Stepwise threshold clustering: a new method for genotyping MHC loci using next-generation sequencing technology.

Area of Science:

• Genomics
• Immunogenetics
• Evolutionary Biology

Background:

• Vertebrate major histocompatibility complex (MHC) genes are crucial for immunity and disease resistance.
• High genetic diversity in MHC genes makes them vital for evolutionary studies.
• Next-generation sequencing (NGS) is increasingly used for high-throughput MHC genotyping in non-model organisms.

Purpose of the Study:

• To introduce a novel method, Stepwise Threshold Clustering (STC), for accurate MHC genotyping using NGS data.
• To address limitations of previous NGS-based MHC genotyping methods, specifically the "gray zone" and similar sequence problems.
• To provide a reliable and efficient tool for analyzing MHC genetic diversity.

Main Methods:

• Developed and applied the Stepwise Threshold Clustering (STC) algorithm.
• Utilized a quasi-Dirichlet clustering approach to group similar sequences at varying similarity thresholds.
• Applied frequency and similarity criteria to sequence clusters instead of individual sequences.
• Validated the STC method on a single sample library and then on 295 threespine stickleback samples from four populations.

Main Results:

• The STC method successfully distinguishes low-frequency alleles and resolves similar sequences, overcoming previous genotyping challenges.
• STC does not require duplicate sample runs, increasing throughput and efficiency.
• Analysis of threespine stickleback revealed significant differences in MHC allele pools between neighboring populations, highlighting population structure.

Conclusions:

• Stepwise Threshold Clustering (STC) is a reliable, accurate, efficient, and flexible method for genotyping MHC genes using NGS data.
• STC enhances the ability to study genetic diversity and population structure in MHC genes.
• This method is broadly applicable to various downstream biological research areas.