Volume 51 Issue 7, July 2019

Fhb1 is the most effective and most widely deployed source of durable resistance against Fusarium graminearum, a devastating toxin-producing fungal pathogen affecting wheat. Two new studies identify Fhb1 as an atypical disease resistance gene; this discovery is expected to fuel discussion on the molecular nature of this important disease-resistance locus.

Genetics has played a key role in understanding the relationship between the DNA sequence encoding a protein and the protein’s three-dimensional structure. Two new studies present similar analytical approaches to predict three-dimensional structure on the basis of genetic interaction data.

Although human genetics can help identify new drug targets, the best way to prioritize genes as therapeutic targets is uncertain. A new study describes a framework to prioritize potential targets by integrating genome-wide association data with genomic features, disease ontologies and network connectivity.

A Perspective on the future of agricultural genebank collections discusses how the use of molecular passport data can help facilitate genomic selection and accelerate crop breeding.

A genetics-led translational approach integrating functional genomic predictors, knowledge of network connectivity and immune ontologies defines the drug target prioritization landscape for 30 immune traits at the gene and pathway level.

Analysis of whole-exome sequencing data from 2,343 individuals with autism spectrum disorder compared to 5,852 unaffected individuals demonstrates an excess of biallelic, autosomal mutations for both loss-of-function and damaging missense variants.

Genetic studies using map-based cloning, gene editing, RNA interference, haplotyping and association analyses identify a deletion in TaHRC as a key determinant of Fhb1-mediated resistance to Fusarium head blight in wheat.

Genetic mapping and functional studies show that mutation of a histidine-rich calcium-binding-protein gene at the Fhb1 locus confers resistance to Fusarium head blight in wheat. Notably, transgenic plants expressing the R allele show enhanced resistance to infection.

Analysis of evolutionary dynamics of colorectal cancers and paired distant brain or liver metastases provides evidence that early disseminated cancer cells seed metastases before the carcinoma is clinically detectable.

Multi-region sequencing of 35 primary uveal melanomas and their matched metastases yields new insights into the genetics and evolution of these tumors and provides potential biomarkers for progression and therapy.

Whole-genome sequencing and association analysis of 270 Epstein–Barr virus (EBV) isolates from China identify two non-synonymous EBV variants within BALF2 that are strongly associated with the risk of nasopharyngeal carcinoma.

Promoter capture Hi-C maps in human pancreatic islets identify more than 1,300 three-dimensional regulatory hubs, linking diabetes-associated enhancers to their target genes. Genetic variation in hubs impacts insulin secretion heritability.

Comprehensive CRISPR mutagenesis targeting all members of the NuRD complex identifies a specific subcomplex required for fetal globin silencing and informs a rational targeting strategy for elevating globin levels while avoiding cytotoxicity.

Application of SuRE reporter technology to survey the effect of 5.9 million SNPs in the human genome on enhancer and promoter activity identifies over 30,000 SNPs that alter the activity of putative regulatory elements.

The authors present a method for determining 3D protein structures using high-throughput mutation experiments. Pairs of residues with the largest positive epistasis are sufficient to determine the 3D fold.

This method predicts three-dimensional protein structures based on the activity of mutant variants. The approach relies on quantifying genetic interactions between mutations to infer direct contacts between residues.

In the version of this article initially published, there was a mistake in the calculation of the nucleotide mutation rate per site per generation: 1 × 10⁻⁹ mutations per site per generation was used, whereas 9.5 × 10⁻⁹ was correct. This error affects the interpretation of population-size changes over time and their possible correspondence with known geological events, as shown in the original Fig. 4 and supporting discussion in the text, as well as details in the Supplementary Note. Neither the data themselves nor any other results are affected. Figure 4 has been revised accordingly. Images of the original and corrected figure panels are shown in the correction notice.