Clinical-Genomics

Introduction

Clinical genomics analysis refers to the application of genomic technologies and computational methods to understand the genetic basis of diseases and inform clinical decision-making. It involves the analysis of genomic data obtained from patients to identify genetic variations, mutations, and other molecular features that may contribute to disease development or progression. The goal is to use this information to guide diagnosis, treatment decisions, and personalized medicine approaches.

Prerequisites:

Before we begin, please have all the files from the GATK Resource Bundle and keep all the files downloaded in one folder (let’s name the folder resource). We can create a folder using the mkdir command. All the tools and analysis steps shown below are for a Linux sytem. For macOS or WSL in Windows, we need to make some minor modifications in the commands.

Installation of tools ( follow the manual page of each tool to install them properly):

• Docker
• Java (version >= 17) : Picard and GATK only work with Java version >= 17, this is only required in case your docker installation did not work. If you were able to successfully install docker, then you can pull the GATK docker container and you would not require require Java (version >= 17) . Follow this tutorial for step-by-step guidance :

  apt-cache search openjdk | grep openjdk-17
  sudo apt install openjdk-17-jre
  sudo apt install openjdk-17-jdk
  

• FastQC (requires Java version >= 11)
• TrimGalore/Fastp
• BWA
• samtools
• tabix
• R
• python3

  sudo apt install python3
  sudo apt install python3-pip
  

• picard
• snpEff/SnpSift

Running GATK on Docker :

docker run -v /path/to/demo_data_set:/gatk/data -it broadinstitute/gatk:4.4.0.0

A step-by-step guide to Clinical Genomics analysis:

1. Pre-processing reads:

Pre-processing the reads involves checking the quality (Phred-scores) using tools like FASTQC or MultiQC. After that, we need to remove adapter contamination and trim quality reads using either one of the following tools : fastp, cutadapt, TrimGalore or Trimmomatic.

The following code can be used to check the quality of reads obtained from the sequencer.

Running FastQC

mkdir fastq_bt  # bt stands for before trimming
#Running FASTQC
fastqc -o fastq_bt gatk_demo1.fastq.gz gatk_demo2.fastq.gz

Running FASTP

fastp -i gatk_demo1.fastq.gz -I gatk_demo2.fastq.gz -o gatk_demo1_trimmed.fastq.gz -O gatk_demo2_trimmed.fastq.gz \
       -R gatk_demo -h gatk_demo.html -j gatk_demo.json --detect_adapter_for_pe

In case you want to use cutadapt or trimgalore ( which is essentially a wrapper around cutadapt, 

Running FastQC again after Adapter and Quality Trimming

mkdir fastq_at  # at stands for after trimming
#Running FASTQC
fastqc -o fastq_at gatk_demo1_trimmed.fastq.gz gatk_demo2_trimmed.fastq.gz

2. Aligning reads to reference genome:

Aligning sequencing reads to a reference genome is a crucial step in many bioinformatics workflows, including variant calling and downstream genomic analyses. The Burrows-Wheeler Aligner BWA is a popular tool for aligning short DNA sequences to a large reference genome efficiently. Before performing the alignment, we first need to index the reference genome.

Indexing the reference genome

cd resource
bwa index Homo_sapiens_assembly38.fasta 

Note: Since the Homo sapiens reference genome is very huge in size (3GB), the indexing process can take anywhere between 45min to 2hrs, depending on your system configurations. Alternatively, if you have downloaded all the files in the GATK resource bundle folder, then the respective index files from BWA are already downloaded, and you do not need to index your reference genome again.

Alignment to the indexed reference genome

bwa mem Homo_sapiens_assembly38.fasta gatk_demo1_trimmed.fastq.gz gatk_demo2_trimmed.fastq.gz > demo.sam

3. Alignment Post-Processing:

After aligning your sequencing reads to a reference genome using BWA, there are several post-processing steps you may want to perform to analyze and manipulate the alignment results. For this purpose, we will be utilising the Picard tool. Picard is a collection of command-line tools for manipulating high-throughput sequencing (HTS) data files, such as SAM and BAM files. These tools are developed by the Broad Institute and are widely used in genomics and bioinformatics workflows. Picard tools are often employed for quality control, data preprocessing, and various manipulations of sequence data. Here are some common post-processing steps:

