Biology subjects

Robinson, M. R.

Publications and source records attributed to Robinson, M. R..

17 recordsLinked to original sources

bioRxiv2026

Joint Variable Selection for Omic Biomarkers in Time-to-Event Data

The incidence of the vast majority of neurodegenerative, cancer, and metabolic diseases generally increases exponentially with age. In large-scale biobanks, linking time-to-diagnosis information in electronic health records to multiple genomic ("multiomics") measures has the potential to reveal the genes and biological pathways involved in the disease onset and progression. To date, association testing has commonly been conducted by testing one variable at a time using semiparametric Cox proportional hazards (CoxPH) models, which ignores correlation structure and increases the risk of false discoveries. To address these issues, we introduce a novel fully parametric Bayesian computational method, vampW, based on the Vector Approximate Message Passing framework applied to a Weibull model. vampW jointly models correlated features, while providing an interpretable hazard structure, producing a continuous survival curve, and incorporating prior knowledge. In an extensive simulation study, we demonstrate that joint modeling of omics data and time-to-event outcomes with vampW, substantially reduces false discoveries in comparison to marginal testing and other forms of joint CoxPH models. In 53,018 individuals from the UK Biobank, vampW identifies 219 protein associations with 24 disease outcomes, most of which are not among the top marginal discoveries. We further correct protein levels for exponential age effects, identifying 1,308 associations and highlighting the sensitivity of the analysis to age-correction methodology. Our findings replicate in independent cohorts using different measurement technologies, within data from Iceland and a novel Generation Scotland proteomics dataset. vampW also achieves significant improvement in the prediction of disease onset times: across 14 outcomes, it reduces the root mean squared error by over 32% and 26%, when compared to CoxPH variants and the deep learning approach DeepSurv, respectively, while maintaining predictive utility in minority populations. In summary, vampW offers accurate and interpretable variable selection and out-of-sample prediction within a single computational framework, making it a powerful tool for dissecting the genomic architecture of common complex disease onset.

bioinformatics↗

bioRxiv2026

Sex differences in the genetic basis of human recombination within 190,000 parent-child pairs

Structured AbstractO_ST_ABSIntroductionC_ST_ABSSexual reproduction uses a specialized cell division called meiosis, in which a single round of DNA replication is followed by two cell divisions to create hap-loid gametes. Genetic recombination in meiosis assures faithful segregation of chromosomes and establishes patterns of genetic linkage and inheritance. Meiotic recombination is thus a fundamental genomic process that shapes major features of the genomic landscape, influences mutation, and creates genetic diversity. RationaleThe genetic basis of between-individual variation in the occurrence and genomic location of meiotic recombination events remains poorly understood in humans. In particular, we lack an understanding of whether the same DNA regions shape vari-ation in recombination rates across studies. We address this by curating 112,144 maternal and 78,653 paternal meiosis events (parent-child pairs) across cohorts from four countries, creating the largest dataset of its kind. ResultsWe identify 58 independent genomic regions associated with the occurrence and genomic location of recombination events, 24 of which are unique across phe-notypes. Of the 24 unique regions, 6 are novel and specific to female meiotic recombination harboring genes linked to ovarian function, a 7th novel region is linked to male recombination. Many regions have significant sex-dependent ef-fects across studies. We estimate the between-sex genetic correlation for meiotic recombination rate to be 0.374 (0.072 SD), which captures the extent to which the genetic factors influencing a trait in one sex also impact the same trait in the other. 30-40% of the individual-level variation in recombination rate is at-tributable to DNA markers, of which only 37.7% (2.7% SD) and 47.1% (3.8% SD) is attributable to previously known global modulators of meiotic recombi-nation, in females and males respectively. We create a polygenic predictor that significantly predicts recombination rates of both women and men after adjusting for age and year of birth. We find significant correlations between female meiotic recombination, reproductive outcomes and reproductive aging, after adjusting for study ascertainment. ConclusionsIn the largest meta-analysis to date of human meiotic recombination, we identify novel loci with sex-specific effects that influence male and female recombination rate across multiple cohorts.

