu kopiolukumuutoksia sisältämättömiksi. Kontrollinäytteille saavutettiin kehittämällämme menetelmällä 100 % havaitsemisherkkyys ja 100 % tarkkuus. MYOcap:lla tai MNDcap:lla sekvensoiduista näytteistä havaituista kopiolukumuutoksista 36 varmistettiin todellisiksi havainnoiksi MLPA:lla, PCR:lla tai aCGH:llä ja kahdeksan varmistettiin vääriksi positiivisiksi. Nämä ja positiiviset kontrollinäytteet sisällytettiin logistiseen regressioon perustuvan tilastollisen mallin validointiin. Erottelumallin kehitysvaiheessa MYOcap-sekvensoituihin näytteisiin tehtiin in silico kopiolukumuutoksia, mikä tuotti 18677 spesifiä ja 3892 ei-spesifiä kopiolukumuutoshavaintoa mallinnukseen. Malli kehitettiin erottelemaan todelliset kopiolukumuutoshavainnot vääristä positiivista havainnoista havaintomenetelmän tarkkuuden lisäämiseksi. Neljän ohjelman havaintojen käyttämisen paremmuus verrattuna ohjelmien käyttämiseen yksittäin tai muilla yhdistelmillä todennettiin in silico kopiolukumuutosten havaitsemisen herkkyyden tuloksilla. Erottelumalli, jossa oli muuttujia kaikilta neljältä ohjelmalta, saavutti korkeimman herkkyyden (96,6 %), täsmällisyyden (87,5 %) ja tarkkuuden 95,5 % (95 % CI 87,3–99,1 %) kopiolukumuutosten erottelulle. Kopiolukumuutoshavaitsemismenetelmä ja erottelumalli validoitiin WES-kontrollinäytteillä, joissa oli 235 aiemmin tunnistettua kopiolukumuutosta. Havaitsemisherkkyys kopiolukumuutoksille, jotka sisältävät vähintään kolme eksonia oli 97,3 %, ja erottelumallin herkkyys oli 99,3 % kunhan mallin arviointiraja oli uudelleensäädetty WES-datalle. Kopiolukumuutosten annotaatiotyökalu cnvScan laajennettiin sisältämään uusimmat kopiolukumuutospopulaatiotietokannat ja talonsisäinen kopiolukumuutostietokanta kaikista sekvensointinäytejoukoista. Alkuperäiset kopiolukumuutoshavainnot neljältä ohjelmalta suodatettiin 1 % enimmäisyleisyyden ja vastavuoroisen 90 % muutoksen kattamisen vaatimuksella yleisissä kopiolukumuutospopulaatiotietokannoissa, tällä sekä 5 % enimmäisyleisyyden ja vastavuoroisen 50 % muutoksen kattamisen vaatimuksella talonsisäisessä tietokannassa, ja lisäksi erottelumallilla todellisiin havaintoihin. Nämä toimenpiteet vähensivät merkittävästi työmäärää kliinisen merkityksen arvioinnille kopiolukumuutoksille säästäen 3–13 % alkuperäisistä havainnoista. Lisääntyneiden diagnoosien määrä kopiolukumuutoshavaintojen myötä sekä kohdennetuilla geenipaneeleilla että WES-sekvensoiduilla näytteillä oli noin 1,9 %. Kopiolukumuutoshavainnoilla saavutettiin 39 lopullista geneettistä diagnoosia potilaille. Lisäksi 18:lla tutkitulla oli todennäköisesti patogeeninen löydös, ja viidellä tutkitulla havaittiin heterotsygoottinen kopiolukumuutos, jonka arvioitiin olevan patogeeninen peittyvästi periytyvän taudin variantti ilman yhteyttä potilaan taudinkuvaan. Selvitettyihin tapauksiin sisältyi kuusi eri DMD-geenissä olevaa deleetiota tai duplikaatiota, jotka aiheuttivat dystrofinopatioita. Kolme potilasta, joilla oli oireisia perheenjäseniä, sekvensointiin perhetapauksina, ja havaitut kopiolukumuutokset geeneissä CACNA1A, SGCD ja TTN segregoituivat yhdessä taudin kanssa. Yhdellä tutkitulla havaittiin kaksi perinnöllistä tautia, tibiaalinen lihasdystrofia (TMD) ja BMD, joiden aiheuttajina olivat perustajamutaatio FINmaj TTN-geenissä ja deleetio DMD-geenissä. Osalla selvitetyistä tapauksista oli ennen havaitsemattomia löydöksiä: NEB-geenissä toinen koskaan raportoitu iso geeninsisäinen deleetio, joka aiheuttaa vallitsevasti periytyvän taudin, sekä TIA1-geenin geeninsisäinen deleetio, joka on ensimmäinen havaittu kopiolukumuutos TIA1:ssä Welanderin distaalimyopatiaa (WDM) sairastavalla potilaalla. Jotkin geeneistä, jotka on liitetty lihastauteihin, ovat haastavia analysoitavia lyhytlukuisesta sekvensointidatasta homologian ja toistojaksojen takia. Hyvin homologisille geeneille SMN1 ja SMN2 kehitettiin erillinen ohjelma erottelemaan geenien kopiolukumäärät. Kaksi kontrollitapausta tunnistettiin onnistuneesti SMN1 ja SMN2 kopiolukumäärillä 0 ja 3, ja lisäksi tunnistettiin viisi tapausta, joilla on mahdollisesti eksonin 7 konversio SMN1:ssä ja yhteensopiva spinaalinen lihasatrofia. Jälkimmäiset löydökset luokiteltiin todennäköisesti patogeeniseksi, ja ne odottavat genomista lisävarmistusta. Kopiolukumuutoshavaintojen vertailu NEB-geenin triplikaattitoistoalueella talonsisäisessä tietokannassa paljasti eroavaisuuksia, joilla on potentiaalisesti kliinisesti merkitystä. Yksi aCGH:llä varmistettu muutos korreloi selkeästi nemaliinisauvakappalepatologian