Christopher Hartl

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

Recent advances in sequencing technology make it possible to comprehensively catalog 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 (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.

This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high‐quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data‐processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK. Curr. Protoc. Bioinform. 43:11.10.1‐11.10.33.

Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism

Autism spectrum disorder (ASD) involves substantial genetic contributions. These contributions are profoundly heterogeneous but may converge on common pathways that are not yet well understood. Here, through post-mortem genome-wide transcriptome analysis of the largest cohort of samples analysed so far, to our knowledge, we interrogate the noncoding transcriptome, alternative splicing, and upstream molecular regulators to broaden our understanding of molecular convergence in ASD. Our analysis reveals ASD-associated dysregulation of primate-specific long noncoding RNAs (lncRNAs), downregulation of the alternative splicing of activity-dependent neuron-specific exons, and attenuation of normal differences in gene expression between the frontal and temporal lobes. Our data suggest that SOX5, a transcription factor involved in neuron fate specification, contributes to this reduction in regional differences. We further demonstrate that a genetically defined subtype of ASD, chromosome 15q11.2-13.1 duplication syndrome (dup15q), shares the core transcriptomic signature observed in idiopathic ASD. Co-expression network analysis reveals that individuals with ASD show age-related changes in the trajectory of microglial and synaptic function over the first two decades, and suggests that genetic risk for ASD may influence changes in regional cortical gene expression. Our findings illustrate how diverse genetic perturbations can lead to phenotypic convergence at multiple biological levels in a complex neuropsychiatric disorder.

SMaSH: a benchmarking toolkit for human genome variant calling

MOTIVATION Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers. RESULTS We propose SMaSH, a benchmarking methodology for evaluating germline variant calling algorithms. We generate synthetic datasets, organize and interpret a wide range of existing benchmarking data for real genomes and propose a set of accuracy and computational performance metrics for evaluating variant calling methods on these benchmarking data. Moreover, we illustrate the utility of SMaSH to evaluate the performance of some leading single-nucleotide polymorphism, indel and structural variant calling algorithms. AVAILABILITY AND IMPLEMENTATION We provide free and open access online to the SMaSH tool kit, along with detailed documentation, at smash.cs.berkeley.edu

Genome-wide changes in lncRNA, alternative splicing, and cortical patterning in autism

The genetic contributions to autism spectrum disorder (ASD) are substantial and profoundly heterogeneous, potentially converging on common molecular and cellular pathways that still are not well understood. Here, through genome-wide transcriptome analysis in the largest cohort of postmortem ASD brains analyzed to date, we interrogate the noncoding transcriptome, alternative splicing, and upstream molecular regulators to broaden our understanding of molecular convergence in ASD. Our analyses reveal dysregulation of primate-specific long noncoding RNAs (lncRNAs), downregulation of the alternative splicing of activity-dependent neuronal exons, and attenuation of normal gene expression differences between frontal and temporal lobes in ASD. We further demonstrate that a genetically defined subtype of ASD, Duplication 15q Syndrome (dup15q), shares the core transcriptomic signature observed in idiopathic ASD. Co-expression network analysis reveals age related changes in microglial and synaptic function that plateau after the first decade of life, and suggests that genetic risk for ASD may influence changes in regional cortical gene expression. Our work implicates SOX5, a transcription factor involved in neuron fate specification, in contributing to these diminished regional differences. These data and analyses show how diverse genetic perturbations can lead to phenotypic convergence at multiple biological levels in a complex neuropsychiatric disorder.We apply transcriptome-wide RNA sequencing in postmortem autism spectrum disorder (ASD) brain and controls and identify convergent alterations in the noncoding transcriptome, including primate specific lncRNA, and transcript splicing in ASD cerebral cortex, but not cerebellum. We characterize an attenuation of patterning between frontal and temporal cortex in ASD and identify SOX5, a transcription factor involved in cortical neuron fate specification, as a likely driver of this pattern. We further show that a genetically defined subtype of ASD, Duplication 15q Syndrome, shares the core transcriptomic signature of idiopathic ASD, indicating that observed molecular convergence in autism brain is the likely consequence of manifold genetic alterations. Using co-expression network analysis, we show that diverse forms of genetic risk for ASD affect convergent, independently replicated, biological pathways and provide an unprecedented resource for understanding the molecular alterations associated with ASD in humans.

Author Correction: Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism

Change history: In this Letter, the labels for splicing events A3SS and A5SS were swapped in column D of Supplementary Table 3a and b. This has been corrected online.