New SciDAP Workflow: Germline Variant Calling pipeline with GATK4

Germline variant calling focuses on identifying genetic variations that are present in an individual’s germ cells, specifically in the DNA inherited from their parents. These variants are present in every cell of an individual’s body and, importantly, can be passed on to their offspring. Germline variant calling is primarily used in applications such as population genetics, disease association studies, and genetic counseling.

Example of IGV visualization of variants and read alignments within SciDAP.

Background

The primary goal of germline variant calling is to identify genetic variants that are present in an individual’s germline cells (sperm or eggs) and are therefore inherited and present in every cell of the individual’s body. These variants can contribute to an individual’s susceptibility to diseases, inherited conditions, and other genetic traits. Detecting them typically involves analyzing DNA samples derived from blood, saliva, or other sources which represent the individual’s inherited genetic information.

Highlighting the differences between somatic and germline DNA, https://www.bcgsc.ca/news/tumor-sequencing-panel-screens-both-somatic-and-germline-changes-clinical-significance [1]

Comparison to Somatic Variant Calling

Contrasting germline with somatic variant calling, the latter aims to identify genetic variants that are specific to a particular subset of cells within an individual’s body, usually arising due to mutations occurring after conception. Somatic variants can be associated with the development of cancer, as well as other spontaneous diseases. Somatic variant calling involves comparing DNA samples derived from tumor tissues with normal samples [2]. Tumor tissues may contain somatic mutations that are absent in normal tissues from the same individual.

Approaches for Studying Germline Variants

Germline variant calling often utilizes strategies such as local de novo assembly or read-based variant calling to identify genetic variants present in germline cells. These approaches focus on identifying germline-specific variants and distinguishing them from somatic polymorphisms. These pipelines typically incorporate quality filters and population frequency databases to distinguish true germline variants from sequencing artifacts or common polymorphisms thus minimizing false-positive variant calls. Once polymorphisms have been identified and filtered, they are then annotated and their functional effect may be predicted.

Germline Variant Analysis

The accurate identification and characterization of genetic variations in an individual’s genome are crucial for understanding the genetic basis of diseases and personalized medicine. The Genome Analysis Toolkit (GATK) is a widely used software package developed by the Broad Institute to facilitate the analysis of high-throughput sequencing data [3]. GATK4, the latest version of the toolkit as of this writing, leverages advanced algorithms and best practices to deliver reliable and high-quality results, and introduces several improvements and features to enhance germline variant calling.

The pipeline begins with the pre-processing of raw sequencing data, including read alignment and duplicate marking. GATK4 incorporates the HaplotypeCaller, a tool specifically designed for germline variant calling, which employs a sophisticated local de novo assembly approach to identify genetic variants. This technique allows the tool to accurately distinguish between true genetic variations and sequencing artifacts or errors. The pipeline also incorporates best practices for base quality score recalibration (BQSR). This step involves statistical modeling to recalibrate the quality scores assigned to each variant, allowing for more accurate differentiation between true variants and sequencing artifacts. Together, these steps enable researchers and clinicians to obtain reliable and biologically meaningful variant calls, ultimately advancing our understanding of human genetics and its role in health and disease.

Strategic plan workflow for the Genome Analysis Toolkit (GATK) best practices pipeline, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243306/ [3].

The SciDAP Solution for Germline Variant Calling

See the tutorial video on youtube:

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SciDAP offers the Broad Institute’s best practices for the GATK4 germline variant calling pipeline based on open-source packages to make data analysis faster and easier for the biologist [4]. The workflow begins with adapter trimming raw Illumina reads in FASTQ format, then mapping the trimmed reads against a reference genome using BWA-MEM [5]. Mapped reads are then deduplicated with Picard and alignment and duplication metrics generated to show the user the proportion of mapped reads which can be a proxy for library preparation efficiency [6]. Next, GATK4 is used to call variants and if a region of high variability is identified, local reassembly of haplotypes is performed for more accurate variant calls. SNPs and indels are then extracted and filtered for quality. After this first pass, BQSR is performed without any additional user input and variants are called a second time. Finally, SnpEff is used to predict the effects of detected variants such as amino acid changes, frame shifts, etc [7].

Example output of summary impact and functional class effect, available in the SnpEff summary report.

Outputs include insert size distribution and base frequency and quality plots for QC, an overview of various inputs and read, alignment, and variant counts, the SnpEff summary report PDF, and bam tracks are made available in IGV for viewing and browsing. Annotated VCF files are the primary output files of the germline variant calling pipeline. These contain all filtered variant calls along with SnpEff annotations that include variant type, impact on the gene it overlaps or is nearest to, as well as a predicted “impact severity” based on its potential for functional consequences (above images).

Now biologists can easily perform germline variant analysis without learning to code, running command line tools, or running commands in R. With the SciDAP Germline Variant Calling workflow, you can move beyond the command line to easily generate VCF files for further analyses. Try it for yourself with a FREE trial.

References

1. Lau TTY, et al. Use of Treatment-Focused Tumor Sequencing to Screen for Germline Cancer Predisposition. J Mol Diagn. 2021 Sep;23(9):1145–1158. doi: 10.1016/j.jmoldx.2021.06.006. Epub 2021 Jun 29. PMID: 34197922.
2. Roth, A. et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. https://doi.org/10.1093/bioinformatics/bts053. Bioinformatics 28, 907–913 (2012).
3. Van der Auwera GA, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43(1110):11.10.1–11.10.33. doi: 10.1002/0471250953.bi1110s43. PMID: 25431634; PMCID: PMC4243306.
4. Mohammed Khalfan. Variant Calling Pipeline using GATK4. Genomics Core at NYU CGSB. March 25, 2929. https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/
5. Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v2 [q-bio.GN].
6. “Picard Toolkit.” 2018. Broad Institute, GitHub Repository. http://broadinstitute.github.io/picard/; Broad Institute
7. “A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.”, Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. Fly (Austin). 2012 Apr-Jun;6(2):80–92. PMID: 227286