Biology subjects

Farrell, J. J.

Publications and source records attributed to Farrell, J. J..

4 recordsLinked to original sources

bioRxiv2022

A comparative study of structural variant calling strategies using the Alzheimer's Disease Sequencing Project's whole genome family data

BackgroundReliable detection and accurate genotyping of structural variants (SVs) and insertion/deletions (indels) from whole-genome sequence (WGS) data is a significant challenge. We present a protocol for variant calling, quality control, call merging, sensitivity analysis, in silico genotyping, and laboratory validation protocols for generating a high-quality deletion call set from whole genome sequences as part of the Alzheimers Disease Sequencing Project (ADSP). This dataset contains 578 individuals from 111 families. MethodsWe applied two complementary pipelines (Scalpel and Parliament) for SV/indel calling, break-point refinement, genotyping, and local reassembly to produce a high-quality annotated call set. Sensitivity was measured in sample replicates (N=9) for all callers using in silico variant spike-in for a wide range of event sizes. We focused on deletions because these events were more reliably called. To evaluate caller specificity, we developed a novel metric called the D-score that leverages deletion sharing frequencies within and outside of families to rank recurring deletions. Assessment of overall quality across size bins was measured with the kinship coefficient. Individual callers were evaluated for computational cost, performance, sensitivity, and specificity. Quality of calls were evaluated by Sanger sequencing of predicted loss-of-function (LOF) variants, variants near AD candidate genes, and randomly selected genome-wide deletions ranging from 2 to 17,000 bp. ResultsWe generated a high-quality deletion call set across a wide range of event sizes consisting of 152,301 deletions with an average of 263 per genome. A total of 114 of 146 predicted deletions (78.1%) were validated by Sanger sequencing. Scalpel was more accurate in calling deletions [≤]100 bp, whereas for Parliament, sensitivity was improved for deletions > 900 bp. We validated 83.0% (88/106) and 72.5% (37/51) of calls made by Scalpel and Parliament, respectively. Eleven deletions called by both Parliament and Scalpel in the 101-900 bin were tested and all were confirmed by Sanger sequencing. ConclusionsWe developed a flexible protocol to assess the quality of deletion detection across a wide range of sizes. We also generated a truth set of Sanger sequencing validated deletions with precise breakpoints covering a wide spectrum of sizes between 1 and 17,000 bp.

genetics↗

bioRxiv2021

An Association Test of the Spatial Distribution of Rare Missense Variants within Protein Structures Improves Statistical Power of Sequencing Studies

Over 90% of variants are rare, and 50% of them are singletons in the Alzheimers Disease Sequencing Project Whole Exome Sequencing (ADSP WES) data. However, either single variant tests or unit-based tests are limited in the statistical power to detect the association between rare variants and phenotypes. To best utilize rare variants and investigate their biological effect, we exam their association with phenotypes in the context of protein. We developed a protein structure-based approach, POKEMON (Protein Optimized Kernel Evaluation of Missense Nucleotides), which evaluates rare missense variants based on their spatial distribution on the protein rather than allele frequency. The hypothesis behind this is that the three-dimensional spatial distribution of variants within a protein structure provides functional context and improves the power of association tests. POKEMON identified four candidate genes from the ADSP WES data, namely two known Alzheimers disease (AD) genes (TREM2 and SORL) and two novel genes (DUSP18 and CSF1R). For known AD genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low frequency risk variants within these genes. DUSP18 has a cluster of variants primarily shared by case subjects around the ligand-binding domain, and this cluster is further validated in a replication dataset with a larger sample size. POKEMON is an open-source tool available at https://github.com/bushlab-genomics/POKEMON.

bioinformatics↗

bioRxiv2019

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls [≥]50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.

genomics↗

bioRxiv2018

Cis- and Trans-Acting Expression Quantitative Trait Loci Differentially Regulate Gamma-Globin Gene Expression

Genetic association studies have detected two trans-acting quantitative trait loci (QTL) on chromosomes 2, 6 and one cis-acting QTL on chromosome 11 that were associated with fetal hemoglobin (HbF) levels. In these studies, HbF was expressed as a percentage of total hemoglobin or the number of erythrocytes that contain HbF (F-cells). As the{gamma} -globin chains of HbF are encoded by two non-allelic genes (HBG2, HBG1) that are expressed at different levels we used normalized gene expression and genotype data from The Genotype-Tissue Expression (GTEx)-project to study the effects of cis- and trans-acting HbF expression or eQTL. This allowed us to examine mRNA expression of HBG2 and HBG1individually. In addition to studying eQTL for globin genes we examined genes co-expressed with HBG1, studied upstream regulators of HBG1 co-expressed genes and performed a correlation analysis between HBG2 and HBG1 and known HbF regulators. Our results suggest differential effect of cis and trans-acting QTL on HBG and HBG1 expression. Trans-acting eQTLs have the same magnitude of effect on the expression of both HBG2 and HBG1 while the sole cis-acting eQTL affected only HBG2. Furthermore, the analysis of upstream regulators and the correlation analysis suggested that BCL2L1 might be a new potential trans-acting HbF activator. HbF is the major modulator of the phenotype of sickle cell anemia and {beta} thalassemia. Depending on the effect size, modification of trans-acting elements might have a greater impact on HbF levels than cis-acting elements alone.

genetics↗