Using Genetics to Identify Causal Pathways that Influence Bone Related Phenotypes in Children and Young Adults.

Lead Research Organisation: University of Bristol

Abstract

Our aim is to identify genes and biological pathways that influence the density of bones in children and young adults, and consequently future risk of osteoporosis (a disease characterized by thin and brittle bones). This is important because approximately half of British women and one in five men will suffer from osteoporosis at some stage in their lives, costing the NHS in excess of £1.8 billion. Currently most of our knowledge concerning the genes influencing osteoporosis has come from studies of older individuals. These genes are thought to regulate bone loss. However there is a growing realization that it is also important to study bone growth in children and young adults. This is because the genes that contribute to bone growth may be different from the ones that affect bone loss, and how dense an individual’s bones are in young adulthood is a strong predictor of whether they will develop osteoporosis in later life. We will identify genes which affect bone density by correlating measures of bone with millions of genetic markers in thousands of children and young adults. The identification of genes and biological pathways that influence osteoporosis represents the first step in developing new therapies to help treat the condition.

Technical Summary

This Programme will focus on identifying genetic variants and biological pathways that causally influence bone related phenotypes in children and young adults. Bone mineral density (BMD) as measured by Dual X-ray Absorptiometry (DXA) is the primary diagnostic and prognostic marker for osteoporosis and fracture susceptibility in adults as it quantifies the relative strength and fracture risk of the measured bone. Growing evidence from our lab and our collaborators suggest that there is considerable value in examining BMD in children and young adults. This is important, not only because peak bone mass is one of the strongest known predictors of future osteoporosis, but also because the genetic variants that influence bone acquisition may differ in strength and identity from those that influence bone maintenance and bone loss in later life. Given that osteoporosis will at some stage affect approximately half of British women and one in five men, it is important to study BMD throughout the life course so that one can completely understand the factors influencing bone and consequently future risk of osteoporosis.
Genetic variation affecting bone-related phenotypes will be identified using a combination of cutting edge genetic technologies including next generation sequencing and high resolution bone-related phenotypes that have been measured longitudinally in the Avon Longitudinal Study of Parents and Children (ALSPAC). Data will be analysed using a combination of advanced and established statistical methodologies which will then be meta-analyzed and replicated in collaboration with cohort studies with similar data. Genetic loci that influence gene methylation, expression, and metabolites will be identified through the genetic association analysis of genome-wide SNP and sequencing data within ALSPAC. Using a combination of new and existing statistical methodology, allelic scores that index levels of these molecular phenotypes will be constructed, validated and subsequently tested for association with bone mineral density (BMD) and osteoporosis, identifying potentially modifiable intermediate biological pathways affecting these phenotypes. Formal Mendelian randomization analysis and extensions of this approach will be performed to confirm that these associations are likely to represent causal relationships. The Programme will use pre-existing data that has been funded from a large number of sources and many grants that will still be active when the Unit starts. The Programme will require the support of postdoctoral research staff for its aims to be realised, and the analysis pipelines and protocols generated within it will serve as a platform for the genetic analysis of the thousands of other phenotypes within the ALSPAC cohort, benefiting the many investigators, both internal and external to the Unit, who use the ALSPAC resource for their own genetic investigations.

Publications

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Brown MA (2019) Vitamin D-Binding Protein Deficiency and Homozygous Deletion of the GC Gene. in The New England journal of medicine

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Byrne EM (2014) Applying polygenic risk scores to postpartum depression. in Archives of women's mental health

 
Description Arthritis Research UK Project Grant
Amount £183,041 (GBP)
Organisation Versus Arthritis 
Sector Charity/Non Profit
Country United Kingdom
Start 01/2015 
End 12/2016
 
Description Australian Infectious Disease Network Seed Grant
Amount $50,000 (AUD)
Organisation Australian Infectious Disease Network (AID) 
Sector Multiple
Country Australia
Start 01/2016 
End 12/2016
 
Description Australian Research Council Future Fellowship
Amount $823,692 (AUD)
Funding ID FT130101709 
Organisation Australian Research Council 
Sector Public
Country Australia
Start 12/2013 
End 11/2017
 
Description Developing and Applying Statistical Genetics Methods to Elucidate the Developmental Origins of Health and Disease
Amount $327,387 (AUD)
Funding ID 1183074 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 07/2020 
End 06/2023
 
