Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution—giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Inverse-variance weighted two-sample Mendelian Randomization (IVW-MR) is the most widely used approach that uses genome-wide association studies summary statistics to infer the existence and strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to: (i) the overlap between the exposure and outcome samples; (ii) the use of weak instruments and winner's curse. We developed a method that aims at tackling all these biases together. Assuming spike-and-slab genomic architecture and leveraging LD-score regression and other techniques, we could analytically derive and reliably estimate the bias of IVW-MR using association summary statistics only. This allowed us to apply a bias correction to IVW-MR estimates, which we tested using simulated data for a wide range of realistic scenarios. In all the explored scenarios, our correction reduced the bias, in some situations by as much as 30 folds. When applied to real data on obesity-related exposures, we observed significant differences between IVW-based and corrected effects, both for non-overlapping and fully overlapping samples. While most studies are extremely careful to avoid any sample overlap when performing two-sample MR analysis, we have demonstrated that the incurred bias is much less substantial than the one due to weak instruments or winner's curse, which are often ignored.