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Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limiting assumption of LMMs is that the residuals are Gaussian distributed, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and losses in power, and hence it is common practice to pre-process the phenotypic values to make them Gaussian, for instance by applying logarithmic or other non-linear transformations. Unfortunately, different phenotypes require different specific transformations, and choosing a good transformation is in general challenging and subjective. Here, we present an extension of the LMM that estimates an optimal transformation from the observed data. In extensive simulations and applications to real data from human, mouse and yeast we show that using such optimal transformations lead to increased power in genome-wide association studies and higher accuracy in heritability estimates and phenotype predictions.
Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture int
The phenotypic consequences of individual mutations are modulated by the wild type genetic background in which they occur.Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist,
Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets,
RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced tran
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the