Description National Health and Medical Research Council Fellowship
Amount $707,370 (AUD)
Funding ID APP1137714 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2018 
End 12/2022
 
Description National Health and Medical Research Council Project Grant
Amount $276,398 (AUD)
Funding ID APP1085130 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2015 
End 12/2017
 
Description National Health and Medical Research Council Project Grant
Amount $834,269 (AUD)
Funding ID APP1085159 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2015 
End 12/2017
 
Description National Health and Medical Research Council Project Grant
Amount $569,927 (AUD)
Funding ID APP1068023 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2014 
End 12/2015
 
Description National Health and Medical Research Council Project Grant
Amount $830,447 (AUD)
Funding ID APP1142456 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2018 
End 12/2020
 
Description National Health and Medical Research Council Project Grant
Amount $367,992 (AUD)
Funding ID APP1157714 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 01/2019 
End 12/2021
 
Description Project
Amount $279,666 (AUD)
Funding ID APP1125200 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 04/2017 
End 03/2020
 
Description Project Grant
Amount $622,445 (AUD)
Funding ID APP1125141 
Organisation National Health and Medical Research Council 
Sector Public
Country Australia
Start 04/2017 
End 03/2020
 
Description Royal Brisbane and Womens Hospital Foundation Grant
Amount $50,000 (AUD)
Organisation Royal Brisbane & Women's Hospital 
Sector Hospitals
Country Australia
Start 01/2014 
End 12/2015
 
Description UWA-UQ Partnership Research Collaboration Award (PRCA) Scheme
Amount $10,000 (AUD)
Organisation University of Queensland 
Sector Academic/University
Country Australia
Start 01/2015 
End 12/2015
 
Description Wellcome Trust Collaborative Award
Amount £1,565,599 (GBP)
Funding ID 209233_Z_17_Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 09/2018 
End 08/2023
 
Title LDHUb 
Description LDHub is an online utility for performing LD Score regression- a statistical genetics method for estimating the genetic correlation between different traits. 
Type Of Material Data analysis technique 
Year Produced 2016 
Provided To Others? No  
Impact Over 80 citations in a single year 
URL http://ldsc.broadinstitute.org/
 
Title MRBase 
Description MRBase is a website that allows users to perform two sample Mendelian randomization analyses. 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes  
Impact N/A 
URL http://www.mrbase.org/
 
Title Maternal GCTA 
Description A novel statistical method that uses genome-wide SNP data on mothers and offspring in order to estimate the variance explained by maternal effects. 
Type Of Material Data analysis technique 
Year Produced 2014 
Provided To Others? Yes  
Impact N/A 
URL http://www.ncbi.nlm.nih.gov/pubmed/25060210
 
Title Maternal and Offspring Genetic Effects Power Calculator 
Description This website allows users to calculate the power to detect maternal and offspring genetic effects in genetic association studies. 
Type Of Material Computer model/algorithm 
Year Produced 2018 
Provided To Others? Yes  
Impact N/A 
URL http://evansgroup.di.uq.edu.au/MGPC/
 
Title Mining the phenome 
Description Describes a statistical method whereby molecular phenotypes are tagged by allelic scores and then these scores datamined in existing genome-wide association studies to identify possible causal relationships. 
Type Of Material Data analysis technique 
Year Produced 2013 
Provided To Others? Yes  
Impact N/A 
URL http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3814299/
 
Title Parent of Origin Effects 
Description We have developed a method to estimate the proportion of phenotypic variance in a trait explained by parent of origin effects. 
Type Of Material Data analysis technique 
Year Produced 2017 
Provided To Others? Yes  
Impact N/A 
 
Title Partitioning maternal effects in UKBB 
Description We have created a new method to partition genetic effects into maternal and fetal components. 
Type Of Material Data analysis technique 
Year Produced 2018 
Provided To Others? Yes  
Impact We are currently using the method to identify genetic loci associated with birthweight and determine whether these loci act through the mother or child 
 
