Investigating Genetic Predictors of Inhibitors among Persons with Hemophilia Restricted; Files Only

Payne, Amanda (Spring 2018)

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Hemophilia A (HA), an inherited bleeding disorder affecting approximately 20,000 males in the United States, is caused by pathogenic variants in the F8 gene leading to loss or reduced functionality of the procoagulant factor VIII (FVIII).  HA is most-commonly treated by replacing the missing or dysfunctional FVIII.  Unfortunately, 10%-15% of persons with HA develop antibodies (inhibitors) to the replacement FVIII, rendering it ineffective.  Identifying persons at highest risk of developing inhibitors is important, as treatment may be altered.  A previously-validated inhibitor-risk-prediction tool has limitations, including its reliance on prior hemophilia treatment and knowledge of family history.  An inhibitor-risk-prediction tool that relies on information available at the time of HA diagnosis could be more useful clinically.  Risk factors for inhibitors include situations that make recognition of foreign FVIII and upregulation of the immune system more likely.  Family studies have indicated a genetic component to inhibitor risk.  This dissertation explores the feasibility of constructing an inhibitor-risk-prediction tool that uses only genetic information.


In Aim 1 information about the hemophilia genotype was used to predict inhibitor status. Three different paradigms to categorize hemophilia genotype were constructed, and the ability of each to predict inhibitor status was evaluated.   The tool that used previously-published estimates of hemophilia genotype effect performed best; however, none of the tools performed as well as the previously-validated tool.


In Aim 2 variation in immune response genes was used to predict inhibitor status. Estimates for the effect size of genetic variants were obtained in two ways: a meta-analysis was performed on published studies, and estimates were empirically derived from a genetic association study.  Two tools were then developed, using estimates from the meta-analysis or the empirically-derived data. Neither of the tools performed as well as the previously-validated tool.


Aim 3 combined information from aims 1 and 2, using both hemophilia genotype and variation in immune response genes.  The best-performing tool performed similarly to the previously-validated tool, without requiring treatment or family history information.

The results of this investigation indicate that prediction of inhibitors using only genetic information may be possible.  Further validation of the results in an external population is warranted.

Table of Contents


Chapter 1:  Introduction and Rationale         1


Chapter 2:  Background         4


Hemophilia A         4


Inhibitors in Hemophilia A 4


Impact of Inhibitors           5


How Inhibitors Develop     5


Risk Factors for Inhibitor Development    6


Non-Genetic Risk Factors  6


Genetic Risk Factors          7


Hemophilia Genotype        7


Immune Response Genetics          9


Race/Ethnicity       10


Predicting Inhibitor Risk   10


Dissertation Aims   12


Chapter 3:  Evaluation of variant-scoring tools for use in assigning inhibitor risk among persons with hemophilia A  14


Abstract      14


Introduction           15


Methods     17


Population  17


Genotyping 18


Risk Prediction Tools         18


Tool based on predicted pathogenicity (Pathogenicity Tool)        18


Tool based on predicted impact on gene function (Function Tool)          18


Tool based on prior evidence (Evidence-Based Tool)       19


Statistical Analysis 20


Results        20


Pathogenicity Tool  20


Function Tool         20


Evidence-based Tool          21


Discussion  21


Conclusions 23


Acknowledgements 24


Tables         25


Figures        26


Chapter 4:  Genetic variants associated with inhibitors among persons with hemophilia:  A systematic review and meta-analysis      29


Abstract      29


Introduction           30


Methods     31


Results        33


Study identification and selection  33


Study characteristics          33


Variants investigated         34


Summary estimates of effect          34


T cell regulators      34


Class I HLA genes  35


Class II HLA genes 35


Cytokines    36


Evaluation of heterogeneity           36


Evaluation of bias   36


Publication bias      37


Study-related bias  37


Discussion  37


Conclusions 40


Acknowledgements 41


Tables         42


Figures        48


Chapter 5:  Associations between variants in immune response genes and inhibitors among persons with hemophilia A  51


Abstract      51


Introduction           52


Methods     53


Population  53


Laboratory Methods           54


Statistical Analysis 55


Results        56


HLA Class I and II Variants           56


Other Variants        57


Discussion  58


Conclusions 61


Acknowledgements 61


Tables         62


Chapter 6:  Evaluation of inhibitor risk prediction tools based on genetic risk factors in persons with hemophilia A  68


Abstract      68


Introduction           69


Methods     70


Population  70


Genotyping 70


Risk Prediction Tools         71


Hemophilia Genotype Scoring       71


Immune Response Variant Scoring           72


Statistics     73


Results        73


Discussion  75


Conclusions 77


Tables         78


Figures        80


Chapter 7:  Summary, strengths, limitations, public health implications, and future research       86


Summary    86


Strengths    88


Limitations 89


Public Health Implications 89


Future Research     90


References  92


Supplementary Information  113


Chapter 3    113


Chapter 4    114


Chapter 5    199


Chapter 6    211


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