Rare…but be aware!
Related Articles
Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels
The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent rare noncoding single-variant associations with circulating protein levels. Unlike protein-coding variation, rare noncoding genetic variation was almost as likely to increase or decrease protein levels. Rare noncoding aggregate testing identified 357 conditionally independent associated regions. Of these, 74 (21%) were not detectable by single-variant testing alone. Our findings have important implications for the identification, and role, of rare noncoding genetic variation associated with common human phenotypes, including the importance of testing aggregates of noncoding variants.
International Precision Child Health Partnership (IPCHiP): an initiative to accelerate discovery and improve outcomes in rare pediatric disease
Advances in genomic technologies have revolutionized the diagnosis of rare genetic diseases, leading to the emergence of precision therapies. However, there remains significant effort ahead to ensure the promise of precision medicine translates to improved outcomes. Here, we discuss the challenges in advancing precision child health and highlight how international collaborations such as the International Precision Child Health Partnership, which embed research into clinical care, can maximize benefits for children globally.
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
Excess of rare noncoding variants in several type 2 diabetes candidate genes among Asian Indian families
Type 2 diabetes (T2D) etiology is highly complex due to its multiple roots of origin. Polygenic risk scores (PRS) based on genome-wide association studies (GWAS) can partially explain T2D risk. Asian Indian people have up to six times higher risk of developing T2D than European people, and underlying causes of this disparity are unknown.
Mineral origin of tremolite jade artifacts from the Guojiamiao Cemetery, Eastern Zhou Dynasty, Hubei, China: based on petrology, spectroscopy, and geochemistry
The origin of raw materials is a key area of study in jade archaeology, with significant implications for understanding the interactions and exchanges between ancient cultures. The Guojiamiao Cemetery, located in Zaoyang City, Hubei Province, China, has been the subject of two protective excavations, one in 2004 and another in 2014. These excavations revealed a large aristocratic cemetery from the Zeng State, dating from the late Western Zhou Dynasty to the early Spring and Autumn periods. The jade artifacts found at the site are diverse in type and exquisite in craftsmanship, serving as important burial items. This study applied gemological, spectroscopic, and geochemical methods to analyze 30 jade artifacts in detail. We examined the types of minerals, shape characteristics, and chemical composition of the materials. In particular, we focused on determining the origin of the tremolite jade artifacts found at the cemetery. Using a classification method based on combinations of trace and rare earth elements associated with different regions, we were able to identify the sources of the jade. Our findings suggest that the raw materials for the Guojiamiao Cemetery jade artifacts unearthed were transported over long distances, from northwestern China to the middle reaches of the Yangtze River. This research is important for understanding the jade use system of the Zeng State from the early Western Zhou to the mid-Warring States periods. It also provides insights into the sources of jade materials in different historical periods of the Zeng State and its connections with the Chu State. Ultimately, this study contributes to a broader understanding of the evolution of civilization in the middle Yangtze River region.
Responses