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This Week in Nature: May 23, 2019

In this week's Nature, an international team presents the largest exome sequence analysis of individuals with type 2 diabetes to date, uncovering new rare disease-related variants. The investigators performed the analysis on more than  20,000 patients and roughly 24,000 controls across five ancestry groups. They found gene-level associations of rare variants in four genes at exome-wide significance, but note that the strongest type 2 diabetes gene-level signals for rare variants explain at most 25 percent of the heritability of the strongest common single-variant signals. As such, the team writes, the gene-level effect sizes of the rare variants observed in established disease drug targets will require 75,000 to 185,000 sequenced cases to achieve exome-wide significance. GenomeWeb has more on this, here.

Also in Nature, researchers from industry and academia report the genomic characterization of metastatic breast cancers, revealing a genetic complexity that points to the need for the earlier use of precision treatments. The scientists examined the landscape of somatic alterations in 617 metastatic breast cancers, identifying nine driver genes that were more frequently mutated in metastatic breast cancers that expressed hormone receptors, but lacked high levels of HER2 when compared to early breast cancers. Additional findings include 18 amplicons that were more frequently observed in hormone receptor positive/HER2 negative metastatic breast cancers, and an increased mutational burden and clonal diversity in metastatic versus early-stage disease. "Thus, the genomic landscape of metastatic breast cancer is enriched in clinically relevant genomic alterations and is more complex than that of early breast cancer," the researchers write. "The identification of genomic alterations associated with poor outcome will allow earlier and better selection of patients who require the use of treatments that are still in clinical trials." GenomeWeb covers this one, too, here.

Finally, in Nature Genetics, a group of Dutch and French scientists describe the use of genomic prediction to evaluate maize yield across environmental conditions in Europe. In the approach, yield is determined via grain weight and number, while gene-environment interaction in these components is modeled as genotypic sensitivity to environmental drivers. Environments, meantime, are characterized using genotype-specific indices computed from sensor network data in various fields and the progression of phenology calibrated for each genotype on a phenotyping platform. "Our approach offers good prospects for breeding programs aiming at the development of new hybrids adapted to the diversity of current and future climatic conditions," the researchers say.