Title Supporting data for "PhenoSpD: an integrated toolkit for phenotypic correlation es-timation and multiple testing correction using GWAS summary statistics" 
Description Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restrict-ed access to much individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using only genome-wide association study (GWAS) summary results.
Here, we present an integrated R toolkit, PhenoSpD, to 1) use LD score regression to estimate phenotypic correlations using GWAS summary statistics; and 2) utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest 1) it is pos-sible to identify non-independence of phenotypes using samples with partial overlap, as overlap decreases the estimated phenotypic correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples; 2) in contrast to LD score regression, metaCCA will provide approximate genetic correlations rather than phenotypic correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 inde-pendent tests estimated using true phenotypic correlation. We further applied PhenoSpD to an estimated 5618 pair-wise phenotypic correlations among 107 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested the equivalent of 33.5 independent tests for theses metabolites.
PhenoSpD extends the use of summary level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.
R code and documentation for PhenoSpD V1.0.0 is available online https://github.com/MRCIEU/PhenoSpD. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Description EAGLE Consortium 
Organisation Helmholtz Association of German Research Centres
Department Helmholtz Zentrum Munchen
Country Germany 
Sector Academic/University 
PI Contribution Organizing and international meta-analysis of Eczema data from pediatric cohorts
Collaborator Contribution Provision of genetic data for analysis
Impact 22197932 23817569
Start Year 2009
 
Description EGG Consortium 
Organisation University of Oxford
Country United Kingdom 
Sector Academic/University 
PI Contribution Analysis of genome-wide association data
Collaborator Contribution Discovery of genetic loci influencing early growth phenotypes
Impact 20372150 23449627 22885924 22484627 22504419 21515849
Start Year 2008
 
Description GEFOS Consortium 
Organisation Erasmus MC
Department Department of Internal Medicine
Country Netherlands 
Sector Academic/University 
PI Contribution Provision of genome-wide association results for bone related traits. Coordination of meta-analyses.
Collaborator Contribution Provision of genome-wide association results for bone related traits. Coordination of meta-analyses.
Impact 24014423 23437003 23396134 23074152 22792071 22792070 22504420 21533022 21124946 20534768 19181680
Start Year 2011
 
Description International Mouse Knock Out Consortium 
Organisation Imperial College London
Department Imperial College Trust
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution We have provided a list of genome-wide significant loci from a genome-wide association study of bone mineral density to our mouse collaborators.
Collaborator Contribution Our collaborators have been busy generating mouse knockout models of candidate genes from the loci that we have given them.
Impact Multi-disciplinary. Human genetics. Mouse genetics.
Start Year 2016
 
Description International Mouse Knock Out Consortium 
Organisation The Garvan Institute for Medical Research
Country Australia 
Sector Hospitals 
PI Contribution We have provided a list of genome-wide significant loci from a genome-wide association study of bone mineral density to our mouse collaborators.
Collaborator Contribution Our collaborators have been busy generating mouse knockout models of candidate genes from the loci that we have given them.
Impact Multi-disciplinary. Human genetics. Mouse genetics.
Start Year 2016
 
Description International Mouse Knock Out Consortium 
Organisation The Wellcome Trust Sanger Institute
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution We have provided a list of genome-wide significant loci from a genome-wide association study of bone mineral density to our mouse collaborators.
Collaborator Contribution Our collaborators have been busy generating mouse knockout models of candidate genes from the loci that we have given them.
Impact Multi-disciplinary. Human genetics. Mouse genetics.
Start Year 2016
 
Description International Mouse Knock Out Consortium 
Organisation University of Rochester
Country United States 
Sector Academic/University 
PI Contribution We have provided a list of genome-wide significant loci from a genome-wide association study of bone mineral density to our mouse collaborators.
Collaborator Contribution Our collaborators have been busy generating mouse knockout models of candidate genes from the loci that we have given them.
Impact Multi-disciplinary. Human genetics. Mouse genetics.
Start Year 2016
 
Title IMPISH 
Description IMPISH allows uses to impute parental genotypes given genotype data on sibling or half sibling pairs across the genome. Users may then perform genome-wide association testing for maternal, paternal and/or offspring genetic effects. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact N/A 
URL http://evansgroup.di.uq.edu.au/software.html
 
Title Maternal GCTA 
Description Allows the user to partition the phenotypic variance into components due to the maternal and offspring genomes given genotyped mother-offspring pairs. 
Type Of Technology Software 
Year Produced 2019 
Open Source License? Yes  
Impact N/A 
URL http://evansgroup.di.uq.edu.au/software.html
 
Title Power calculators 
Description Allows the user to calculate the power to detect maternal, paternal and offspring genetic effects for a range of different study designs including for when parental genotypes are imputed 
Type Of Technology Webtool/Application 
Year Produced 2019 
Impact N/A 
URL http://evansgroup.di.uq.edu.au/power-calculators.html