Major methodological advances in Phase 1, including improved methods for detecting and genotyping variants40, statistical and machine-learning methods for evaluating the quality of candidate variant calls, modelling of genotype likelihoods and performing statistical haplotype integration41, have generated a high-quality resource. However, regions of low sequence complexity, satellite regions, large repeats and many large-scale structural variants, including copy-number polymorphisms, segmental duplications and inversions (which constitute most of the “inaccessible genome”), continue to present a major challenge for short-read technologies. Some issues are likely to be improved by methodological developments such as better modelling of read-level errors, integrating de novo assembly42,43 and combining multiple sources of information to aid genotyping of structurally-diverse regions40,44. Importantly, even subtle differences in data type, data processing or algorithms may lead to systematic differences in false-positive and false negative error modes between samples. Such differences complicate efforts to compare genotypes between sequencing studies. Moreover, analyses that naively combine variant calls and genotypes across heterogeneous data sets are vulnerable to artifact. Analyses across multiple data sets must therefore either process them in standard ways or use meta-analysis approaches that combine association statistics (but not raw data) across studies.
