Genetic variation in the genome of an individual plays a key role in susceptibility to many human diseases. Analysis of the genetic variants harboured by individuals presenting with disease phenotypes is crucial for unravelling the genetic landscape of human disease. The methods that are now available for the characterisation of genetic variants, including single nucleotide polymorphism (SNP) microarrays and next generation sequencing, make it possible to explore all genetic variants harboured within an individual with a specific disease phenotype, allowing for tailoring of treatments. This thesis focuses on the genetic dissection of early-onset breast cancer, syndromic and nonsyndromic forms of cleft lip with or without palate (CLP), and an oculopharyngeal muscular dystrophy-like (OPMD-like) phenotype through the analysis of SNP and exome data. Novel analysis approaches were used to explore the breast cancer genome-wide SNP data; a variety of machine learning algorithms were used to identify potential interactions and pathways influencing disease that cannot be uncovered using conventional analysis techniques. Such approaches are necessary because in many cases disease aetiology is likely to be complex with many genetic factors and interactions influencing disease susceptibility. Further characterisation of the genetic landscape of early-onset breast cancer, as well as the genetics of CLP and OPMD-like disease phenotypes, was possible through the use of whole exome sequencing technology. Exome sequencing identified many potentially important variants in the breast cancer samples and nonsyndromic CLP cases. Particular success was observed in the disease that were Mendelian in nature, namely syndromic CLP and the OPMD-like family; in all cases the likely causative mutation was successfully identified. Genetic studies of human disease using sequencing technologies and novel methods to analyse data are vital as personalised medicine becomes a real possibility in healthcare.