• 3.1 Adding one or more read groups to your SAM file: The AddOrReplaceReadGroups tool in Picard is used to add or replace read group information in a SAM or BAM file. This is important for downstream applications that require proper grouping and identification of reads, especially when dealing with data from multiple sequencing libraries or samples. Even though for this demo data purpose, we have a single sample bam file, still this step is absolutely mandatory for the GATK pipeline! The GATK Pipeline requires at least one ReadGroup id to be specified in the Here’s an example of how to use the AddOrReplaceReadGroups tool:

  java -jar picard.jar AddOrReplaceReadGroups \
         I=demo.sam \
         O=demo_rg.sam \
         RGID=ID123 \
         RGLB=library1 \
         RGPL=illumina \
         RGPU=unit1 \
         RGSM=sample1
  
  

  Note: For those of you who are running GATK in Docker, you dont need to install picard separately. All the functionalities of picard are already present within GATK, check out the script (clinical_genomics_demo.sh) for clarity. However, those of you who will have downloaded the GATK https://github.com/broadinstitute/gatk/releases/download/4.4.0.0/gatk-4.4.0.0.zip. Make sure you have Java (v.>= 17) installed, and then you can execute the Picard command as shown in the example. Additionally, specify the full path to the picard.jar file in the command, depending on your system configuratio

• 3.2 SAM to BAM conversion: The SamFormatConverter tool in Picard can be used to convert a SAM file to BAM format. Here’s an example of how to use SamFormatConverter for this purpose:

  java -jar picard.jar SamFormatConverter \
         I=demo_rg.sam\
         O=demo_rg.bam
  
  

• 3.3 Sorting BAM file: The SortSam tool in Picard is used to sort a SAM or BAM file by coordinate order. Sorting is a necessary step for downstream analyses and visualization tools that require data to be ordered by genomic coordinates. Here’s an example of how to use SortSam:

  java -jar picard.jar SortSam \
         I=demo_rg.bam \
         O=demo_sorted.bam \
         SORT_ORDER=coordinate
  
  

• 3.4 Marking (and optionally deleting) duplicates: In Picard, the MarkDuplicates tool is used to identify and mark duplicate reads in a SAM or BAM file. Marking duplicates is a common step in the preprocessing of sequencing data, especially when dealing with PCR-amplified libraries. Here’s an example of how to use MarkDuplicates:

  java -jar picard.jar MarkDuplicates \
      I=demo_sorted.bam \
      O=demo_sorted_md.bam\
      M=marked_dup_metrics.txt
  

In case we want to remove the duplicated reads the command goes as below:

  java -jar picard.jar MarkDuplicates \
      I=demo_sorted.bam \
      O=demo_sorted_dedup.bam\
      M=marked_dup_metrics.txt
       --REMOVE_DUPLICATES

4. Base Quality score recalibration (not mandatory, but highly recommended) :

Base Quality Score Recalibration (BQSR) is primarily used to improve the accuracy of base quality scores assigned to individual nucleotides in the sequencing data. In NGS, the quality scores associated with each base in a sequence represent the estimated probability of an incorrect base call. However, these scores are not always perfectly accurate, and recalibration is performed to correct for systematic errors and biases in the sequencing data. The general process of Base Quality Score Recalibration involves the following steps:

• Collecting Metrics: The first step involves collecting various metrics from the sequencing data, including the actual base calls, base quality scores, and additional information such as machine cycle, base context, and sequence context.

• Model Training: A statistical model is then trained using the collected metrics to estimate the true error probability for each base call. The model takes into account various factors that can influence the accuracy of base calls, such as the quality scores of neighboring bases, machine-specific biases, and sequence context.

• Recalibration: Based on the trained model, the base quality scores are recalibrated to reflect a more accurate estimate of the true error probability. This recalibration involves adjusting the original quality scores assigned during the sequencing process.

