Best Practices in Germline Variant Analysis – Second- and Third-Generation Sequencing

Over the past 20 years, sequencing technologies used in genetic analysis have advanced beyond the Sanger method, ushering in the era of "second generation sequencing" (SGS). This approach enables billions of short DNA sequences to be sequenced simultaneously. Third generation sequencing (TGS) is now revolutionizing genomic analysis by reading much longer DNA sequences.

SGS and TGS play important roles in detecting and analyzing germline variants.

Germline variants are genetic changes present in all of an individual's cells that can be passed from generation to generation through reproductive cells (sperm or eggs). They may contribute to inherited diseases and shape an individual's genetic makeup.

These variants are critical to diagnosing inherited diseases and developing personalized treatments. Despite the extensive possibilities offered by these technologies, accurate results require adherence to standards and best practices at every step of analysis.

On 5 November 2024, Human Genomics published an important article on this subject. The comprehensive review examines best practices for germline variant and DNA methylation analyses using SGS and TGS, aiming to provide detailed guidance for researchers and physicians.

DNA methylation studies aim to understand how gene expression is regulated by examining epigenetic changes resulting from the addition of methyl groups to genetic material. These studies play important roles in disease diagnosis, biomarker discovery, and understanding mechanisms of genetic regulation.

SGS (Second Generation Sequencing) and TGS (Third Generation Sequencing) represent two stages of the broader technological advances known as "Next Generation Sequencing" (NGS).

1. Next Generation Sequencing (NGS):
   This term encompasses sequencing methods that are faster, less costly, and higher-throughput than traditional Sanger sequencing. Both SGS and TGS are subcategories of NGS.

2. Second Generation Sequencing (SGS):

   • SGS refers to "second-generation" sequencing technology, which generally achieves high accuracy by reading short DNA sequences in parallel.
   • Examples: Illumina and Ion Torrent platforms.
   • The data generally consist of short reads (50-300 base pairs). These methods are particularly strong at detecting small genetic changes, such as SNVs and InDels.

3. Third Generation Sequencing (TGS):

   • TGS refers to "third-generation" technologies, which can read much longer stretches of DNA.
   • Examples: PacBio (HiFi) and Oxford Nanopore.
   • These methods excel at resolving more complex genomic structures, such as structural variants (SVs) and repetitive DNA sequences. They can also detect epigenetic modifications directly.

In summary, SGS and TGS are two branches of NGS, each with advantages and limitations for specific genomic analysis needs. TGS overcomes limitations of SGS, providing new capabilities such as longer reads and epigenetic data analysis.

Germline Variant Analysis with SGS and TGS

1. SGS Technology and Data Processing

SGS enables parallel sequencing of short DNA fragments and generally provides high accuracy. Second-generation sequencing involves the following steps:

• Data alignment: DNA sequences are aligned to the reference genome.
• Quality control: Data must be cleaned and filtered to minimize sequencing errors.
• Variant calling: Single-nucleotide variants (SNVs), insertions, and deletions (InDels) are identified in the DNA sequence.
• Variant annotation: The functional effects of identified variants are assessed.

Prominent tools for processing SGS data include BWA-MEM, GATK, and DeepVariant. These programs detect and classify variants with high sensitivity.

2. TGS Technology: The Advantages of Long Reads

TGS overcomes limitations of SGS by reading much longer stretches of DNA. It is particularly advantageous for analyzing structural variants (SVs) and repetitive DNA sequences.

• PacBio: Provides highly accurate long reads (HiFi) and directly detects epigenetic modifications.
• Nanopore: Notable for reading long DNA sequences and, theoretically, operating without a DNA length limit.

TGS makes an important contribution, particularly to diagnosing rare genetic diseases and filling gaps in the genome.

3. HLA Genotyping and Analysis of Homozygous Regions

• HLA Genotyping: The highly polymorphic HLA region cannot be analyzed using standard variant-calling methods. Specialized algorithms such as HLAminer, OptiType, and xHLA are therefore used.
• Regions of Homozygosity (ROH): ROH analysis is particularly important for identifying autosomal recessive disease genes. Programs such as H3M2 and BCFTools are recommended for analyzing ROH in SGS and TGS data.

DNA Methylation Analysis

1. The Importance of Epigenetic Regulation

DNA methylation (DNAm) is an epigenetic modification involved in regulating gene expression. Methylation profiles at CpG islands provide distinctive biomarkers for many genetic diseases and cancer types.

2. Methylation Analysis Methods

• Sodium Bisulfite-Based Methods: Considered the gold standard, these methods determine methylation status at CpG sites.
• Sequencing-Based Methods: Techniques such as WGBS and RRBS analyze genome-wide methylation profiles at high resolution.
• Methylation Analysis with TGS: Directly detects modified bases, removing the need for chemical treatment of DNA.

3. Data Analysis and Tools

Tools such as MethylKit, DSS, and RnBeads can process both sequencing and methylation microarray data. They identify differentially methylated regions (DMRs) and perform functional enrichment analyses to interpret their biological significance.

Conclusions and Recommendations

This review comprehensively addresses the opportunities and challenges of SGS and TGS technologies in germline variant and DNA methylation analysis.

• Quality control: Strict quality control should be applied throughout the process to minimize sequencing errors.
• Up-to-date software: Tools such as BWA-MEM, DeepVariant, and MethylKit are recommended to improve analysis accuracy.
• Best practices: Standardizing genetic and epigenetic analyses improves diagnostic accuracy and enables personalized treatment strategies to be developed.

Genomic and epigenetic data obtained by combining SGS and TGS are driving breakthroughs in human genetics and molecular medicine. Widespread adoption of these technologies will open new opportunities for early diagnosis and targeted treatment.