Biology subjects

MacArthur, D.

Publications and source records attributed to MacArthur, D..

13 recordsLinked to original sources

bioRxiv2023

Pitfalls in performing genome-wide association studies on ratio traits

Genome-wide association studies (GWAS) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator-driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

genetics↗

bioRxiv2022

A genome-wide mutational constraint map quantified from variation in 76,156 human genomes

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts to assess constraint for non-protein-coding regions have proven more difficult. Here we aggregate, process, and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD), the largest public open-access human genome reference dataset, and use this dataset to build a mutational constraint map for the whole genome. We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation across the genome. As expected, proteincoding sequences overall are under stronger constraint than non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association, and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, while non-coding constraint captures additional functional information underrecognized by gene constraint metrics. We demonstrate that this genome-wide constraint map provides an effective approach for characterizing the non-coding genome and improving the identification and interpretation of functional human genetic variation.

genetics↗

bioRxiv2020

Biological insights from the whole genome analysis of human embryonic stem cells

There has not yet been a systematic analysis of hESC whole genomes at a single nucleotide resolution. We therefore performed whole genome sequencing (WGS) of 143 hESC lines and annotated their single nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer scale structural and single nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESCs genomes and human blood-derived genomes at similar frequencies. However, WGS did identify SNVs associated with cancer or other diseases that will likely alter cellular phenotypes and may compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/337352v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1c28e3aorg.highwire.dtl.DTLVardef@7a07corg.highwire.dtl.DTLVardef@b95f2borg.highwire.dtl.DTLVardef@1732af_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFMerkle and Ghosh et al. describe insights from the whole genome sequences of commonly used human embryonic stem cell (hESC) lines. Analyses of these sequences show that while hESC genomes had more large structural variants than humans do from genetic inheritance, hESCs did not have an observable excess of finer-scale variants. However, many hESC lines contained rare loss-of-function variants and combinations of common variants that may profoundly shape their biological phenotypes. Thus, genome sequencing data can be valuable to those selecting cell lines for a given biological or clinical application, and the sequences and analysis reported here should facilitate such choices. HIGHLIGHTSO_LIOne third of hESCs we analysed are siblings, and almost all are of European ancestry C_LIO_LILarge structural variants are common in hESCs, but finer-scale variation is similar to that human populations C_LIO_LIMany strong-effect loss-of-function mutations and cancer-associated mutations are present in specific hESC lines C_LIO_LIWe provide user-friendly resources for rational hESC line selection based on genome sequence C_LI

genomics↗

bioRxiv2020

Insights into dispersed duplications and complex structural mutations from whole genome sequencing 706 families

Two intriguing forms of genome structural variation (SV) - dispersed duplications, and de novo rearrangements of complex, multi-allelic loci - have long escaped genomic analysis. We describe a new way to find and characterize such variation by utilizing identity-by-descent (IBD) relationships between siblings together with high-precision measurements of segmental copy number. Analyzing whole-genome sequence data from 706 families, we find hundreds of "IBD-discordant" (IBDD) CNVs: loci at which siblings CNV measurements and IBD states are mathematically inconsistent. We found that commonly-IBDD CNVs identify dispersed duplications; we mapped 95 of these common dispersed duplications to their true genomic locations through family-based linkage and population linkage disequilibrium (LD), and found several to be in strong LD with genome-wide association (GWAS) signals for common diseases or gene expression variation at their revealed genomic locations. Other CNVs that were IBDD in a single family appear to involve de novo mutations in complex and multi-allelic loci; we identified 26 de novo structural mutations that had not been previously detected in earlier analyses of the same families by diverse SV analysis methods. These included a de novo mutation of the amylase gene locus and multiple de novo mutations at chromosome 15q14. Combining these complex mutations with more-conventional CNVs, we estimate that segmental mutations larger than 1kb arise in about one per 22 human meioses. These methods are complementary to previous techniques in that they interrogate genomic regions that are home to segmental duplication, high CNV allele frequencies, and multi-allelic CNVs. Author SummaryCopy number variation is an important form of genetic variation in which individuals differ in the number of copies of segments of their genomes. Certain aspects of copy number variation have traditionally been difficult to study using short-read sequencing data. For example, standard analyses often cannot tell whether the duplicated copies of a segment are located near the original copy or are dispersed to other regions of the genome. Another aspect of copy number variation that has been difficult to study is the detection of mutations in the copy number of DNA segments passed down from parents to their children, particularly when the mutations affect genome segments which already display common copy number variation in the population. We develop an analytical approach to solving these problems when sequencing data is available for all members of families with at least two children. This method is based on determining the number of parental haplotypes the two siblings share at each location in their genome, and using that information to determine the possible inheritance patterns that might explain the copy numbers we observe in each family member. We show that dispersed duplications and mutations can be identified by looking for copy number variants that do not follow these expected inheritance patterns. We use this approach to determine the location of 95 common duplications which are dispersed to distant regions of the genome, and demonstrate that these duplications are linked to genetic variants that affect disease risk or gene expression levels. We also identify a set of copy number mutations not detected by previous analyses of sequencing data from a large cohort of families, and show that repetitive and complex regions of the genome undergo frequent mutations in copy number.

