Genetic association analysis in family samples with missing data

Lead Research Organisation: London Sch of Hygiene and Trop Medicine
Department Name: Epidemiology and Population Health

Abstract

Collections of families are useful for genetic studies because their members share a common background, both genetic and environmental, meaning that differences between them are likely to be due to the genes being studied. Family studies can also determine whether a mother?s genes affect the health of her child through inheritance by the child or presence in the mother. For practical reasons it is often difficult to collect data from all the members of a family, so statistical methods can be used to fill in the missing data. In recent years development of such methods has fallen behind similar methods designed for unrelated subjects. This project will extend our previous work on missing data in family studies, developing several new methods that will be applied to a number of collaborative studies.

Technical Summary

Family-based association analysis uses samples of cases and their relatives to draw inferences on disease association: most commonly, cases and their parents, or sib pairs (including twins) are used. Their advantages include matching of subjects for genetic and environmental background, and the ability to distinguish the effects of maternally inherited genes from those paternally inherited. Major collections of family data are in progress and require rigorous statistical methods for their epidemiology. This project will extend our previous work on likelihood based analysis of samples with missing data. We will develop and implement methods for distinguishing the effects of genes transmitted by the mother from those carried by her. We will develop proper analysis of quantitative traits when subjects have been sampled on the basis of their trait values. We will devise appropriate methods for analysis of copy number variants in family data. In each of these applications we will allow for missing genotype data, in particular missing parents in nuclear families, in a statistically optimal fashion. These methods will be applied in a number of collaborative studies. We will also translate our methods into a general epidemiological framework to allow more widespead application outside of genetics.

Publications

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Cross-Disorder Group Of The Psychiatric Genomics Consortium (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. in Lancet (London, England)

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Cross-Disorder Group Of The Psychiatric Genomics Consortium (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. in Nature genetics

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Dudbridge F (2012) Estimating causal effects of genetic risk variants for breast cancer using marker data from bilateral and familial cases. in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology

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Dudbridge F (2014) Gene-environment dependence creates spurious gene-environment interaction. in American journal of human genetics

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Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. in PLoS genetics

 
Title Sibship association methods 
Description Methods and software for association analysis in sibships 
Type Of Material Model of mechanisms or symptoms - human 
Year Produced 2008 
Provided To Others? Yes  
Impact Publication 
URL https://sites.google.com/site/fdudbridge/software
 
Description PGC 
Organisation Psychiatric GWAS Consortium (PGC)
Country Global 
Sector Academic/University 
PI Contribution Statistical advice
Collaborator Contribution Data analysis
Impact Publications
Start Year 2009
 
Description SANCD 
Organisation Public Health Foundation of India
Department South Asia Network for Chronic Disease in India (SANCD)
Country India 
Sector Public 
PI Contribution Statistical Analysis
Collaborator Contribution Motivating applications for developing statistical methods
Impact Publications
Start Year 2009
 
Description UCLEB 
Organisation University College London
Department Research Department of Epidemiology and Public Health
Country United Kingdom 
Sector Academic/University 
PI Contribution Statistical analysis and advice
Collaborator Contribution Data collection, project management
Impact Fine mapping of genetic loci for cardiovascular outcomes
Start Year 2011
 
Description UWA 
Organisation University of Western Australia
Country Australia 
Sector Academic/University 
PI Contribution Expertise in statistical genetics
Collaborator Contribution Genetic studies of osteoporosis and thyroid disease
Impact Several publications
 
Title AVENGEME 
Description Estimation of genetic model parameters from polygenic association statistics 
Type Of Technology Software 
Year Produced 2015 
Open Source License? Yes  
Impact Used as a research tool by a number of independent groups. 
URL http://sites.google.com/site/fdudbridge/software
 
Title UNPHASED 
Description Genetic association analysis in nuclear families and unrelated subjects, allowing for missing genotype data and uncertain haplotypes 
Type Of Technology Software 
Year Produced 2006 
Open Source License? Yes  
Impact Over 1000 published applications by users 
URL https://sites.google.com/site/fdudbridge/software