• Quality Control: After recalibration, quality control metrics are often generated to assess the success of the recalibration process. These metrics help ensure that the recalibrated data meets certain quality standards. In the GATK pipeline, we can perform BQSR using two commands : BaseRecalibrator and ApplyBQSR. BaseRecalibrator builds a machine learning model on the basis of known variant sites and generates a recalibration table on the statistical accuracy of sequencer-predicted base quality scores.

gatk BaseRecalibrator \
       -I demo_sorted_dedup.bam \
       -R resource/Homo_sapiens_assembly38.fasta \
       --known-sites resource/Homo_sapiens_assembly38.known_indels.vcf.gz \
       --known-sites resource/Homo_sapiens_assembly38.dbsnp.vcf.gz \
       -O recal_data.table

ApplyBQSR then applies the recalibration table generated in the previous step to recalibrate the base quality scores of the processed bam file.

gatk ApplyBQSR \
       -I demo_sorted_dedup.bam \
       -R resource/Homo_sapiens_assembly38.fasta \
       --bqsr-recal-file recal_data.table \
       -O demo_sorted_dedup_recal.bam

5. Variant Calling:

Variant calling is a crucial step in clinical genomics analysis, where the goal is to identify genetic variations (variants) such as single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants in a patient’s genome. These variations can be associated with diseases, provide insights into individual susceptibility to certain conditions, and guide treatment decisions.

The GATK Variant Calling pipeline employs 2 SNV callers : HaplotypeCaller and Mutect2. HaplotypeCaller is used for germline variant calling, while Mutect2 is specialized for somatic variant calling in cancer samples. For this tutorial we will be focussing on HaplotypeCaller for the calling the variants from clinical genomic data.

gatk HaplotypeCaller \
       -I demo_sorted_dedup_recal.bam \
       -O unfiltered.vcf \
       -R resource/Homo_sapiens_assembly38.fasta \
       --dbsnp resource/Homo_sapiens_assembly38.dbsnp.vcf.gz \
       --annotation AlleleFraction \
       --annotation VariantType

6. Variant Filtering:

Variant Filtering is necessary to obtain high confidence variants from the list obtained from the variant calling step. There are two ways to filter variants in the GATK pipeline : (i) Hard-Filtering and (ii) Variant Quality Score Recalibration.

(i) Hard Filtering:

GATK provides recommended hard filtering criteria as an alternative to VQSR. This involves setting specific thresholds for certain variant quality metrics. Common filters include read depth (DP), variant allele fraction (AF), strand bias, and mapping quality.

(ii) Variant Quality Score Recalibration (VQSR):

This step involves applying machine learning to the variant call set to recalibrate variant quality scores based on multiple features such as depth of coverage, mapping quality, and strand bias.A model is trained using known variants and is then applied to the dataset to filter variants based on the recalibrated scores.

• 6.1 Splitting variants into SNPs and INDELs (optional): The variants called during the HaplotypeCaller step can be split into separate vcf files containing SNPs and InDels each, using the SelectVariants command.

gatk SelectVariants \
       -V unfiltered.vcf \
       -R resource/Homo_sapiens_assembly38.fasta \
       -O unfiltered_snp.vcf \
       -select-type SNP

gatk SelectVariants \
       -V unfiltered.vcf \
       -R resource/Homo_sapiens_assembly38.fasta \
       -O unfiltered_indel.vcf \
       -select-type INDEL

• 6.2 Hard Filtering Variants:

Hard filtering is an alternative approach to variant quality score recalibration (VQSR) in the Genome Analysis Toolkit (GATK) pipeline. Instead of using a machine learning model to recalibrate variant quality scores, hard filtering involves setting specific threshold values for various variant quality metrics. Variants failing these thresholds are then filtered out. Hardfiltering is carried out using the VariantFiltration command.

gatk VariantFiltration \
	-V unfiltered.vcf \
	-filter "QD < 2.0" --filter-name "QD2" \                                                                  
	-filter "QUAL < 30.0" --filter-name "QUAL30"     \
	-filter "SOR > 3.0" --filter-name "SOR3"     \
	-filter "FS > 60.0" --filter-name "FS60"     \
	-filter "MQ < 40.0" --filter-name "MQ40" \
	-O hardfiltered.vcf

• 6.3 Variant Quality Score Recalibration: Variant Quality Score Recalibration (VQSR) is a critical step in the Genome Analysis Toolkit (GATK) pipeline for identifying high-confidence variants in high-throughput sequencing data. The purpose of VQSR is to recalibrate the variant quality scores assigned by variant calling algorithms to improve the accuracy of variant calls. The general process involves training a machine learning model using known variant sites and then applying this model to score variants in the dataset. Here are the key steps for Variant Quality Score Recalibration in GATK:

• Generate a Training Set of Variants:

Create a set of known variant sites that can be used to train the VQSR model. This set can come from a high-quality variant database, such as dbSNP or the 1000 Genomes Project.

