Chiao-Feng Lin

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case–control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.

Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders.

To characterize the role of rare complete human knockouts in autism spectrum disorders (ASDs), we identify genes with homozygous or compound heterozygous loss-of-function (LoF) variants (defined as nonsense and essential splice sites) from exome sequencing of 933 cases and 869 controls. We identify a 2-fold increase in complete knockouts of autosomal genes with low rates of LoF variation (≤ 5% frequency) in cases and estimate a 3% contribution to ASD risk by these events, confirming this observation in an independent set of 563 probands and 4,605 controls. Outside the pseudoautosomal regions on the X chromosome, we similarly observe a significant 1.5-fold increase in rare hemizygous knockouts in males, contributing to another 2% of ASDs in males. Taken together, these results provide compelling evidence that rare autosomal and X chromosome complete gene knockouts are important inherited risk factors for ASD.

HIPPIE: a high-throughput identification pipeline for promoter interacting enhancer elements

UNLABELLED We implemented a high-throughput identification pipeline for promoter interacting enhancer element to streamline the workflow from mapping raw Hi-C reads, identifying DNA-DNA interacting fragments with high confidence and quality control, detecting histone modifications and DNase hypersensitive enrichments in putative enhancer elements, to ultimately extracting possible intra- and inter-chromosomal enhancer-target gene relationships. AVAILABILITY AND IMPLEMENTATION This software package is designed to run on high-performance computing clusters with Oracle Grid Engine. The source code is freely available under the MIT license for academic and nonprofit use. The source code and instructions are available at the Wang lab website (http://wanglab.pcbi.upenn.edu/hippie/). It is also provided as an Amazon Machine Image to be used directly on Amazon Cloud with minimal installation. CONTACT lswang@mail.med.upenn.edu or bdgregor@sas.upenn.edu SUPPLEMENTARY INFORMATION Supplementary Material is available at Bioinformatics online.

Bioinformatics Workflow for Clinical Whole Genome Sequencing at Partners HealthCare Personalized Medicine

Effective implementation of precision medicine will be enhanced by a thorough understanding of each patient’s genetic composition to better treat his or her presenting symptoms or mitigate the onset of disease. This ideally includes the sequence information of a complete genome for each individual. At Partners HealthCare Personalized Medicine, we have developed a clinical process for whole genome sequencing (WGS) with application in both healthy individuals and those with disease. In this manuscript, we will describe our bioinformatics strategy to efficiently process and deliver genomic data to geneticists for clinical interpretation. We describe the handling of data from FASTQ to the final variant list for clinical review for the final report. We will also discuss our methodology for validating this workflow and the cost implications of running WGS.

Global and local ancestry in African-Americans: Implications for Alzheimer's disease risk.

African‐American (AA) individuals have a higher risk for late‐onset Alzheimers disease (LOAD) than Americans of primarily European ancestry (EA). Recently, the largest genome‐wide association study in AAs to date confirmed that six of the Alzheimers disease (AD)‐related genetic variants originally discovered in EA cohorts are also risk variants in AA; however, the risk attributable to many of the loci (e.g., APOE, ABCA7) differed substantially from previous studies in EA. There likely are risk variants of higher frequency in AAs that have not been discovered.

Analyzing copy number variation using SNP array data: protocols for calling CNV and association tests.

High-density SNP genotyping technology provides a low-cost, effective tool for conducting Genome Wide Association (GWA) studies. The wide adoption of GWA studies has indeed led to discoveries of disease- or trait-associated SNPs, some of which were subsequently shown to be causal. However, the nearly universal shortcoming of many GWA studies--missing heritability--has prompted great interest in searching for other types of genetic variation, such as copy number variation (CNV). Certain CNVs have been reported to alter disease susceptibility. Algorithms and tools have been developed to identify CNVs using SNP array hybridization intensity data. Such an approach provides an additional source of data with almost no extra cost. In this unit, we demonstrate the steps for calling CNVs from Illumina SNP array data using PennCNV and performing association analysis using R and PLINK.

DRAW+SneakPeek: Analysis workflow and quality metric management for DNA-seq experiments

Summary: We report our new DRAW+SneakPeek software for DNA-seq analysis. DNA resequencing analysis workflow (DRAW) automates the workflow of processing raw sequence reads including quality control, read alignment and variant calling on high-performance computing facilities such as Amazon elastic compute cloud. SneakPeek provides an effective interface for reviewing dozens of quality metrics reported by DRAW, so users can assess the quality of data and diagnose problems in their sequencing procedures. Both DRAW and SneakPeek are freely available under the MIT license, and are available as Amazon machine images to be used directly on Amazon cloud with minimal installation. Availability: DRAW+SneakPeek is released under the MIT license and is available for academic and nonprofit use for free. The information about source code, Amazon machine images and instructions on how to install and run DRAW+SneakPeek locally and on Amazon elastic compute cloud is available at the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (http://www.niagads.org/) and Wang lab Web site (http://wanglab.pcbi.upenn.edu/). Contact: gerardsc@mail.med.upenn.edu or lswang@mail.med.upenn.edu

A Rigorous Interlaboratory Examination of the Need to Confirm NGS-Detected Variants by an Orthogonal Method in Clinical Genetic Testing

