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Updated: May 3, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data
Yun Li1, Wei Chen2, Eric Yi Liu3
1Department of Genetics, University of North Carolina, Chapel Hill, NC 27599-7264, USA. Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7264, USA. Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-7264, USA.
Abstract:
Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.
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Single Nucleotide Polymorphisms-SNPs
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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GWAS does not require the identification of the target gene involved in...
Sanger Sequencing

