23 papers · ranked by Valyu relevance
Stephen Burgess, Simon G. Thompson
A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular…
Claudia Giambartolomei, Damjan Vukcevic, Eric E. Schadt, Lude Franke + 3 more
'Aroon D. Hingorani' 'Chris Wallace' 'Vincent Plagnol'] Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The…
Qingbo S. Wang, Hailiang Huang
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS…
Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Ayellet V. Segrè + 7 more
Recent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal…
Nanye Long, Samuel P. Dickson, Jessica M. Maia, Hee Shin Kim + 3 more
'Qianqian Zhu' 'Andrew S. Allen' 'Wyeth W. Wasserman'] Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method…
Susanna C Larsson, Adam S Butterworth, Stephen Burgess
Large-scale genome-wide association studies conducted over the last decade have uncovered numerous genetic variants associated with cardiometabolic traits and risk factors. These discoveries have enabled the Mendelian randomization (MR) design, which uses genetic variation as a natural experiment to improve causal…
Feng Zhou, Opeyemi Soremekun, Tinashe Chikowore, Segun Fatumo + 3 more
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary…
Feng Zhou, Opeyemi Soremekun, Tinashe Chikowore, Segun Fatumo + 3 more
'Inês Barroso' 'Andrew P. Morris' 'Jennifer L. Asimit'] Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between…
Ichcha Manipur, Guillermo Reales, Jae Hoon Sul, Myung Kyun Shin + 3 more
Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS…
Joseph K. Pickrell, Tomaz Berisa, Laure Segurel, Joyce Y. Tung + 1 more
We performed a genome-wide scan for genetic variants that influence multiple human phenotypes by comparing large genome-wide association studies (GWAS) of 40 traits or diseases, including anthropometric traits (e.g. nose size and male pattern baldness), immune traits (e.g. susceptibility to childhood ear infections and…
Fatima Batool, Ashish Patel, Dipender Gill, Stephen Burgess
This research was funded by United Kingdom Research and Innovation Medical Research Council (MC UU 00002 7 and MR/S037675/1) and supported by the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215-20014). Fatima Batool and Stephen Burgess are supported by a Sir Henry Dale Fellowship…
Chad Shaw, Christopher Williams, Tao Tao Tan, Nicholas Di + 2 more
We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages one established causal factor to detect the contribution of a second suspected cause. Specifically, polygenic risk scores (PRS) serve as known causes, while rare variants (RV) or…
Zhaoran Wang, David M. Blei
Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that…
Jessica M. B. Rees, Angela Wood, Stephen Burgess
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the…
Pengfei Hu, Rong Jiao, Li Jin, Momiao Xiong
The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the…
Sophie Hackinger, Eleftheria Zeggini
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now…
Luke J. O’Connor, Alkes L. Price
Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Apostolos Gkatzionis, Stephen Burgess, Paul J. Newcombe
Mendelian randomization is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data Mendelian randomization analyses with many correlated variants from a single gene region, and particularly on cis-Mendelian…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Marina Gorostiola González, Olivier J. M. Béquignon, Emma Manners, Anna Gaulton + 7 more
Bioactivity prediction is essential in computational drug discovery, particularly within virtual screening campaigns. Despite advancements in model architectures and features, the sparsity and quality of relevant training data remain a major bottleneck. Notably, genetic variance annotation, crucial for understanding…
Valeria A. Risso, Adrian Romero-Rivera, Luis I. Gutierrez-Rus, Mariano Ortega-Muñoz + 4 more
Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated…
Pavel Kohout, Michal Vasina, Marika Majerova, Veronika Novakova + 5 more
Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights, leveraging homologous sequences to pinpoint conserved and functionally…