genomics↗

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗

bioRxiv2019

Deep phenotyping of a healthy human HAO1 knockout informs therapeutic development for primary hyperoxaluria type 1.

Primary Hyperoxaluria Type 1 (PH1) is a rare autosomal recessive metabolic disorder of oxalate metabolism leading to kidney failure as well as multi-organ damage. Overproduction of oxalate occurs in the liver due to an inherited genetic defect in the enzyme alanine-glyoxylate aminotransferase (AGXT), causing pathology due to the insolubility of calcium oxalate crystals in body fluids. The main current therapy is dual liver-kidney transplant, which incurs high morbidity and has poor availability in some health systems where PH1 is more prevalent. One approach currently in active clinical investigation targets HAO1 (hydroxyacid oxidase 1), encoding glycolate oxidase, to reduce substrate levels for oxalate production. To inform drug development, we sought individuals with reduced HAO1 function due to naturally occurring genetic variation.\n\nAnalysis of loss of function variants in 141,456 sequenced individuals suggested individuals with complete HAO1 knockout would only be observed in 1 in 30 million outbred people. However in a large sequencing and health records program (Genes & Health), in populations with substantial autozygosity, we identified a healthy adult individual predicted to have complete knockout of HAO1 due to an ultra rare homozygous frameshift variant (rs1186715161, ENSP00000368066.3:p.Leu333SerfsTer4). Primary care and hospital health records confirmed no apparently related clinical phenotype. At recall, urine and plasma oxalate levels were normal, however plasma glycolate levels (171 nmol/mL) were 12 times the upper limit of normal in healthy, reference individuals (mean+2sd=14 nmol/mL, n=67) while her urinary glycolate levels were 6 times the upper limit of normal. Comparison with preclinical and phase 1 clinical trial data of an RNAi therapeutic targeting HAO1 (lumasiran) suggests the individual likely retains <2% residual glycolate oxidase activity.\n\nThese results provide important data to support the safety of HAO1 inhibition as a potential chronic therapy for a devastating metabolic disease (PH1). We also suggest that the effect of glycolate oxidase suppression in any potential other roles in humans beyond glycolate oxidation do not lead to clinical phenotypes, at least in this specific individual. This demonstrates the value of studying the lifelong complete knockdown of a target protein in a living human to aid development of a potential therapeutic, both in de-risking the approach and providing potential hypotheses to optimize its development. Furthermore, therapy for PH1 is likely to be required lifelong, in contrast to data from chronicity studies in non-human species or relatively short-term therapeutic studies in people. Our approach demonstrates the potential for improved drug discovery through unlocking relevant evidence hiding in the diversity of human genetic variation.

genetics↗

bioRxiv2018

Cohort Profile: East London Genes & Health (ELGH), a community based population genomics and health study in people of British-Bangladeshi and -Pakistani heritage.

