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Article ; Online: Comparison of regmed and BayesNetty for exploring causal models with many variables.

Howey, Richard / Cordell, Heather J

2023 Volume 47, Issue 7, Page(s) 496–502

Abstract: Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed ... ...

Abstract	Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as regmed is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of regmed can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying regmed to the resulting "filled-in" data set.
MeSH term(s)	Humans ; Bayes Theorem ; Models, Genetic
Language	English
Publishing date	2023-06-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22532
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Article: Confirmation of the superior performance of the causal Graphical Analysis Using Genetics (cGAUGE) pipeline in comparison to various competing alternatives.

Howey, Richard / Cordell, Heather J

Wellcome open research

2022 Volume 7, Page(s) 180

Abstract: Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently ... ...

Abstract	Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently proposed causal Graphical Analysis Using Genetics (cGAUGE) pipeline, used as a precursor to Mendelian randomization analysis, in comparison to our previously proposed Bayesian Network approach for addressing this problem. We use the same simulation (and analysis) code as was used by the developers of cGAUGE, adding in a comparison with the Bayesian Network approach. Overall, we find the optimal method (in terms of giving high power and low false discovery rate) is the cGAUGE pipeline followed by subsequent analysis using the MR-PRESSO Mendelian randomization approach.
Language	English
Publishing date	2022-07-05
Publishing country	England
Document type	Journal Article
ISSN	2398-502X
ISSN	2398-502X
DOI	10.12688/wellcomeopenres.17991.1
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Article ; Online: Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.

Fryett, James J / Morris, Andrew P / Cordell, Heather J

Genetic epidemiology

2022 Volume 46, Issue 8, Page(s) 629–643

Abstract: As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach ... ...

Abstract	As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.
MeSH term(s)	Humans ; Polymorphism, Single Nucleotide ; Multifactorial Inheritance ; Genome-Wide Association Study/methods ; Models, Genetic ; DNA Methylation/genetics ; Genotype ; Transcriptome ; CpG Islands/genetics
Language	English
Publishing date	2022-08-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22496
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Article ; Online: The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective.

Vaskimo, Lotta M / Gomon, Georgy / Naamane, Najib / Cordell, Heather J / Pratt, Arthur / Knevel, Rachel

Genes

2023 Volume 14, Issue 12

Abstract: Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview ... ...

Abstract	Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
MeSH term(s)	Humans ; Genetic Predisposition to Disease ; Genetic Risk Score ; Risk Factors ; Phenotype ; Rheumatic Diseases/genetics
Language	English
Publishing date	2023-12-01
Publishing country	Switzerland
Document type	Review ; Journal Article
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes14122167
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Article ; Online: Summary of results and discussions from the gene-based tests group at Genetic Analysis Workshop 18.

Cordell, Heather J

Genetic epidemiology

2014 Volume 38 Suppl 1, Page(s) S44–8

Abstract: I present a summary of the results and discussions held within the working group on gene-based tests at Genetic Analysis Workshop 18 (GAW18). The main focus of interest in our working group was modeling the action of combinations or "groups" of genetic ... ...

Abstract	I present a summary of the results and discussions held within the working group on gene-based tests at Genetic Analysis Workshop 18 (GAW18). The main focus of interest in our working group was modeling the action of combinations or "groups" of genetic variants, with a group of variants most often defined as a set of single-nucleotide polymorphisms lying within a known gene. Some contributions investigated the performance of previously proposed methods (particularly rare variant collapsing or burden-type methods) for addressing this question, applied to the GAW18 data, and other contributions developed novel approaches and addressed novel questions. Most approaches were successful in detecting significant effects at MAP4 in the simulated data. No other genetic effects were consistently detected across different analyses. Low power was noted, particularly for those methods that restricted analysis to purely the subset of unrelated individuals.
MeSH term(s)	Blood Pressure/genetics ; Education ; Genetic Testing ; Genetic Variation ; Humans ; Microtubule-Associated Proteins/genetics ; Polymorphism, Single Nucleotide
Chemical Substances	MAP4 ; Microtubule-Associated Proteins
Language	English
Publishing date	2014-08-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.21824
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Article ; Online: Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.

Fryett, James J / Morris, Andrew P / Cordell, Heather J

Genetic epidemiology

2020 Volume 44, Issue 5, Page(s) 425–441

Abstract: In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the ... ...

Abstract	In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods-LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests-by performing cross-validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.
MeSH term(s)	Bayes Theorem ; Genome-Wide Association Study/methods ; Genome-Wide Association Study/standards ; Genotype ; Humans ; Models, Genetic ; Models, Statistical ; Pedigree ; Phenotype ; Reproducibility of Results ; Sample Size ; Transcriptome
Language	English
Publishing date	2020-03-19
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22290
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Article ; Online: Rare complement factor I variants associated with reduced macular thickness and age-related macular degeneration in the UK Biobank.

Tzoumas, Nikolaos / Kavanagh, David / Cordell, Heather J / Lotery, Andrew J / Patel, Praveen J / Steel, David H

Human molecular genetics

2022 Volume 31, Issue 16, Page(s) 2678–2692

Abstract: To evaluate potential diagnostic and therapeutic biomarkers for age-related macular degeneration (AMD), we identified 8433 UK Biobank participants with rare complement Factor I gene (CFI) variants, 579 with optical coherence tomography-derived macular ... ...