genomics↗

bioRxiv2026

Converging evidence of positive selection at height-associated loci in Europe

The contribution of natural selection to height differences across Europe remains elusive. Prior evidence of selection on height based on genome-wide association study (GWAS) data has been challenged because of residual confounding in earlier GWAS results. Here, we reassess this evidence using complementary approaches integrating multi-ancestry and within-family GWAS data with allele frequency estimates from 13 European ancestry populations. We detect signatures of positive selection at height-associated loci within and across populations, which remain significant after accounting for ancient admixture events and is largely driven by allele frequency differences between the Netherlands and other European ancestry populations. Altogether, our results provide new and converging evidence of polygenic adaptation on height in Europe.

genetics↗

bioRxiv2025

Separating direct, indirect and parent-of-origin genetic effects in the human population

Here, we present a novel approach to estimate the degree to which the phenotypic effect of a DNA locus is attributable to four components: alleles in the child (direct genetic effects), alleles in the mother and the father (indirect genetic effects), or is dependent upon the parent from which it is inherited (parent-of-origin, PofO effects). Applying our model, JODIE, to 30,000 child-mother-father trios with phased DNA information from the Estonian Biobank (EstBB) and the Norwegian Mother, Father, Child Cohort (MoBa), we jointly estimate the phenotypic variance attributable to these four effects unbiased of assortative mating (AM) for height, body mass index (BMI) and childhood educational test score (EA). For all three traits, direct effects make the largest contribution to the genetic effect variance. But we find that parental indirect genetic effects make an equivalent combined contribution, and that there is a non-zero PofO effect variance for all traits. We calculate the heritability that would be obtained at the populationlevel in the absence of AM for common DNA loci, and show that the proportional contribution of direct effects to these heritability values can be calculated as 64.0% for EA in MoBa, 77.1% and 63.4% for height in MoBa and EstBB, and 81.2% and 88.0% for BMI in MoBa and EstBB. Additionally, using within-family genome-wide association testing, we identify 276 independently associated DNA regions that replicate across two additional biobanks, which all show a genotype-phenotype relationship that reflects an interplay of direct, indirect and PofO effects. Determining how direct, parental and PofO genetic effects combine across loci genome-wide to influence human phenotypic variation requires joint modeling of parental and child genotypes alongside the parental origin of loci and here, we make the first attempt to do this in the human population.

genetics↗

bioRxiv2024

Confounding of indirect genetic and epigenetic effects

An individuals phenotype reflects a complex interplay of the direct effects of their DNA, epigenetic modifications of their DNA induced by their parents, and indirect effects of their parents DNA. Here, we derive how the genetic variance within a population is changed under the influence of indirect maternal, paternal and parent-of-origin effects under random mating. We also consider indirect effects of a sibling, in particular how the genetic variance is altered when looking at the phenotypic difference between two siblings. The calculations are then extended to include assortative mating (AM), which alters the variance by inducing increased homozygosity and correlations within and across loci. AM likely leads to covariance of parental genetic effects, a measure of the similarity of parents in the indirect effects they have on their children. We propose that this assortment for parental characteristics, where biological parents create similar environments for their children, can create shared parental effects across traits and the appearance of cross-trait AM. Interestingly, the genetic variances is increased under AM for the childmother-father design, while it is decreased for the sibling difference. Our results demonstrate that it is not possible to get unbiased estimates of direct genetic effects without controlling for parental and parent-of-origin effects.

genetics↗

bioRxiv2024

Discovery of shared epigenetic pathways across human phenotypes

Omics-based association studies typically consider the marginal effects of a feature, such as CpG DNA methylation, on a trait (e.g, independent models for each feature). Although some methods can assess all features together in joint and conditional estimation, this is currently done on a trait-by-trait basis. Here, we introduce MAJA, a method to learn shared and outcome-specific effects for multiple traits in multi-omics data. MAJA determines the unique contribution of individual loci, genes, or molecular pathways, to variation in one or more traits, conditional on all other measured "omics" data genome-wide. Simulations show MAJA accurately finds shared and distinct associations between omics-data and multiple traits and estimates omics-specific (co)variances, allowing for sparsity and correlations within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation data (n=18,264), we find novel shared epigenetic pathways among cholesterol metabolism, osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we find only 10 CpG probes with significant effects above the genome-wide background. This highlights the need for joint association testing in highly correlated methylation data from whole blood and for studies of increased sample size in order to refine epigenomic associations in observational data.

