Genetic Prediction and Screening of Early Life Polygenic Autoimmunity
Notice bibliographique
Résumé
Autoimmune disorders occur when the immune system inappropriately reacts to self, causing the destruction of healthy tissue. Most polygenic autoimmune disorders share a common genetic architecture with strong associations in human leukocyte antigen (HLA) region genes which are critical to antigen specific immunity. The recent widespread availability of affordable single nucleotide polymorphism (SNP) array genotyping has enabled large case-control genome-wide association studies (GWAS) of polygenic traits. Polygenic risk scores (PRS) utilising SNPs identified from GWAS have enabled discrimination and prediction of polygenic traits. However, the unique architecture of polygenic autoimmunity requires special consideration and there exists scope for new methodologies to improve prediction of autoimmunity. In this thesis I focus on Type 1 Diabetes (T1D) and Coeliac Disease (CD) where existing PRS have demonstrated potential to classify diabetes type, predict incident cases and provide insight into research questions. Challenges in modelling HLA region associated risk arise due to its highly polymorphic nature, long range linkage disequilibrium (LD) and epistatic effects further confounded by nomenclature predating modern genetic understanding. In Chapter 2, focusing on T1D and using case-control data from the Type 1 Diabetes Genetics Consortium (T1DGC) I develop a new method to accurately mark molecular HLA haplotypes at the DR/DQ locus by studying linkage disequilibrium between SNPs and HLA haplotypes. Using T1DGC cohort genetic data I then develop an improved PRS model (“T1D-GRS2”) by accounting for haplotype interactions at the DR/DQ locus. By standardising the PRS to the UK Biobank population cohort I demonstrate potential thresholds for population screening and prediction. However, these results were developed and validated in European ancestry samples only, thus I was unable to demonstrate applicability to other ancestry populations. Further to this work, in Chapter 3 I applied the same methodology using CD case- control genotyping data. CD occurs due an autoimmune reaction to the ingestion of gluten peptides and current genetic testing employed in clinic does not factor in polygenic risk and is not cost-effective. In contrast to the previous chapter, we limited selection of SNPs for the “CD-GRS” PRS to those that we were able to develop into a direct genotyping panel and validated the PRS panel in a small clinical cohort from a paediatric gastroenterology clinic at Stollery Children’s Hospital, Alberta. By validating the PRS panel in a clinical setting and again standardising to the UK Biobank we were able to suggest potential use cases such as the PRS panel as an affordable rule-out test to avoid gastroscopy, which carries risks in a paediatric population. The analysis was again limited to European ancestry participants. Throughout my previous chapters and in ongoing studies of PRS there is a long- standing question over the applicability of PRS developed in European ancestry cohorts to non-European populations. In Chapter 4 I examine polygenic risk for T1D and T2D in a mixed ancestry US population youth with diabetes and examine the use of PRS in classification of diabetes type, and in prediction of the onset of T1D associated autoimmunity. I demonstrate the “T1D-GRS2” is broadly effective in a mixed ancestry population, however additional work is needed to more closely study subpopulations and determine if population specific PRS are needed or if trans- ancestry PRS can be broadly applied in all cases such as “T1D-GRS2”. Screening and follow-up studies are crucial to develop effective interventions such as immunotherapies which have demonstrated the ability to delay onset of T1D. In Chapter 5, tying together my previous findings I develop a combined screening panel for newborn screening of T1D and CD using a neural network model validated in Seattle area samples. The model and panel are made publicly available and easily reproducible for new and future screening studies. In this thesis I have improved upon the characterisation of polygenic risk associated with T1D and CD and expanded upon potential applications in clinical and research settings, particularly for population screening and intervention studies. I In Chapter 6 I discuss my findings, the implications of those findings, limitations and future steps and transferability to other polygenic autoimmune disorders.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».