• VariantRecalibrator:

Use the VariantRecalibrator tool to train the model. This involves providing the training set of variants, as well as a set of features that describe the variants, such as allele frequency, depth of coverage, mapping quality, and more. GATK assigns a Gaussian mixture model to the training data, modeling the distribution of variant quality scores for true variants and non-variants. In this example, features like DP (depth of coverage), QD (variant quality by depth), FS (FisherStrand), SOR (StrandOddsRatio), MQ (mapping quality), and various rank sum tests are used.

• ApplyRecalibration:

Apply the learned model to the original variant calls using the ApplyVQSR tool. This assigns new, recalibrated quality scores to each variant.

gatk --java-options "-Xmx4g -Xms4g" VariantRecalibrator \
	-V unfiltered.vcf \
	--trust-all-polymorphic \
	-tranche 100.0 -tranche 99.95 -tranche 99.9 -tranche 99.5 -tranche 99.0 -tranche 97.0 -tranche 96.0 \
	-tranche 95.0 -tranche 94.0 -tranche 93.5 -tranche 93.0 -tranche 92.0 -tranche 91.0 -tranche 90.0 \
	-an FS -an ReadPosRankSum -an MQRankSum -an QD -an SOR -an DP \
	-mode INDEL \
	--max-gaussians 4 \
	-resource:mills,known=false,training=true,truth=true,prior=12 resource/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz \
	-resource:axiomPoly,known=false,training=true,truth=false,prior=10 resource/Axiom_Exome_Plus.genotypes.all_populations.poly.hg38.vcf.gz \
	-resource:dbsnp,known=true,training=false,truth=false,prior=2 resource/Homo_sapiens_assembly38.dbsnp138.vcf \
	-O unfiltered_indel.recal \
	--tranches-file unfiltered_indel.tranches

gatk --java-options "-Xmx4g -Xms4g" ApplyVQSR \
	-V hardfilter_PASS.vcf \
	--recal-file unfiltered_indel.recal \
	--tranches-file unfiltered_indel.tranches \
	--truth-sensitivity-filter-level 99.7 \
	--create-output-variant-index true \
	-mode INDEL \
	-O unfiltered_indel_recal.vcf
gatk --java-options "-Xmx3g -Xms3g" VariantRecalibrator \
	-V unfiltered_indel_recal.vcf \
	--trust-all-polymorphic \
	-tranche 100.0 -tranche 99.95 -tranche 99.9 -tranche 99.8 -tranche 99.6 \
	-tranche 99.5 -tranche 99.4 -tranche 99.3 -tranche 99.0 -tranche 98.0 -tranche 97.0 -tranche 90.0 \
	-an QD -an MQRankSum -an ReadPosRankSum -an FS -an MQ -an SOR -an DP \
	-mode SNP \
	--max-gaussians 6 \
	-resource:hapmap,known=false,training=true,truth=true,prior=15 resource/hapmap_3.3.hg38.vcf.gz \
	-resource:omni,known=false,training=true,truth=true,prior=12 resource/1000G_omni2.5.hg38.vcf.gz \
	-resource:1000G,known=false,training=true,truth=false,prior=10 resource/1000G_phase1.snps.high_confidence.hg38.vcf.gz \
	-resource:dbsnp,known=true,training=false,truth=false,prior=7 resource/Homo_sapiens_assembly38.dbsnp138.vcf \
	-O unfiltered_snp.recal \
	--tranches-file unfiltered_snp.tranches \
	--rscript-file unfiltered_snp.R
gatk --java-options "-Xmx4g -Xms4g" ApplyVQSR \
	-V unfiltered_indel_recal.vcf \
	--recal-file unfiltered_snp.recal \
	--tranches-file unfiltered_snp.tranches \
	--truth-sensitivity-filter-level 99.7 \
	--create-output-variant-index true \
	-mode SNP \
	-O unfiltered_recalibrated.vcf

7. Variant Annotation:

Variant annotation is a crucial step in the analysis of genomic variants, providing additional information about the potential functional consequences, known associations, and other relevant details of genetic variations. This process helps researchers and clinicians interpret the biological impact of identified variants. Here are the key components of variant annotation:

Functional Annotation:

Variant Effect Predictor (VEP): VEP is a widely used tool for annotating genomic variants. It provides information on the consequences of variants, such as whether they are non-synonymous mutations, affect splice sites, or have other functional implications.

