Publications by authors named "Lloyd T Elliott"

10 Publications

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Estimating Genetic Similarity Matrices Using Phylogenies.

J Comput Biol 2021 Jun 29;28(6):587-600. Epub 2021 Apr 29.

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada.

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http://dx.doi.org/10.1089/cmb.2020.0375DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219189PMC
June 2021

An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.

Nat Neurosci 2021 05 19;24(5):737-745. Epub 2021 Apr 19.

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada.

UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer's disease and mitochondrial disorders.
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http://dx.doi.org/10.1038/s41593-021-00826-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610742PMC
May 2021

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

Stroke 2020 07 10;51(7):2111-2121. Epub 2020 Jun 10.

Department of Psychiatry (C.F.-N.), University of California, San Diego, La Jolla, CA.

Background And Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.

Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.

Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (), 10q23.1 (), and 10q24.33 ( In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 () and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: (2q32.1), (3q27.1), (5q27.1), and (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.

Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
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http://dx.doi.org/10.1161/STROKEAHA.119.027544DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365038PMC
July 2020

Kinship Solutions for Partially Observed Multiphenotype Data.

Authors:
Lloyd T Elliott

J Comput Biol 2020 09 10;27(9):1461-1470. Epub 2020 Mar 10.

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada.

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http://dx.doi.org/10.1089/cmb.2019.0440DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482112PMC
September 2020

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations.

Elife 2020 03 5;9. Epub 2020 Mar 5.

Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom.

Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.
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http://dx.doi.org/10.7554/eLife.52677DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162660PMC
March 2020

The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure.

Nat Neurosci 2019 05 15;22(5):809-819. Epub 2019 Apr 15.

Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1-13%, or r = 0.1-0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure-function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas.
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http://dx.doi.org/10.1038/s41593-019-0379-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517273PMC
May 2019

The UK Biobank resource with deep phenotyping and genomic data.

Nature 2018 10 10;562(7726):203-209. Epub 2018 Oct 10.

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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http://dx.doi.org/10.1038/s41586-018-0579-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786975PMC
October 2018

Genome-wide association studies of brain imaging phenotypes in UK Biobank.

Nature 2018 10 10;562(7726):210-216. Epub 2018 Oct 10.

Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.

The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.
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http://dx.doi.org/10.1038/s41586-018-0571-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786974PMC
October 2018

Probing principles of large-scale object representation: category preference and location encoding.

Hum Brain Mapp 2013 Jul 27;34(7):1636-51. Epub 2012 Feb 27.

Bernstein Center for Computational Neuroscience Berlin, Charité-Universitätsmedizin Berlin, Germany.

Knowledge about the principles that govern large-scale neural representations of objects is central to a systematic understanding of object recognition. We used functional magnetic resonance imaging (fMRI) and multivariate pattern classification to investigate two such candidate principles: category preference and location encoding. The former designates the preferential activation of distinct cortical regions by a specific category of objects. The latter refers to information about where in the visual field a particular object is located. Participants viewed exemplars of three object categories (faces, bodies, and scenes) that were presented left or right of fixation. The analysis of fMRI activation patterns revealed the following. Category-selective regions retained their preference to the same categories in a manner tolerant to changes in object location. However, category preference was not absolute: category-selective regions also contained location-tolerant information about nonpreferred categories. Furthermore, location information was present throughout high-level ventral visual cortex and was distributed systematically across the cortical surface. We found more location information in lateral-occipital cortex than in ventral-temporal cortex. Our results provide a systematic account of the extent to which the principles of category preference and location encoding determine the representation of objects in the high-level ventral visual cortex.
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http://dx.doi.org/10.1002/hbm.22020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6870376PMC
July 2013

Cortical surface-based searchlight decoding.

Neuroimage 2011 May 23;56(2):582-92. Epub 2010 Jul 23.

Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Local voxel patterns of fMRI signals contain specific information about cognitive processes ranging from basic sensory processing to high level decision making. These patterns can be detected using multivariate pattern classification, and localization of these patterns can be achieved with searchlight methods in which the information content of spherical sub-volumes of the fMRI signal is assessed. The only assumption made by this approach is that the patterns are spatially local. We present a cortical surface-based searchlight approach to pattern localization. Voxels are grouped according to distance along the cortical surface-the intrinsic metric of cortical anatomy-rather than Euclidean distance as in volumetric searchlights. Using a paradigm in which the category of visually presented objects is decoded, we compare the surface-based method to a standard volumetric searchlight technique. Group analyses of accuracy maps produced by both methods show similar distributions of informative regions. The surface-based method achieves a finer spatial specificity with comparable peak values of significance, while the volumetric method appears to be more sensitive to small informative regions and might also capture information not located directly within the gray matter. Furthermore, our findings show that a surface centered in the middle of the gray matter contains more information than to the white-gray boundary or the pial surface.
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http://dx.doi.org/10.1016/j.neuroimage.2010.07.035DOI Listing
May 2011