Cooper‐Knock, Johnathan

Abstract Although sequencing costs have steadily decreased with advances in technology, they remain high for large scale studies. The design of traditional individual-disease sequencing studies is either case only or cases with relatively few controls, resulting in potential loss of statistical power for discovery of disease associated genes. Here we show that for a given number of sequenced cases, a large control sample size is critical to maximize power for rare variant burden analysis. Furthermore, we have developed an end-to-end workflow based tool (CoCoRV-nf) to facilitate the use of external biobank sequence resources as controls. The modules include consistent variant QC, variant annotation, ancestry population prediction, and gene based burden analysis using summary genotype information, and combined analysis from multiple independent results. The tool supports exomes and genomes from gnomAD and All of Us as controls with preprocessed datasets. We apply the tool in two rare neurological diseases: amyotrophic lateral sclerosis and neuroblastoma. For each disease, two case cohorts are paired with gnomAD and All of Us data, respectively, followed by a combined analysis. Not only did we recapture known genes, but also, we identified new candidate genes for both diseases. By leveraging multiple large external biobank sequence data, we demonstrate the feasibility of using our tool to maximize statistical power to identify new disease predisposition genes.

Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a life-time risk of 1 in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry GWAS in ALS including 29,612 ALS patients and 122,656 controls which identified 15 risk loci in ALS. When combined with 8,953 whole-genome sequenced individuals (6,538 ALS patients, 2,415 controls) and the largest cortex-derived eQTL dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, repeat expansions or regulatory effects. ALS associated risk loci were shared with multiple traits within the neurodegenerative spectrum, but with distinct enrichment patterns across brain regions and cell-types. Of the environmental and life-style risk factors obtained from literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. All ALS associated signals combined reveal a role for perturbations in vesicle mediated transport and autophagy, and provide evidence for cell-autonomous disease initiation in glutamatergic neurons.