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

Payne, Amanda (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/gt54kn01b?locale=zh
Published

Abstract

 

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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