Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data

The discovery of novel recessive genetic disorders in dairy cattle : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at AL Rae Centre of Genetics and Breeding, Massey University, Palmerston North, New Zealand

The selection of desirable characteristics in livestock has resulted in the transmission of advantageous genetic variants for generations. The advent of artificial insemination has accelerated the propagation of these advantageous genetic variants and led to tremendous advances in animal productivity. However, this intensive selection has led to the rapid uptake of deleterious alleles as well. Recently, a recessive mutation in the GALNT2 gene was identified to dramatically impair growth and production traits in dairy cattle causing small calf syndrome. The research presented here seeks to further investigate the presence and impact of recessive mutations in dairy cattle. A primary aim of genetics is to identify causal variants and understand how they act to manipulate a phenotype. As datasets have expanded, larger analyses are now possible and statistical methods to discover causal mutations have become commonplace. One such method, the genome-wide association study (GWAS), presents considerable exploratory utility in identifying quantitative trait loci (QTL) and causal mutations. GWAS' have predominantly focused on identifying additive genetic effects assuming that each allele at a locus acts independently of the other, whereas non-additive effects including dominant, recessive, and epistatic effects have been neglected. Here, we developed a single-locus non-additive GWAS model intended for the detection of dominant and recessive genetic mechanisms. We applied our non-additive GWAS model to growth, developmental, and lactation phenotypes in dairy cattle. We identified several candidate causal mutations that are associated with moderate to large deleterious recessive disorders of animal welfare and production. These mutations included premature-stop (MUS81, ITGAL, LRCH4, RBM34), splice disrupting (FGD4, GALNT2), and missense (PLCD4, MTRF1, DPF2, DOCK8, SLC25A4, KIAA0556, IL4R) variants, and these occur at surprisingly high frequencies in cattle. We further investigated these candidates for anatomical, molecular, and metabolic phenotypes to understand how these disorders might manifest. In some cases, these mutations were analogous to disorder-causing mutations in other species, these included: Coffin-Siris syndrome (DPF2); Charcot Marie Tooth disease (FGD4); a congenital disorder of glycosylation (GALNT2); hyper Immunoglobulin-E syndrome (DOCK8); Joubert syndrome (KIAA0556); and mitochondrial disease (SLC25A4). These discoveries demonstrate that deleterious recessive mutations exist in dairy cattle at remarkably high frequencies and we are able to detect these disorders through modern genotyping and phenotyping capabilities. These are important findings that can be used to improve the health and productivity of dairy cattle in New Zealand and internationally

A stew of mixed ingredients: Observational omics in the post-GWAS era

The past 20 years have seen extensive profiling of the DNA. Collectively, scientists all across the world have identified many places in the DNA, known as loci, that impact human traits such as disease state or immune function. However, interpreting the results from these studies, known as genome wide association studies (GWAS), has been challenging. This thesis studies several approaches for interpreting GWAS results, with a specific focus on our immune system given its important role in preventing and causing disease. This is done through the use of so called ‘omics’ technologies, that can study the role of thousands of genes, proteins and genetic variants at the same time. By doing this, maps can be constructed of which genes and proteins interact to impact human traits. The ultimate goal of this research is to provide a better understanding of the cascade between the DNA and human traits. The hope is that building a specific understanding of how the variation in the DNA leads to the development of human traits, such as disease, will ultimately aid the development of drugs for these diseases

Biologically-informed interpretable deep learning techniques for BMI prediction and gene interaction detection

The analysis of genetic point mutations at the population level can offer insights into the genetic basis of human traits, which in turn could potentially lead to new diagnostic and treatment options for heritable diseases. However, existing genetic data analysis methods tend to rely on simplifying assumptions that ignore nonlinear interactions between variants. The ability to model and describe nonlinear genetic interactions could lead to both improved trait prediction and enhanced understanding of the underlying biology. Deep Learning models offer the possibility of automatically learning complex nonlinear genetic architectures, but it is currently unclear how best to optimise them for genetic data. It is also essential that any models be able to “explain” what they have learned in order for them to be used for genetic discovery or clinical applications, which can be difficult due to the black-box nature of DL predictors. This thesis addresses a number of methodological gaps in applying explainable DL models end-to-end on variant-level genetic data. We propose novel methods for encoding genetic data for deep learning applications and show that feature encodings designed specifically for genetic variants offer the possibility of improved model efficiency and performance. We then benchmark a variety of models for the prediction of Body Mass Index using data from the UK Biobank, yielding insights into DL performance in this domain. We then propose a series of novel DL model interpretation methods with features optimised for biological insights. We first show how these can be used to validate that the network has automatically replicated existing knowledge, and then illustrate their ability to detect complex nonlinear genetic interactions that influence BMI in our cohort. Overall, we show that DL model training and interpretation procedures that have been optimised for genetic data can be used to yield new insights into disease aetiology

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research