Background The confirmation of genetic variants identified by next-generation sequencing (NGS) using orthogonal assays (e.g., Sanger sequencing) is standard practice in many laboratories. Published studies have examined this issue, concluding that confirmation of the highest-quality NGS calls may not always be necessary. However, these studies are generally small, omit statistical justification, and explore limited aspects of the underlying data. Defining criteria that separate high accuracy NGS calls that do not benefit from confirmation from those that do remains a critical and pressing issue. Methods We conducted a rigorous examination of NGS data from two clinical laboratories. Five Genome in a Bottle reference samples and over 80,000 clinical patient specimens were analyzed, with the combined data providing insights that neither data type alone could provide. In total, almost 200,000 variant calls with orthogonal data were examined including 1684 false–positives detected by confirmation. Results We used these data to identify criteria that flag 100% of false positives as requiring confirmation (CI lower bound: 98.5 to 99.8% depending on variant type) while minimizing the number of flagged true positives. Rather than relying on one or two quality metrics, as current publications do, a battery of criteria proved superior, consistent with the metrics recommended by recent practice guidelines. Indeed, our expanded criteria identify some false positives as requiring confirmation that the currently published criteria miss. We also find that historical performance (observing a variant as a true positive some number of times) without other strict quality criteria can also lead to false positives escaping confirmation. Discussion Although we found limitations with the currently published criteria, our large, multi-laboratory study reaffirms prior findings that high accuracy variant calls can be separated from those of lower confidence. Our methodology for determining test and laboratory-specific criteria can be generalized into a practical approach that can help reduce the cost and time burden of confirmation without impacting clinical accuracy.

Multiple deletion copy number variants (CNVs) are associated with late-onset Alzheimer's disease: The Alzheimer’s disease genetics consortium

Background:Alzheimer’s disease (AD) is characterized by progressive neuropathology and cognitive decline. Although the neuropathological manifestation of AD is well characterized in postmortem brain, little is known about the underlying risk factors or mechanism(s) involved in disease progression. We recently published the first epigenome-wide association studies (EWAS), demonstrating methylomic variation in AD brain and blood (Lunnon et al., 2014), with the most notable differences occurring in regions of the brain characterised by extensive neuropathology. Following on from these studies, we describe a network approach to identify modules of co-methylated loci associated with amyloid and tau burden using DNA from multiple brain regions in a cohort of 144 samples. Methods: DNA was extracted from 100mg tissue from a cohort of 144 individuals from the Mount Sinai NIH Brain and Tissue Repository (NBTR), with two brain regions (Prefrontal cortex-PFC and Superior temporal gyrus-STG) obtained from each individual. Genomic DNA was bisulfite treated using the Zymo EZ DNA methylation kit and samples were assessed using the Illumina Infinium Human Methylation 450K BeadChip. Following data normalisation and stringent quality control, we identifiedmethylomic variation associatedwith quantitativemeasures of neuropathology (amyloid/tau) using linear models, whilst controlling for the effects of age and gender. We used the R package “WGCNA” to identify groups of co-methylated genes that were associated with (a) neuropathological measures and (b) genetic variants associated with AD from recent large meta-analyses. Results: We identified many differentially methylate positions (DMPs) associated with disease in both the PFC and STG, replicating many of our previous findings. The strongest results were found with amyloid burden and braak staging, where we observed DMPs located in and near previously identified genes containing risk variants, including APOE. UsingWGCNA, we also identified multiple modules of co-methylated loci characterised by consistent associations with disease status and neuropathology measures, as well as polygenic risk status. Conclusions:This study provides further evidence for a role of epigenomic dysfunction in AD, identifying networks of co-methylated loci associated withvarious neuropathological traits. We demonstrate strong evidence for an interaction between genotype and DNA methylation at loci previously implicated in genetic studies of AD.

Prediction of late-onset Alzheimer’s disease-associated enhancer elements

Background: Genome-wide association studies (GWAS) have added substantially to our understanding of the genetic factors implicated in late-onset Alzheimer’s disease (LOAD). However, GWAS alone lacks the granularity to determine causal variants from among thousands of candidates in each significant region. Determination of true causal variants must therefore be conducted through additional analyses following GWAS. This is further complicated by the fact that an estimated 93% of GWAS signals implicate variants in non-protein-coding regions where mechanisms involve indirect gene regulation (Maurano et al. 2012). Furthermore, regulatory elements do not necessarily regulate the closest genes. Therefore two key questions arise: (1) How do we distinguish between the multitudes of non-coding variants with significant P-values to determine causal variants? (2) How do we determine which target genes these causal variants regulate?Methods:We developed a pipeline integrating annotation data from ENCODE, NIH Roadmap Epigenomics, and FANTOM5 data among others for the purpose of detecting cell-type-specific enhancer-promoter relationships affected by LOAD-associated non-coding SNPs. From the 21 genome-wide significant LOAD-associated regions identified by the IGAP GWAS, 1,980 candidate SNPs were identified. Candidates were then prioritized for enhancer potential based on consensus among cell-type-specific and non-cell-type-specific enhancer markings. Target genes were predicted for top candidates based on gene expression QTL and chromatin conformation (3C and Hi-C) results, as well as through enhancer-promoter correlation of both open chromatin and RNA expression levels. Results:Using our pipeline we identified a set of five top candidate LOAD-associated enhancer SNPs, which are supported by multiple independent monocyte-specific enhancer marks. These SNPs were linked to the genes PTK2B, CASS4, MS4A4A, MS4A6A, and TAS2R41 by both FANTOM5 enhancer-promoter RNA expression correlation as well as monocyte-specific eQTL studies. We are currently in the process of performing luciferase assays in the THP-1 monocyte cell line in order to validate the enhancers’ activities. Conclusions: Through utilization of genomic annotation data, we were able to identify candidate causal SNPs within LOAD-associated non-protein coding regions. Further wet-lab validations are underway; we will report the findings during the AAIC 2015 meeting.