kanssa, joka potilaalla oli havaittu. Kopiolukumuutoshavainnointi käyttäen sekvensointidataa kohdennetusta geenipaneelista tai WES-näytteistä lisäsi diagnoosien määrää kuten aiemmissa vastaavissa tutkimuksissa lihastaudeille. Käyttämämme usean algoritmin ja alustan lähestymistapa vähensi varianttianalyysin työmäärää ja tarjosi lisää tietoa useista hankalasti analysoitavista genomisista alueista, jotka on liitetty lihastauteihin. Tulevaisuudessa koko genomin sekvensointi ja pitkälukuinen sekvensointi tarjonnevat paremman resoluution kopiolukumuutoksille ja paljastavat enemmän rakenteellisia genomin muutoksia yhdessä muiden kehitteillä olevien kattavien menetelmien kanssa, kuten optinen kartoitus

    The prediction of HLA genotypes from next generation sequencing and genome scan data

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    Genome-wide association studies have very successfully found highly significant disease associations with single nucleotide polymorphisms (SNP) in the Major Histocompatibility Complex for adverse drug reactions, autoimmune diseases and infectious diseases. However, the extensive linkage disequilibrium in the region has made it difficult to unravel the HLA alleles underlying these diseases. Here I present two methods to comprehensively predict 4-digit HLA types from the two types of experimental genome data widely available. The Virtual SNP Imputation approach was developed for genome scan data and demonstrated a high precision and recall (96% and 97% respectively) for the prediction of HLA genotypes. A reanalysis of 6 genome-wide association studies using the HLA imputation method identified 18 significant HLA allele associations for 6 autoimmune diseases: 2 in ankylosing spondylitis, 2 in autoimmune thyroid disease, 2 in Crohn's disease, 3 in multiple sclerosis, 2 in psoriasis and 7 in rheumatoid arthritis. The EPIGEN consortium also used the Virtual SNP Imputation approach to detect a novel association of HLA-A*31:01 with adverse reactions to carbamazepine. For the prediction of HLA genotypes from next generation sequencing data, I developed a novel approach using a naïve Bayes algorithm called HLA-Genotyper. The validation results covered whole genome, whole exome and RNA-Seq experimental designs in the European and Yoruba population samples available from the 1000 Genomes Project. The RNA-Seq data gave the best results with an overall precision and recall near 0.99 for Europeans and 0.98 for the Yoruba population. I then successfully used the method on targeted sequencing data to detect significant associations of idiopathic membranous nephropathy with HLA-DRB1*03:01 and HLA-DQA1*05:01 using the 1000 Genomes European subjects as controls. Using the results reported here, researchers may now readily unravel the association of HLA alleles with many diseases from genome scans and next generation sequencing experiments without the expensive and laborious HLA typing of thousands of subjects. Both algorithms enable the analysis of diverse populations to help researchers pinpoint HLA loci with biological roles in infection, inflammation, autoimmunity, aging, mental illness and adverse drug reactions

    Investigación de la distribución de los alelos HLA en poblaciones sanas y enfermas mediante la aplicación de nuevas metodologías de secuenciación

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, Departamento de Inmunología, Oftalmología y ORL, leída el 09/03/2021Increasing our knowledge of the HLA system, including both the complete sequence description and the assessment of its diversity at the worldwide human population-level, is of great importance for elucidating the molecular functional mechanisms of the immune system and its regulation in health and disease. Furthermore, assessment of HLA allelic and haplotypic diversity of each human population is essential in the clinical histocompatibility and transplantation setting as well as in the pharmacogenetics, immunotherapy and anthropology fields. Nevertheless, the inherent vast polymorphism and high complexity presented by the HLA system have been an important challenge for its unambiguous and in-depth (high-resolution) characterization by previously available legacy molecular HLA genotyping methods (e.g. SSP, SSO and even SBT). Recent application of novel next-generation sequencing (NGS) technology for high-resolution molecular HLA genotyping has enabled to obtain, at a high-throughput mode and larger scale, full-length and/or extended sequences