Abstract	To evaluate potential diagnostic and therapeutic biomarkers for age-related macular degeneration (AMD), we identified 8433 UK Biobank participants with rare complement Factor I gene (CFI) variants, 579 with optical coherence tomography-derived macular thickness data. We stratified these variants by predicted gene expression and measured their association with retinal pigment epithelium-Bruch's membrane (RPE-BM) complex and retinal thicknesses at nine macular subfields, as well as AMD risk, using multivariable regression models adjusted for the common complement Factor H gene (CFH) p.Y402H and age-related maculopathy susceptibility protein 2 gene (ARMS2) p.A69S risk genotypes. CFI variants associated with low Factor I levels predicted a thinner mean RPE-BM (95% confidence interval [CI] -1.66 to -0.37 μm, P = 0.002) and retina (95% CI -5.88 to -0.13 μm, P = 0.04) and a higher AMD risk (odds ratio [OR] = 2.26, 95% CI 1.56 to 3.27, P < 0.001). CFI variants associated with normal Factor I levels did not impact mean RPE-BM/retinal thickness (P = 0.28; P = 0.99) or AMD risk (P = 0.97). CFH p.Y402H was associated with a thinner RPE-BM (95% CI -0.31 to -0.18 μm, P < 0.001 heterozygous; 95% CI -0.62 to -0.42 μm, P < 0.001 homozygous) and retina (95% CI -0.73 to -0.12 μm, P = 0.007 heterozygous; 95% CI -1.08 to -0.21 μm, P = 0.004 homozygous). ARMS2 p.A69S did not influence RPE-BM (P = 0.80 heterozygous; P = 0.12 homozygous) or retinal thickness (P = 0.75 heterozygous; P = 0.07 homozygous). p.Y402H and p.A69S exhibited a significant allele-dose response with AMD risk. Thus, CFI rare variants associated with low Factor I levels are robust predictors of reduced macular thickness and AMD. The observed association between macular thickness and CFH p.Y402H, but not ARMS2 p.A69S, highlights the importance of complement dysregulation in early pathogenesis.
MeSH term(s)	Biological Specimen Banks ; Complement Factor H/genetics ; Complement Factor I/genetics ; Fibrinogen/genetics ; Genotype ; Humans ; Macular Degeneration/genetics ; Polymorphism, Single Nucleotide/genetics ; United Kingdom
Chemical Substances	Complement Factor H (80295-65-4) ; Fibrinogen (9001-32-5) ; Complement Factor I (EC 3.4.21.45)
Language	English
Publishing date	2022-03-11
Publishing country	England
Document type	Journal Article
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddac060
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Article: Further investigations of the W-test for pairwise epistasis testing.

Howey, Richard / Cordell, Heather J

Wellcome open research

2017 Volume 2, Page(s) 54

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2017-07-21
Publishing country	England
Document type	Journal Article
ISSN	2398-502X
ISSN	2398-502X
DOI	10.12688/wellcomeopenres.11926.1
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Article ; Online: Genetic variants associated with leaf spot disease resistance in oil palm (Elaeis guineensis): A genome‐wide association study

Wibowo, Cahyo S. / Apriyanto, Ardha / Ernawan, Reza / Neing, Dionisius / Susilo, Ricki / Cordell, Heather J. / Gatehouse, A. M. R. / Edwards, Martin G.

Plant Pathology. 2023 Dec., v. 72, no. 9 p.1626-1636

2023

Abstract: Leaf spot is considered as a common disease of oil palm, caused primarily by Curvularia spp. fungi. This disease mainly affects the early stages of oil palm and if not adequately controlled can cause plant death. Among the methods available to control ... ...

Abstract	Leaf spot is considered as a common disease of oil palm, caused primarily by Curvularia spp. fungi. This disease mainly affects the early stages of oil palm and if not adequately controlled can cause plant death. Among the methods available to control the disease, breeding resistant varieties is the most economically effective and promising strategy. A genome‐wide association study for leaf spot resistance was conducted on 210 individual tenera palms from seven different (origin) crosses. These palms were subsequently infected with Curvularia spp. pathogenic inoculum in a nursery trial located in an endemic area. The area under the disease progress curve was used as a phenotypic measure. In addition, a genotyping‐by‐sequencing (GBS) approach was used to obtain the genotyping data of each individual. We found two loci, at chromosome 2 and chromosome 13, that were significantly associated with leaf spot disease resistance. Six genetic variants at the two loci (five variants at chromosome 2 and one variant at chromosome 13) surpassed the threshold for genome‐wide significance (p < 10⁶). These loci are linked with three widely known disease‐related genes, namely, resistance gene analogue 3 (RGA3), resistance gene analogue 4 (RGA4) and receptor‐like protein 9a (RLP9a). The loci identified here can be used for marker‐assisted selection when developing leaf spot disease‐resistant oil palm varieties.
Keywords	Curvularia ; Elaeis guineensis ; chromosomes ; death ; disease progression ; disease resistance ; genome-wide association study ; genotyping by sequencing ; inoculum ; leaf spot ; marker-assisted selection ; phenotype ; plant pathology ; resistance genes
Language	English
Dates of publication	2023-12
Size	p. 1626-1636.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	415941-x
ISSN	1365-3059 ; 0032-0862
ISSN (online)	1365-3059
ISSN	0032-0862
DOI	10.1111/ppa.13774
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Correction: Comparison of methods for transcriptome imputation through application to two common complex diseases.

Fryett, James J / Inshaw, Jamie / Morris, Andrew P / Cordell, Heather J

European journal of human genetics : EJHG

2020 Volume 28, Issue 8, Page(s) 1135–1136

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

Abstract	An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Language	English
Publishing date	2020-03-17
Publishing country	England
Document type	Published Erratum
ZDB-ID	1141470-4
ISSN	1476-5438 ; 1018-4813
ISSN (online)	1476-5438
ISSN	1018-4813
DOI	10.1038/s41431-020-0605-0
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