Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

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A framework for variation discovery and genotyping using next-generation DNA sequencing data

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

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

Objective:To update and expand the 2013 Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the ICU.Design:Thirty-two international experts, four methodologists, and four critical illness survivors met virtually at least monthly. All section groups g

/pdf/clinical-practice-guidelines-for-the-prevention-and-fvgclilv6m.pdf

Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd
 . The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

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ANGSD: Analysis of Next Generation Sequencing Data

SNPs are highly abundant in the human genome, explaining most of sequence variation. This makes them a valuable resource for population genetics, evolution, and gene mapping. In this article we have given an overview of the major issues arising in their application to haplotype and haplotype block estimation and genetic association. The discussion should make clear that many statistical methods have been developed for these problems, but there is still much more to understand about the relative merits of the competing methods. Perhaps more important is further understanding of the practical utility of the methods.

Single Nucleotide Polymorphisms and Their Applications

Recent evidence suggests that complex traits are likely determined by multiple loci, each of which contributes a weak to moderate individual effect. Although extensive literature exists on multilocus analysis of unrelated subjects, there are relatively fewer strategies for jointly analyzing multiple loci using family data. Here we address this issue by evaluating two pseudosibship methods: the 1:1 matching, which matches each affected offspring to the pseudosibling formed by the alleles not transmitted to the affected offspring, and the exhaustive matching, which matches each affected offspring to the pseudosiblings formed by all the other possible combinations of parental alleles. We prove that the two matching strategies use exactly and approximately the same amount of information from data under additive and multiplicative genetic models, respectively. Using numerical calculations under a variety of models and testing assumptions, we show that compared with the exhaustive matching, the 1:1 matching has comparable asymptotic power in detecting multiplicative/additive effects in single-locus analysis and main effects in multilocus analysis, and it allows association testing of multiple linked loci. These results pave the way for many existing multilocus analysis methods developed for the case-control (or matched case-control) design to be applied to case-parents data with minor modifications. As an example, with the 1:1 matching, we applied an L1 regularized regression toa Crohn’s disease dataset.Using the multipleloci selected in our approach, we obtained an order-of-magnitude decrease in p-value and an 18.9% increase in prediction accuracy when compared with using the most significant individual locus. Copyright © 2011 John Wiley & Sons, Ltd.

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Pseudosibship methods in the case-parents design.

Statistical methods and challenges in connectome genetics

Importance N-glycan branching modulates cell surface receptor availability, and its deficiency in mice promotes inflammatory demyelination, reduced myelination, and neurodegeneration.N-acetylglucosamine (GlcNAc) is a rate-limiting substrate forN-glycan branching, but, to our knowledge, endogenous serum levels in patients with multiple sclerosis (MS) are unknown. Objective To investigate a marker of endogenous serum GlcNAc levels in patients with MS. Design, Setting, and Participants A cross-sectional discovery study and cross-sectional confirmatory study were conducted at 2 academic MS centers in the US and Germany. The discovery study recruited 54 patients with MS from an outpatient clinic as well as 66 healthy controls between April 20, 2010, and June 21, 2013. The confirmatory study recruited 180 patients with MS from screening visits at an academic MS study center between April 9, 2007, and February 29, 2016. Serum samples were analyzed from December 2, 2013, to March 2, 2015. Statistical analysis was performed from February 23, 2020, to March 18, 2021. Main Outcomes and Measures Serum levels of GlcNAc plus its stereoisomers, termedN-acetylhexosamine (HexNAc), were assessed using targeted tandem mass spectroscopy. Secondary outcomes (confirmatory study) comprised imaging and clinical disease markers. Results The discovery cohort included 66 healthy controls (38 women; mean [SD] age, 42 [20] years), 33 patients with relapsing-remitting MS (RRMS; 25 women; mean [SD] age, 50 [11] years), and 21 patients with progressive MS (PMS; 14 women; mean [SD] age, 55 [7] years). The confirmatory cohort included 125 patients with RRMS (83 women; mean [SD] age, 40 [9] years) and 55 patients with PMS (22 women; mean [SD] age, 49 [80] years). In the discovery cohort, the mean (SD) serum level of GlcNAc plus its stereoisomers (HexNAc) was 710 (174) nM in healthy controls and marginally reduced in patients with RRMS (mean [SD] level, 682 [173] nM;P = .04), whereas patients with PMS displayed markedly reduced levels compared with healthy controls (mean [SD] level, 548 [101] nM;P = 9.55 × 10−9) and patients with RRMS (P = 1.83 × 10−4). The difference between patients with RRMS (mean [SD] level, 709 [193] nM) and those with PMS (mean [SD] level, 405 [161] nM;P = 7.6 × 10−18) was confirmed in the independent confirmatory cohort. Lower HexNAc serum levels correlated with worse expanded disability status scale scores (ρ = –0.485;P = 4.73 × 10−12), lower thalamic volume (t = 1.7;P = .04), and thinner retinal nerve fiber layer (B = 0.012 [SE = 7.5 × 10−11];P = .008). Low baseline serum HexNAc levels correlated with a greater percentage of brain volume loss at 18 months (t = 1.8;P = .04). Conclusions and Relevance This study suggests that deficiency of GlcNAc plus its stereoisomers (HexNAc) may be a biomarker for PMS. Previous preclinical, human genetic, and ex vivo human mechanistic studies revealed thatN-glycan branching and/or GlcNAc may reduce proinflammatory responses, promote myelin repair, and decrease neurodegeneration. Combined, the data suggest that GlcNAc deficiency may be associated with progressive disease and neurodegeneration in patients with MS.

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Association of a Marker of N-Acetylglucosamine With Progressive Multiple Sclerosis and Neurodegeneration.

Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information. We propose a novel genotype calling framework. In this framework, we consider the signal intensities and underlying LD structure of SNPs simultaneously by estimating both cluster parameters and haplotype frequencies. As a result, our new method outperforms some existing algorithms in terms of both call rates and genotyping accuracy. Our studies also suggest that jointly analyzing multiple SNPs in LD provides more accurate estimation of haplotypes than haplotype reconstruction methods that only use called genotypes. Our study demonstrates that jointly analyzing signal intensities and LD structure of multiple SNPs is a better way to determine genotypes and estimate LD parameters.

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Zhaoxia Yu

Papers

Single Nucleotide Polymorphisms and Their Applications

Pseudosibship methods in the case-parents design.

Statistical methods and challenges in connectome genetics

Association of a Marker of N-Acetylglucosamine With Progressive Multiple Sclerosis and Neurodegeneration.

Genotype determination for polymorphisms in linkage disequilibrium