and genotypes of all major HLA genes, thus overcoming most of these previous limitations. Objectives: I) Characterization of HLA allele and haplotype diversity of all major classical HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 and -DRB3/4/5) by application of NGS of a first representative cohort of the Spanish population that could also serve as a healthy control reference group. Respective statistical analyses were performed for this immunogenetic population data. II) Characterization of HLA allele and haplotype diversity of all major classical HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 and -DRB3/4/5) by application of NGS of a respective cohort of multiple sclerosis (MS) patients in the Spanish population (recruited at the Department of Neurology, Hospital Clínic, Barcelona, Catalonia, Spain). A first case-control study was carried out to examine HLA-disease associations with MS in these Spanish population cohorts as well as to attempt a fine-mapping of these allele and haplotype associations by full gene resolution level via NGS. In addition, a second analysis exercise (i.e. test case) of this case-control study was carried out using an alternative healthy control group dataset, exclusively from the Spanish northeastern region of Catalonia in this second case, to evaluate possible differences in the findings of HLA-disease association with MS due to plausible regional HLA genetic variation within mainland Spain (i.e. as a statistical way to try controlling for any possible existing population stratification)...El estudio del sistema HLA, incluyendo la descripción completa de su secuencia y de la diversidad de este complejo HLA a nivel poblacional, es de gran importancia de cara a poder entender los mecanismos moleculares y funciones del sistema inmune así como su regulación en individuos sanos y enfermos. Además, la caracterización exhaustiva de la diversidad de alelos y haplotipos HLA de cada población humana es esencial en el campo de la inmunología de trasplante e histocompatibilidad al igual que en las áreas de farmacogenética e inmunoterapia. El inmenso polimorfismo y gran complejidad que presenta el sistema HLA han sido hasta ahora importantes barreras de cara a poder caracterizarlo en gran detalle (por alta resolución) y sin ambigüedades mediante métodos de genotipaje HLA tradicionales disponibles (como son SSP, SSO o incluso SBT). La reciente aplicación de la novedosa tecnología de secuenciación masiva NGS para el genotipaje molecular HLA por alta resolución ha posibilitado obtener secuencias completas o mucho más extendidas para genotipos de los principales genes de HLA, superándose así estas previas limitaciones. Objetivos: I) Caracterización de la diversidad alélica y haplotípica de los principales genes HLA (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 y -DRB3/4/5) mediante la aplicación de NGS en una primera cohorte representativa de la población española que, igualmente, constituirá una población control de referencia para estudios de asociación de HLA y enfermedades. También, respectivos análisis estadísticos se realizaron para estos resultados de genotipaje HLA. II) Caracterización de la diversidad alélica y haplotípica de los principales genes HLA (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 y -DRB3/4/5) mediante la aplicación de NGS en una correspondiente cohorte de pacientes con esclerosis múltiple (EM) de la población española (reclutados y procedentes del Departamento de Neurología del Hospital Clínic (Barcelona, Cataluña)). Un primer estudio de asociación HLA tomando casos (pacientes EM) frente a controles sanos se llevó a cabo para examinar la asociación de genes HLA y la enfermedad de EM en estas cohortes de población española antes mencionadas. Así se buscaba realizar un mapeo fino de las respectivas asociaciones alélicas y haplotípicas de HLA mediante la gran resolución alélica proporcionada por esta metodología de secuenciación masiva. De modo adicional, y como un segundo ejercicio de análisis en este estudio de asociación HLA, se utilizó un grupo control sano alternativo al previo, que incluía individuos procedentes de la región de Cataluña (situada al noreste de España) exclusivamente en este caso, para evaluar así posibles diferencias dadas en la asociación de HLA con EM debido a la probable variación genética en HLA existente a nivel regional dentro del territorio de España...Fac. de MedicinaTRUEunpu
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