Each of our cells carries two copies of our genome, the 3bn letters of DNA that serves as their instruction manual. The costs of sequencing (reading) a human genome have decreased by more than six orders of magnitude over the last 10-15 years. Globally, perhaps 100,000 whole genomes have been sequenced, with a clear short-term path to several million. In 10-15 years a billion human genomes will have been sequenced, with many of those sequences linked to extensive information about the individuals, from their medical records and wearable devices. The availability of extensive genetic information linked to information about health outcomes and other traits on very large numbers of individuals presents an extraordinary opportunity. Combining genomic information with biological and health measurements on individuals will improve our ability to assess individual health risks, predict outcomes, and personalise medical treatment. But crucially, and perhaps uniquely, genetics also offers the possibility of unravelling causality amongst otherwise highly correlated features. The resulting much deeper understanding of human biology will have a big impact on drug discovery and healthcare delivery. DNA sequence data from different individuals has a complex correlation structure due to our shared evolutionary history. Inference methods which model these correlations have been very successful to date, but the explosion in the scale and nature of available data will require novel approaches. The talk will illustrate the opportunities and challenges in applying ML and other inference tools to genomic data, by walking through specific examples. No previous knowledge of genetics will be necessary.