A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

By Heng Li. Bioinform., 2011

6,134 citations recorded by the source corpus.

MOTIVATION Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. RESULTS We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. AVAILABILITY http://samtools.sourceforge.net. CONTACT hengli@broadinstitute.org.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/12b0e1d632b523b10de540cd3e66503653d83024

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In Genetics

Citations in the corpus

pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, methodology): “After alignments (and optional HLA typing) were completed, somatic mutation detection was performed using the following series of steps (Additional file 1: Figure S1): (1) Samtools [30, 31] mpileup v0.”
Evidence for Weak Selective Constraint on Human Gene Expression (2018, methodology): “Allele frequencies in gnomAD were extracted using bcftools (Li 2011).”
Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel (2014, methodology): “These GLs can be obtained using specialised software like SAMtools 14 , SNPtools 15 or GATK 16 that derive these likelihoods directly from the BAM ﬁles containing the sequence reads.”
An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data (2015, methodology): “GotCloud uses a modified version of SAMtools to generate genotype likelihoods for all 10 possible diploid SNP genotypes and store them in GLF format (Li 2011).”
Major impacts of widespread structural variation on gene expression and crop improvement in tomato (2020, positive): “0), SNPs were called with samtools/bcftools (Li, 2011; Li et al., 2009) using read alignments for the two genomic DNA sequencing pools in addition to the M82 (Bolger et al., 2014a) and Fla.8924 (Lee et al., 2018) parents.”
Deriving genotypes from RAD-seq short-read data using Stacks (2017, methodology): “Methods oriented toward whole-genome shotgun sequencing data include SAMtools’ mpileup 29 , HaplotypeCaller 30 , FreeBayes 31 , and ANGSD 19 .”
The Next-Generation Sequencing Revolution and Its Impact on Genomics (2013, neutral): “Most of these tools currently deal with trios (mother, father, and child) only and can identify de novo variants arising in the children (Cartwright et al., 2012; http://sourceforge.net/p/denovogear/wiki/Home/; Li et al., 2012; Li, 2011).”
Estimating Individual Admixture Proportions from Next Generation Sequencing Data (2013, methodology): “Li, H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan et al., 2009 The Sequence Alignment/Map format and SAMtools.”