方法对比

	贝叶斯变异检测	贝叶斯全基因组关联研究 (Bayesian GWAS)
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009	2007–2009 (formal statistical framework)
提出者	Mark DePristo, Eric Banks, and the Broad Institute GATK team	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
类型	Probabilistic genomic inference pipeline	Statistical genetic association analysis
开创性文献	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
别名	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
相关	6	5
摘要	Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based callers, Bayesian approaches explicitly model sequencing error, mapping uncertainty, and prior genotype frequencies to produce calibrated genotype likelihoods that can be used for downstream filtering and association testing.	Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a continuous scale, and supports principled fine-mapping of causal variants within associated loci. It is widely used in complex trait genetics, population genomics, and translational research where uncertainty quantification and multi-variant modeling matter.