Cohort profile in a nutshellO_LIEast London Genes & Health (ELGH) is a large scale, community genomics and health study (to date >34,000 volunteers; target 100,000 volunteers). C_LIO_LIELGH was set up in 2015 to gain deeper understanding of health and disease, and underlying genetic influences, in British-Bangladeshi and British-Pakistani people living in east London. C_LIO_LIELGH prioritises studies in areas important to, and identified by, the community it represents. Current priorities include cardiometabolic diseases and mental illness, these being of notably high prevalence and severity. However studies in any scientific area are possible, subject to community advisory group and ethical approval. C_LIO_LIELGH combines health data science (using linked UK National Health Service (NHS) electronic health record data) with exome sequencing and SNP array genotyping to elucidate the genetic influence on health and disease, including the contribution from high rates of parental relatedness on rare genetic variation and homozygosity (autozygosity), in two understudied ethnic groups. Linkage to longitudinal health record data enables both retrospective and prospective analyses. C_LIO_LIThrough Stage 2 studies, ELGH offers researchers the opportunity to undertake recall-by-genotype and/or recall-by-phenotype studies on volunteers. Sub-cohort, trial-within-cohort, and other study designs are possible. C_LIO_LIELGH is a fully collaborative, open access resource, open to academic and life sciences industry scientific research partners. C_LI

genomics↗

bioRxiv2017

Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only [~]1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as Linked-Reads. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

genomics↗

bioRxiv2017

New synthetic-diploid benchmark for accurate variant calling evaluation

Constructed from the consensus of multiple variant callers based on short-read data, existing benchmark datasets for evaluating variant calling accuracy are biased toward easy regions accessible by known algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two human cell lines that are homozygous across the whole genome. This benchmark provides a more accurate and less biased estimate of the error rate of small variant calls in a realistic context.

bioinformatics↗

bioRxiv2017

Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation

Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large \"real-world data\" cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P<1.8x10-6; out of 72 novel associations with FDR<0.1). By leveraging directionality and point estimate of the effect sizes, we describe new associations that may predict ADEs, e.g., acne, high cholesterol, gout and gallstones for rs738409 (p.I148M) in PNPLA3; or asthma for rs1990760 (p.T946A) in IFIH1. We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.\n\nOne Sentence SummaryMatching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.

genetics↗

bioRxiv2017

Contribution of structural and intronic mutations to RPGRIP1-mediated inherited retinal dystrophies.

PurposeWith the advent of gene therapies for inherited retinal degenerations (IRDs), genetic diagnostics will have an increasing role in clinical decision-making. Yet the genetic cause of disease cannot be identified using exon-based sequencing for a significant portion of patients. We hypothesized that non-coding mutations contribute significantly to the genetic causality of IRDs and evaluated patients with single coding mutations in RPGRIP1 to test this hypothesis.\n\nMethodsIRD families underwent targeted panel sequencing. Unsolved cases were explored by whole exome and genome sequencing looking for additional mutations. Candidate mutations were then validated by Sanger sequencing, quantitative PCR, and in vitro splicing assays in two cell lines analyzed through amplicon sequencing.\n\nResultsAmong 1722 families, three had biallelic loss of function mutations in RPGRIP1 while seven had a single disruptive coding mutation. Whole exome and genome sequencing revealed potential non-coding mutations in these seven families. In six, the non-coding mutations were shown to lead to loss of function in vitro.\n\nConclusionNon-coding mutations were identified in 6 of 7 families with single coding mutations in RPGRIP1. The results suggest that non-coding mutations contribute significantly to the genetic causality of IRDs and RPGRIP1-mediated IRDs are more common than previously thought.

genomics↗

bioRxiv2017

STRetch: detecting and discovering pathogenic short tandem repeats expansions

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.\n\nHere we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.\n\nSTRetch is open source software, available from github.com/Oshlack/STRetch.

bioinformatics↗

bioRxiv2017

Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum

There is a limited understanding about the impact of rare protein truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization and reduced age. Gene sets implicated from GWAS did not show a significant protein truncating variants-burden beyond what captured by established Mendelian genes. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.\n\nMain abbreviations

genetics↗