*Population Frequency Databases:

dbSNP: The Single Nucleotide Polymorphism Database (dbSNP) is a central repository for cataloging human genetic variations. It provides information on the frequency of variants in different populations.

gnomAD (Genome Aggregation Database): gnomAD aggregates variant data from exome and genome sequencing projects, providing population-level allele frequencies.

Clinical Databases:

• ClinVar: ClinVar is a public database that archives and aggregates information about the relationships among human variations and phenotypes. It includes data on the clinical significance of variants.

• HGMD (Human Gene Mutation Database): HGMD is a comprehensive collection of germline mutations associated with human genetic diseases.

Pathogenicity Prediction Tools:

• SIFT, PolyPhen-2, CADD: These tools predict the potential pathogenicity of missense variants by considering factors such as amino acid conservation, protein structure, and evolutionary conservation.

Conservation Scores:

• PhyloP, GERP++: These tools provide scores indicating the evolutionary conservation of genomic regions, helping assess the potential impact of variants in conserved regions. Functional Element Annotations:

• ENCODE (Encyclopedia of DNA Elements): ENCODE provides annotations for functional elements in the genome, such as transcription factor binding sites, histone modifications, and open chromatin regions.

Disease-Specific Databases:

Depending on the focus of the study, researchers may use disease-specific databases that provide information on variants associated with particular conditions.

Gene Ontology (GO) Analysis:

GO analysis helps understand the biological functions and processes associated with genes affected by variants.

Pathway Analysis:

Analyzing the pathways enriched with genes harboring variants can provide insights into the biological context.

Custom Annotations:

Researchers may include custom annotations relevant to their specific study, such as information about regulatory elements, known functional domains, or experimentally validated functional effects.

8. Clinical Interpretation:

The American College of Medical Genetics and Genomics (ACMG) has established guidelines to standardize the interpretation of genomic variants, particularly for clinical diagnostic purposes. These guidelines provide a framework for assessing the pathogenicity of genetic variants and help clinicians make informed decisions about patient care. The ACMG guidelines were last updated in 2015, and it’s advisable to refer to the latest versions or updates for the most current recommendations. Here’s an overview of the key principles in the ACMG guidelines:

Variant Classification:

Variants are classified into five categories based on their pathogenicity:

• Pathogenic: A variant that is expected to cause disease.
• Likely Pathogenic: A variant that probably causes disease.
• Uncertain Significance: The evidence is not sufficient to classify the variant as pathogenic or benign.
• Likely Benign: A variant that probably does not cause disease.
• Benign: A variant not expected to cause disease.

Evidence Criteria:

The ACMG guidelines provide a structured set of criteria to evaluate evidence supporting pathogenicity. These criteria include population data, computational predictions, functional studies, segregation data, and de novo occurrence.

Population Databases:

Population databases, such as gnomAD, are used to assess the frequency of a variant in the general population. Variants with high frequencies are less likely to be pathogenic.

Functional Predictions:

Computational tools, like SIFT, PolyPhen-2, and CADD, are used to predict the functional impact of missense variants.

Segregation Data:

Information about the presence or absence of the variant in affected and unaffected family members helps assess whether the variant co-segregates with the disease phenotype.

De Novo Occurrence:

The occurrence of a variant as a de novo mutation in a patient with no family history of the disorder is considered strong evidence for pathogenicity.

Multiple Lines of Evidence:

The guidelines emphasize the importance of considering multiple lines of evidence when classifying variants. No single criterion should be considered definitive.

Applying ACMG Criteria:

A systematic approach is recommended for applying the ACMG criteria, considering each piece of evidence and reaching an overall classification based on the collective evidence.

Reporting and Communication:

Clinical laboratories providing genetic testing services are encouraged to report variants according to ACMG standards, clearly indicating the classification, evidence considered, and potential implications for patient management.

Periodic Reassessment:

Variants classified as “Uncertain Significance” should be periodically reassessed as new evidence becomes available. It’s important to note that the interpretation of genomic variants is a dynamic field, and advancements in genomics research may lead to updates in guidelines. The ACMG guidelines provide a valuable framework, but clinical interpretation often requires expertise from a multidisciplinary team, including geneticists, genetic counselors, and clinicians, to consider the specific clinical context and patient information. Additionally, ethical considerations and patient consent are integral aspects of genetic testing and reporting.