Accurate population-scale variant calling in turn requires joint analysis of all constituent raw data, where different batches have been aligned and processed systematically using compatible methods. Our ability as a field to harness these collective data to their full analytic potential depends on the availability of high quality variant calls from large populations of individuals. Systematic aggregation and co-analysis of these (and other) genomic datasets will enable increasingly well-powered studies of human traits, population history and genome evolution, and will provide population-scale reference databases that expand upon the groundbreaking efforts of the 1000 Genomes Project 7, 8, Haplotype Reference Consortium 9, ExAC 10, and GnomAD 11. Indeed, at the time of writing, >150,000 human genomes have already been sequenced by three NIH programs: NHGRI Centers for Common Disease Genomics 5 (CCDG), NHLBI Trans-Omics for Precision Medicine (TOPMed), and NIMH Whole Genome Sequencing in Psychiatric Disorders 6 (WGSPD). These projects will generate hundreds of thousands of publicly available deep (>20×) WGS datasets from diverse human populations. Over the past few years, a wave of large-scale WGS-based human genetics studies have been launched by various institutes and funding programs worldwide 1, 2, 3, 4 aimed at elucidating the genetic basis of a variety of human traits. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines.
This approach is no longer tenable given the scale of current studies and data volumes. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years.
0 kommentar(er)
