Biology subjects

Southam, L.

Publications and source records attributed to Southam, L..

9 recordsLinked to original sources

bioRxiv2019

Whole genome sequencing analysis of the cardiometabolic proteome

The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5x) whole-genome sequencing (WGS) in 1,328 individuals. We discover 131 independent sequence variant associations (P<7.45x10-11) across the allele frequency spectrum, all of which replicate in an independent cohort (n=1,605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and non-coding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.

genetics↗

bioRxiv2019

Genome-wide association and functional analyses identify CASC20 and KIF26B as target loci in heterotopic ossification

Heterotopic ossification (HO) is bone formation that occurs after trauma within tissues that do not normally have the property of ossification, resulting in pain and disability. The genetic architecture of HO remains unclear. In the first genome-wide association studies of this disease, we identify the human-only long non-coding RNA-encoding gene CASC20 as a robust, replicating susceptibility locus for HO and KIF26B as a potential severity locus. We find that both CASC20 and KIF26B are expressed in human bone. Both CASC20 and KIF26B expression is upregulated upon BMP2 induced osteogenic differentiation in primary human mesenchymal stem cells, followed by RUNX2 and OSTERIX upregulation and mineralised nodule formation. A CRISPR-Cas9 mediated knockout of Kif26b inhibits BMP2-induced Runx2, Sp7/Osterix, Col1A1, Alp, and Bglap/Osteocalcin expression in a murine myocyte model of osteogenic trans-differentiation, and prevents mineralised nodule formation. These studies provide the first insights into the heritable biology of common, complex HO.

genetics↗

bioRxiv2019

Decoding the genomic basis of osteoarthritis

Osteoarthritis causes pain and functional disability for a quarter of a billion people worldwide, with no disease-stratifying tools nor modifying therapy. Here, we use primary cartilage and synovium from osteoarthritis patients to construct a molecular quantitative trait locus map of gene expression and protein abundance. By integrating data across omics levels, we identify likely effector genes for osteoarthritis-associated genetic signals. We detect pronounced molecular differences between macroscopically intact and highly degenerated cartilage. We identify molecularly-defined patient subgroups that correlate with clinical characteristics, stratifying patients on the basis of their molecular profile. We construct and validate a 7-gene classifier that reproducibly distinguishes between these disease subtypes, and identify potentially actionable compounds for disease modification and drug repurposing.

genomics↗

bioRxiv2019

Accelerating functional gene discovery in osteoarthritis

Osteoarthritis causes debilitating pain and disability, resulting in a huge socioeconomic burden, yet no drugs are available that prevent disease onset or progression. Here, we develop, validate and use rapid-throughput imaging techniques to identify abnormal joint phenotypes in unselected mutant mice generated by the International Knockout Mouse Consortium. We identify 14 genes with functional involvement in osteoarthritis pathogenesis, including the homeobox gene Pitx1, and functionally characterize 6 candidate human osteoarthritis genes in mouse models. We demonstrate sensitivity of the methods by identifying age-related degenerative joint damage in wild-type mice. Finally, we generate mutant mice with an osteoarthritis-associated polymorphism in the Dio2 gene by Crispr/Cas9 genome editing and demonstrate a protective role in disease onset with public health implications. This expanding resource of unselected mutant mice will transform the field by accelerating functional gene discovery in osteoarthritis and offering unanticipated drug discovery opportunities for this common and incapacitating chronic disease.

genetics↗

bioRxiv2019

The transferability of lipid-associated loci across African, Asian and European cohorts

The under-representation of non-European samples in genome-wide association studies could ultimately restrict who benefits from medical advances through genomic science. Our aim was therefore to address the fundamental question whether causal variants for blood lipids are shared across populations.\n\nA polygenic score based on established LDL-cholesterol-associated loci from European discovery samples had consistent effects on serum levels in samples from the UK, Uganda and Greek population isolates (correlation coefficient r=0.23 to 0.28 per LDL standard deviation, p<1.9x10-14). Trans-ethnic genetic correlations between European ancestry, Chinese and Japanese cohorts did not differ significantly from 1 for HDL, LDL and triglycerides. In each study, >60% of major lipid loci displayed evidence of replication with one exception. There was evidence for an effect on serum levels in the Ugandan samples for only 10% of major triglyceride loci. The PRS was only weakly associated in this group (r=0.06, SE=0.013). We establish trans-ethnic colocalization as a method to distinguish shared from population-specific trait loci.\n\nOur results provide evidence for high levels of consistency of genetic associations for cholesterol biomarkers across populations. However, we also demonstrate that the degree of shared causal genetic architecture can be population-, trait- and locus-specific. Efforts to implement genetic risk prediction in clinical settings should account for this.

genetics↗

bioRxiv2018

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

genetics↗

bioRxiv2018

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1,457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens overlapping with, and mostly independent of established common variant signals (ADIPOQ and adiponectin, P=4.2x10-8; APOC3 and triglyceride levels, P=1.58x10-26; GGT1 and gamma-glutamyltransferase, P=2.3x10-6; UGT1A9 and bilirubin, P=1.9x10-8), and identify replicating evidence for a burden associated with triglyceride levels in FAM189A (P=2.26x10-8), indicating a role for this gene in lipid metabolism.

genetics↗

bioRxiv2017

The genetic architecture of osteoarthritis: insights from UK Biobank

Osteoarthritis is a common complex disease with huge public health burden. Here we perform a genome-wide association study for osteoarthritis using data across 16.5 million variants from the UK Biobank resource. Following replication and meta-analysis in up to 30,727 cases and 297,191 controls, we report 9 new osteoarthritis loci, in all of which the most likely causal variant is non-coding. For three loci, we detect association with biologically-relevant radiographic endophenotypes, and in five signals we identify genes that are differentially expressed in degraded compared to intact articular cartilage from osteoarthritis patients. We establish causal effects for higher body mass index, but not for triglyceride levels or type 2 diabetes liability, on osteoarthritis.

genetics↗

bioRxiv2017

Very low depth whole genome sequencing in complex trait association studies

MotivationVery low depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterisation of the genotype quality and association power for very low depth sequencing designs is still lacking.\n\nResultsWe perform cohort-wide whole genome sequencing (WGS) at low depth in 1,239 individuals (990 at 1x depth and 249 at 4x depth) from an isolated population, and establish a robust pipeline for calling and imputing very low depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (WES, 75x depth) and high-depth (22x) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1x WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1x further allowed the discovery of 140,844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design.\n\nSupplementary DataSupplementary Data are appended to this manuscript.

genetics↗