University of Groningen Looking through the noise Johansson, Leonard Fredericus

University of Groningen

Looking through the noise

Johansson, Leonard Fredericus

DOI:

10.33612/diss.95673752

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Publication date:

2019

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):

Johansson, L. F. (2019). Looking through the noise: novel algorithms for genetic variant detection.

University of Groningen. https://doi.org/10.33612/diss.95673752

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LIST OF TABLES

List of Tables

2.1 List of genes included in the targeted SureSelect Enrichment Kit . . . 37

2.2 Overview of the sequence performance for the validation runs . . . 42

2.3 Diagnostic workflow and implementation guidelines . . . 46

4.1 Panel 1 and 2 genes and ACMG and SFMPP inclusion . . . 70

4.2 Number of pathogenic variants found per referral cancer type . . . 73

4.3 Genes with pathogenic and likely pathogenic variants . . . 74

4.4 Screening for secondary findings against screening criteria . . . 78

5.1 TLA and benchmarking of results . . . 93

6.1 Coefficients of regression model chromosome 13 Illumina . . . 123

8.1 NIPTRIC Post-test probability summary table . . . 142

11.1 Properties of a complete DNA sequencing procedure . . . 187

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LIST OF FIGURES

List of Figures

1.1 Human genome variation types . . . 18

1.2 DNA Next-generation sequencing workflows . . . 22

1.3 Overview of the topics addressed in the thesis chapters . . . 27

2.1 Average coverage per exon cardiomyopathy 48 gene panel . . . 41

2.2 Coverage profile of single target LDB3 exon 9 . . . 42

2.3 Summary of the results of our confirmation analyses . . . 43

3.1 CoNVaDING workflow . . . 52

3.2 CoNVaDING match control group . . . 54

3.3 CNV detections CoNVaDING, XHMM, CoNIFER, and CODEX . . . . 60

4.1 Number of detected variants in the cohorts . . . 75

6.1 Flowchart NIPT analysis steps . . . 105

6.2 Effect of peak correction . . . 112

6.3 Comparison of the effect of two GC correction methods . . . 113

6.4 Effect of chi-squared-based variation reduction control samples CV. . 114

6.5 Effect of the different prediction algorithms . . . 114

6.6 Z-scores for three trisomies . . . 115

6.7 Match QC scores and Z-scores . . . 117

6.8 Example effect_χ2_{VR on bin counts . . . 120}

6.9 Example CV per bin with and without χ2VR . . . 120

6.10 Example Z-score normal distribution sum chi-squared value . . . 121

6.11 Example _χ2_{VR correction factor . . . 122}

6.12 Example Weighted read counts after _χ2_{VR . . . 122}

6.13 Relative fractions chromosome 21 before and afterχ2VR . . . 123

6.14 Example of regression model chromosome 13 . . . 124

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LIST OF FIGURES

6.15 Correlation between normalized read counts of chromosomes . . . 125

6.16 Ratios observed / predicted and Z-scores for chromosome 13 . . . 126

7.1 Workflow and functions of NIPTeR . . . 130

8.1 PPR at low and high risk . . . 141

8.2 PPR at different risks . . . 143

11.1 Workflow of laboratory procedures and variant detection . . . 184