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Approved research

Genetic Association to Phenotypic Variance and its Role in Human Health

Principal Investigator: Dr Brent Richards
Approved Research ID: 27449
Approval date: April 1st 2017

Lay summary

Disease may arise when the phenotype of an individual surpasses a given threshold, such as fasting glucose level in type 2 diabetes. Thus, changes in DNA sequence associated with greater fluctuation, or variance, in phenotype may result in a greater number of individuals at increased disease risk, despite possessing the same average phenotype as individuals carrying no change in DNA sequence. The aim of this research program is to identify changes in DNA sequence associated to phenotypic variance across multiple traits. It will determine the extent to which phenotypic variance due to genetic variation contributes to overall disease susceptibility. A DNA change?s effect on phenotypic variance represents an important part of genetics? contribution to differences in phenotype between individuals. Currently, only a few studies have found genetic variants associated to phenotypic variance of human phenotypes[1], [2]. Therefore, a large part of the genetic landscape that contributes to our understanding of human health, including contributions to disease susceptibility, remains unexplored. Identifying genetic variation associated to phenotypic variance across multiple traits would better inform public health policy, such as limiting certain biological or environmental exposures that push the phenotype of at-risk individuals over the disease-risk threshold. Utilizing UK Biobank, we will associate differences in DNA sequence at specific genome positions to the extent that a trait is spread about the mean (ie, variance). We will remove bias due to genetically related individuals and the correlation between traits. Due to increased error inherent in measuring trait variance, and expected small effect of genetic changes on phenotype, only a comprehensive analysis of multiple traits using a very large number of individuals will make this analysis possible. Results will determine if there exists DNA changes that associate to phenotypic variance across multiple traits. We request the full cohort, ~500,000 participants (ETA Q2 2017) of which currently ~150,000 are available, to maximize statistical power and phenotype coverage.

Scope extension:

The current scope of our approved program is:

Disease may arise when the phenotype of an individual surpasses a given threshold, such as fasting glucose level in type 2 diabetes. Thus, changes in DNA sequence associated with greater fluctuation, or variance, in phenotype may result in a greater number of individuals at increased disease risk, despite possessing the same average phenotype as individuals carrying no change in DNA sequence. The aim of this research program is to identify changes in DNA sequence associated to phenotypic variance across multiple traits and to understand if genotypic variation can predict phenotypic variation.  It will determine the extent to which phenotypic variance due to genetic variation contributes to overall disease susceptibility, and whether such variation can predict phenotypic variation.    

The new scope of the program aims to clarify our original intentions. As described above, we would like to understand how genetic variation influences phenotypic variation. We would like to clarify that we intend to understand the genetic determinants of variance in phenotypes and also differences in phenotypes between individuals. Specifically, we are generally interested in the role of genetic variation in producing phenotypic variation in the population for all medically-relevant outcomes.

Furthermore, we will leverage the identified genetic determinants as instruments to investigate the causal effects of various clinical, environmental, and lifestyle risk factors on specific outcomes or diseases. Specifically, we will adopt Mendelian randomization and other approaches to overcome limitations of observational studies and provide robust evidence of causality. This will allow for a better understanding of whether certain risk factors directly contribute to the development or progression of diseases, providing valuable insights for public health interventions, risk prediction, and personalized medicine.