Statistical methods for testing genetic pleiotropy

How are statistics used in genetics?
Statistical geneticists at SPH develop statistical methods for understanding the genetic basis of human diseases and traits.
These methods involve large-scale data sets from candidate-gene, genome-wide and resequencing studies, using both unrelated and related individuals..
How do you test for pleiotropy?
Using the proposed GLIDE method, investigators can determine whether the set of genetic variants being evaluated for IVs show any evidence of global pleiotropy and, if so, which variants exhibit evidence of pleiotropy and therefore should be removed in a sensitivity analysis..
Just as a single characteristic can be influenced by multiple genes, a single gene can influence multiple characteristics – a phenomenon known as pleiotropy.
Pleiotropic genes are those in which variation can cause observable change in two or more body systems that may often appear unrelated at first.
Modern biostatistical methods help to better understand the genetic structure of diseases, disease progression and pathways, and to study the impact of environmental exposures on chronic diseases.

Statistical methods to evaluate pleiotropy have been developed from different angles, ranging from comparison of univariate marginal associations of a genetic variant with multiple traits, to multivariate analyses with simultaneous regression of all traits on a genetic variant, to reversed regression of a genetic

Can pleiotropy be determined by a null hypothesis?

Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait.

Do statistical tests test pleiotropy?

Despite the great deal of attention given to pleiotropy, most statistical tests do not formally test pleiotropy.
Rather, they test the null hypothesis that no trait is associated with a variant; rejecting this null could be due to just one associated trait, not a situation of pleiotropy.

How can we detect pleiotropic loci between two traits?

In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits.

How do we evaluate pleiotropy?

Statistical methods to evaluate pleiotropy have been developed from different angles, ranging from comparison of univariate marginal associations of a genetic variant with multiple traits, to multivariate analyses with simultaneous regression of all traits on a genetic variant, to reversed regression of a genetic variant on all traits.

In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap.
A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical.
The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis.
Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.

Statistical methods for testing genetic pleiotropy

How are statistics used in genetics?

How do you test for pleiotropy?

Can pleiotropy be determined by a null hypothesis?

Do statistical tests test pleiotropy?

How can we detect pleiotropic loci between two traits?

How do we evaluate pleiotropy?