genetics↗

bioRxiv2023

Causal inference for multiple risk factors and diseases from genomics data

Statistical causal learning in genome-wide association studies (GWAS) relies on the instrumental variable method of Mendelian Randomization (MR). Currently, an over-whelming number of MR studies purport to show causal relationships among a wide range of risk factors and outcomes. Here, we find that naive application of many recently proposed MR approaches results in numerous null relationships being discovered as highly significant. We show that a well-controlled error rate can be achieved through a graphical inference approach which: (i) selects a set of genetic instrumental variables (IVs) from GWAS summary static controlling for LD, linkage and pleiotropy; (ii) accommodates rare variants and binary outcomes in a principled way; (iii) distinguishes direct from indirect risk factors in very high-dimensional data; and (iv) identifies potential unobserved latent confounding. Only 20 minutes of wall-clock compute time is required for our Causal Inference GWAS (CI-GWAS) approach to jointly analyze a set of 9 common health risk factors, four common complex metabolic disease outcomes and 8.4M genetic variants recorded for 458,747 individuals in the UK Biobank. Genome-wide, we find that very few genetic variants are suitable MR IVs, with only 696 variants remaining when analyzing all traits jointly. While we replicate almost all paths previously found by CAUSE between risk factors and outcomes, we show that only few of these reflect direct adjacencies and that many cannot be distinguished from unmeasured confounding within the UK Biobank data. Our results suggest that well-curated longitudinal records and family data are likely needed to overcome the mixtures of temporal precedence and reverse-causality in biobank data. Our approach provides a first-step toward robust principled screening for potential causal links to understand the underlying nature of phenotypic correlations in biobank data.

genetics↗

bioRxiv2023

Light-speed Inference of Genetic Effects via Approximate Message Passing

Human height is a model for the genetic analysis of complex traits, and recent studies suggest the presence of thousands of common genetic variant associations and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic paradigm based on approximate message passing, gVAMP, for identifying DNA sequence variants associated with complex traits and common diseases in large-scale whole genome sequence (WGS) data. We show that gVAMP accurately localizes associations to variants with the correct frequency and position in the DNA, outperforming existing fine-mapping methods in selecting the appropriate genetic variants within WGS data. We then apply gVAMP to jointly model the relationship of tens of millions of WGS variants with human height in hundreds of thousands of UK Biobank individuals. We identify 59 rare variants and gene burden scores alongside many hundreds of DNA regions containing common variant associations, and show that understanding the genetic basis of complex traits will require the joint analysis of hundreds of millions of variables measured on millions of people. The polygenic risk scores obtained from gVAMP have high accuracy (including a prediction accuracy of [~] 46% for human height) and outperform current methods for downstream tasks such as mixed linear model association testing across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation towards a wider range of analyses in WGS data.

genetics↗

bioRxiv2022

Refining epigenetic prediction of chronological and biological age

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to refine predictors and improve understanding of the epigenomic architecture of cAge and bAge. First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to improve cAge prediction, we use methylation data from 24,673 participants from the Generation Scotland (GS) study, the Lothian Birth Cohorts (LBC) of 1921 and 1936 and 8 publicly available datasets. Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection/dimensionality reduction in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross validation framework, we arrive at an improved cAge predictor (median absolute error = 2.3 years across 10 cohorts). In addition, we train a predictor of bAge on 1,214 all-cause mortality events in GS, based on epigenetic surrogates for 109 plasma proteins and the 8 component parts of GrimAge, the current best epigenetic predictor of all-cause mortality. We test this predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Womens Health Initiative study) where it outperforms GrimAge in its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 x 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 x 10-60). Finally, we introduce MethylBrowsR, an online tool to visualize epigenome-wide CpG-age associations.

