9 research outputs found
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
A crucial task for a randomized controlled trial (RCT) is to specify a
statistical method that can yield an efficient estimator and powerful test for
the treatment effect. A novel and effective strategy to obtain efficient and
powerful treatment effect inferences is to incorporate predictions from
generative artificial intelligence (AI) algorithms into covariate adjustment
for the regression analysis of a RCT. Training a generative AI algorithm on
historical control data enables one to construct a digital twin generator (DTG)
for RCT participants, which utilizes a participant's baseline covariates to
generate a probability distribution for their potential control outcome.
Summaries of the probability distribution from the DTG are highly predictive of
the trial outcome, and adjusting for these features via regression can thus
improve the quality of treatment effect inferences, while satisfying regulatory
guidelines on statistical analyses, for a RCT. However, a critical assumption
in this strategy is homoskedasticity, or constant variance of the outcome
conditional on the covariates. In the case of heteroskedasticity, existing
covariate adjustment methods yield inefficient estimators and underpowered
tests. We propose to address heteroskedasticity via a weighted prognostic
covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the
mean and variance of the regression model using information obtained from the
DTG. We prove that our method yields unbiased treatment effect estimators, and
demonstrate via comprehensive simulation studies and case studies from
Alzheimer's disease that it can reduce the variance of the treatment effect
estimator, maintain the Type I error rate, and increase the power of the test
for the treatment effect from 80% to 85%~90% when the variances from the DTG
can explain 5%~10% of the variation in the RCT participants' outcomes.Comment: 49 pages, 6 figures, 12 table
Molecular Basis for Increased Risk for Late-onset Alzheimer Disease Due to the Naturally Occurring L28P Mutation in Apolipoprotein E4
Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease
Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant pBonferroni-corrected=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (pmeta-Bonferroni-corrected=9.02*10-3) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (β=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD
Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease
Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant pBonferroni-corrected=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (pmeta-Bonferroni-corrected=9.02*10-3) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (β=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease
Genetic annotation of gain-of-function screens using RNA interference and in situ hybridization of candidate genes in the Drosophila wing
Gain-of-function screens in Drosophila are an effective method with which to identify genes that affect the development of particular structures or cell types. It has been found that a fraction of 2-10% of the genes tested, depending on the particularities of the screen, results in a discernible phenotype when overexpressed. However, it is not clear to what extent a gain-of-function phenotype generated by overexpression is informative about the normal function of the gene. Thus, very few reports attempt to correlate the loss- and overexpression phenotype for collections of genes identified in gain-of-function screens. In this work we use RNA interference and in situ hybridization to annotate a collection of 123 P-GS insertions that in combination with different Gal4 drivers affect the size and/or patterning of the wing. We identify the gene causing the overexpression phenotype by expressing, in a background of overexpression, RNA interference for the genes affected by each P-GS insertion. Then, we compare the loss and gainof-function phenotypes obtained for each gene and relate them to its expression pattern in the wing disc. We find that 52% of genes identified by their overexpression phenotype are required during normal development. However, only in 9% of the cases analyzed was there some complementarity between the gain- and loss-of-function phenotype, suggesting that, in general, the overexpression phenotypes would not be indicative of the normal requirements of the gene. © 2012 by the Genetics Society of America.Peer Reviewe