Timevarying Group Sparse Additive Model for GWAS
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 most existing GWAS methodologies are still limited to the use of static phenotypes measured at a single point.
 the paper proposes a new method for association mapping that considers dynamic phenotypes measured at a sequence of time points.

TimeVarying Group Sparse Additive Models (TVGroupSpAM) for highdimensional, functional regression
 fGWAS: performing GWAS of dynamic traits, construct a separate model to estimate the smooth, timevarying influence of each SNP on the phenotype.
 once the mean effects have been estimated for each genotype at each time point, a hypothesis test is performed to determine whether the SNP has any additive or dominant effect on the trait.
 the principal drawback is that it is inappropriate for modeling complex traits that arise from interactions between genetic effect at different loci.
 the paper introduce a new penalized multivariate regression approach for GWAS of dynamic quantitative traits, in which the phenotype is modeled as a sum of nonparametric, timevarying SNP effects