genomics↗

bioRxiv2021

Maximizing GWAS discovery and genomic prediction accuracy in Biobank data

Genetically informed and deep-phenotyped biobanks are an important research resource. The cost of phenotyping far outstrips that of genotyping, and therefore it is imperative that the most powerful, versatile and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. On average, GMRM accuracies were 15% (SE 7%) greater than prediction models run in the LDAK software with SNP annotation marker groups, 18% (SE 3%) greater than a baseline BayesR model without SNP markers grouped into MAF-LD-annotation categories, and 106% (SE 9%) greater than polygenic risk scores calculated from mixed-linear model association (MLMA) estimates. For height, the prediction accuracy R2 was 47% in a UK Biobank hold-out sample, which was 76% of the estimated [Formula]. We then extend our GMRM prediction model to provide MLMA SNP marker estimates for GWAS discovery, which increased the independent loci detected to 7,910 in unrelated UK Biobank individuals, as compared to 5,521 from BoltLMM and 5,727 from Regenie, a 43% and 38% increase respectively. The average{chi} 2 value of the leading markers was 34% (SE 5.11) higher for GMRM as compared to Regenie, and increased by 17% for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modelling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and for discovery in large-scale individual-level biobank-scale studies.

genomics↗

bioRxiv2021

Pharmacologic Therapy for Engraftment Arrhythmia Induced by Transplantation of Human Cardiomyocytes

BackgroundEngraftment arrhythmias (EAs) are observed in large animal studies of intramyocardial transplantation of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) for myocardial infarction. Although transient, the risk posed by EA presents a barrier to clinical translation. ObjectivesWe hypothesized that clinically approved antiarrhythmic drugs can prevent EA-related mortality as well as suppress tachycardia and arrhythmia burden. MethodshPSC-CM were transplanted into the infarcted porcine heart by surgical or percutaneous delivery to induce EA. Following a screen of antiarrhythmic agents, a prospective study was conducted to determine the effectiveness of amiodarone plus ivabradine in preventing cardiac death and suppressing EA. ResultsEA was observed in all subjects, and amiodarone-ivabradine treatment was well-tolerated. None of the treated subjects experienced the primary endpoint of cardiac death, unstable EA or heart failure compared to 5/8 (62.5%) in the control cohort (hazard ratio 0.00; 95% confidence interval, 0-0.297; p = 0.002). Overall survival including two deaths in the treated cohort from immunosuppression-related infection showed borderline improvement with treatment (hazard ratio 0.21; 95% confidence interval, 0.03-1.01; p = 0.05). Without treatment, peak heart rate averaged 305 {+/-} 29 beats per min (bpm), whereas in treated subjects peak daily heart rate was significantly restricted to 185{+/-}9 bpm (p = 0.006). Similarly, treatment reduced peak daily EA burden from 96.8 {+/-} 2.9% to 76.5 {+/-} 7.9% (p = 0.003). Antiarrhythmic treatment was safely discontinued after approximately one-month of treatment without recrudescence of arrhythmia. ConclusionsThe risk of engraftment arrhythmia following hPSC-CM transplantation can be reduced significantly by combined amiodarone and ivabradine drug therapy. Condensed AbstractEngraftment arrhythmia (EA) is a transient but serious complication of cardiac remuscularization therapy. Using a porcine model of cardiac remuscularization and EA, ivabradine and amiodarone were independently effective in suppressing tachycardia and arrhythmia, respectively. Baseline amiodarone combined with adjunctive ivabradine successfully prevented cardiac death, unstable EA and heart failure (hazard ratio 0.00; 95% confidence interval, 0-0.297; p = 0.002) and significantly suppressed peak daily heart rate and arrhythmia burden (p=0.006 and 0.003, respectively). Antiarrhythmic treatment was successfully discontinued after one-month without recrudescence of arrhythmia. We conclude that EA can be suppressed by combined amiodarone and ivabradine drug therapy.

pharmacology and toxicology↗

bioRxiv2020

Targeting lateral inhibition to improve vision following macular degeneration

Macular degeneration is the leading cause of blindness in the developed world. Whilst most patients lose sight owing to atrophic changes, no treatments currently exist that improve the vision deficit due to atrophy. Here, we identify loss of lateral inhibition as a specific mechanism by which photoreceptor degeneration reduces visual function beyond the atrophic area. We find that this inhibition is adaptive, and that if modulated can improve visual function, making inhibitory circuits an unexpected therapeutic target for age related macular degeneration and related disorders.

neuroscience↗

bioRxiv2020

Integrative omics approach to identify the molecular architecture of inflammatory protein levels in healthy older adults

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. Therefore, in this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink(R) inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified three CpG sites spread across three proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to one polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease, and between IL12B and Crohns disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

genomics↗

bioRxiv2019

Tyr1 phosphorylation promotes the phosphorylation of Ser2 on the C-terminal domain of RNA polymerase II by P-TEFb

The Positive Transcription Elongation Factor b (P-TEFb) phosphorylates Ser2 residues of RNA polymerase IIs C-terminal domain (CTD) and is essential for the transition from transcription initiation to elongation in vivo. Surprisingly, P-TEFb exhibits Ser5 phosphorylation activity in vitro. The mechanism garnering Ser2 specificity to P-TEFb remains elusive and hinders understanding of the transition from transcription initiation to elongation. Through in vitro reconstruction of CTD phosphorylation, mass spectrometry analysis, and chromatin immunoprecipitation sequencing (ChIP-seq) analysis we uncover a mechanism by which Tyr1 phosphorylation directs the kinase activity of P-TEFb and alters its specificity from Ser5 to Ser2. The loss of Tyr1 phosphorylation causes a reduction of phosphorylated Ser2 and accumulation of RNA polymerase II in the promoter region as detected by ChIP-seq. We demonstrate the ability of Tyr1 phosphorylation to generate a heterogeneous CTD modification landscape that expands the CTDs coding potential. These findings provide direct experimental evidence for a combinatorial CTD phosphorylation code wherein previously installed modifications direct the identity and abundance of subsequent coding events by influencing the behavior of downstream enzymes.

biochemistry↗

bioRxiv2018

Integrative haplotype estimation with sub-linear complexity

The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here, we present a new method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear scaling with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPET4 in an open source format on https://odelaneau.github.io/shapeit4/ and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

bioinformatics↗

bioRxiv2018

Extensive mitochondrial population structure and haplotype-specific phenotypic variation in the Drosophila Genetic Reference Panel

The Drosophila Genetic Reference Panel (DGRP) serves as a valuable resource to better understand the genetic landscapes underlying quantitative traits. However, such DGRP studies have so far only focused on nuclear genetic variants. To address this, we sequenced the mitochondrial genomes of >170 DGRP lines, identifying 229 variants including 21 indels and 7 frameshifts. We used our mitochondrial variation data to identify 12 genetically distinct mitochondrial haplotypes, thus revealing important population structure at the mitochondrial level. We further examined whether this population structure was reflected on the nuclear genome by screening for the presence of potential mito-nuclear genetic incompatibilities in the form of significant genotype ratio distortions (GRDs) between mitochondrial and nuclear variants. In total, we detected a remarkable 1,845 mito-nuclear GRDs, with the highest enrichment observed in a 40 kb region around the gene Sex-lethal (Sxl). Intriguingly, downstream phenotypic analyses did not uncover major fitness effects associated with these GRDs, suggesting that a large number of mito-nuclear GRDs may reflect population structure at the mitochondrial level rather than actual genomic incompatibilities. This is further supported by the GRD landscape showing particular large genomic regions associated with a single mitochondrial haplotype. Next, we explored the functional relevance of the detected mitochondrial haplotypes through an association analysis on a set of 259 assembled, non-correlating DGRP phenotypes. We found multiple significant associations with stress- and metabolism-related phenotypes, including food intake in males. We validated the latter observation by reciprocal swapping of mitochondrial genomes from high food intake DGRP lines to low food intake ones. In conclusion, our study uncovered important mitochondrial population structure and haplotype-specific metabolic variation in the DGRP, thus demonstrating the significance of incorporating mitochondrial haplotypes in geno-phenotype relationship studies.

genomics↗

bioRxiv2018

Imprint of Assortative Mating on the Human Genome

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fishers classical theory1 to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation ({theta}) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from [~]400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height ({theta}=3.2%) and educational attainment ({theta}=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

genetics↗