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Register a new userRecommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: experiences from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program
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The ways in which race, ethnicity, and ancestry are used and reported in human genomics research has wide-ranging implications for how research is translated into clinical care, incorporated into public understanding, and implemented in public policy. Genetics researchers play an essential role in proactively dismantling genetic conceptions of race and in recognizing the social and structural factors that drive health disparities. Here, we offer commentary and concrete recommendations on the use and reporting of race, ethnicity, and ancestry across the arc of genetic research, including terminology, data harmonization, analysis, and reporting. While informed by our experiences as researchers in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, the recommendations are broadly applicable to basic and translational genomic research in diverse populations. To fully realize the benefit of diversifying genetics research beyond primarily European ancestry populations, we as genetics researchers need to make structural changes to the research process and within the research community. Considerable collaborative effort and ongoing reflection will be required to root out elements of racism from the field and generate scientific knowledge that yields broad and equitable benefit.
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Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present academic software for which essential materials are or become unavailable, such as source code and documentation. Publications that lack such information compromise the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit any subsequent work produced with the tool. We provide eight recommendations across four different domains to improve reproducibility, transparency, and rigor in computational biology - precisely on the main values which should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in biomedicine and life science research.
Quantum Biology is emerging as a new field at the intersection between fundamental physics and biology, promising novel insights into the nature and origin of biological order. We discuss several elements of QBCL (Quantum Biology at Cellular Level), a research program designed to extend the reach of quantum concepts to higher than molecular levels of biological organization. Key words. decoherence, macroscopic superpositions, basis-dependence, formal superposition, non-classical correlations, Basis-Dependent Selection (BDS), synthetic biology, evolvability mechanism loophole.
The recent genealogical history of human populations is a complex mosaic formed by individual migration, large-scale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material. We make use of genomic data for 2,257 Europeans (the POPRES dataset) to conduct one of the first surveys of recent genealogical ancestry over the past three thousand years at a continental scale. We detected 1.9 million shared genomic segments, and used the lengths of these to infer the distribution of shared ancestors across time and geography. We find that a pair of modern Europeans living in neighboring populations share around 10-50 genetic common ancestors from the last 1500 years, and upwards of 500 genetic ancestors from the previous 1000 years. These numbers drop off exponentially with geographic distance, but since genetic ancestry is rare, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1000 years. There is substantial regional variation in the number of shared genetic ancestors: especially high numbers of common ancestors between many eastern populations likely date to the Slavic and/or Hunnic expansions, while much lower levels of common ancestry in the Italian and Iberian peninsulas may indicate weaker demographic effects of Germanic expansions into these areas and/or more stably structured populations. Recent shared ancestry in modern Europeans is ubiquitous, and clearly shows the impact of both small-scale migration and large historical events. Population genomic datasets have considerable power to uncover recent demographic history, and will allow a much fuller picture of the close genealogical kinship of individuals across the world.
There are no two identical leaves in the world, so how to find effective markers or features to distinguish them is an important issue. Function transformation, such as f(x,y) and f(x,y,z), can transform two, three, or multiple input/observation variables (in biology, it generally refers to the observed/measured value of biomarkers, biological characteristics, or other indicators) into a new output variable (new characteristics or indicators). This provided us a chance to re-cognize objective things or relationships beyond the original measurements. For example, Body Mass Index, which transform weight and high into a new indicator BMI=x/y^2 (where x is weight and y is high), is commonly used in to gauge obesity. Here, we proposed a new system, Funomics (Function Transformation Omics), for understanding the world in a different perspective. Funome can be understood as a set of math functions consist of basic elementary functions (such as power functions and exponential functions) and basic mathematical operations (such as addition, subtraction). By scanning the whole Funome, researchers can identify some special functions (called handsome functions) which can generate the novel important output variable (characteristics or indicators). We also start the Funome project to develop novel methods, function library and analysis software for Funome studies. The Funome project will accelerate the discovery of new useful indicators or characteristics, will improve the utilization efficiency of directly measured data, and will enhance our ability to understand the world. The analysis tools and data resources about the Funome project can be found gradually at http://www.funome.com.
Recent statistical and computational analyses have shown that a genealogical most recent common ancestor (MRCA) may have lived in the recent past. However, coalescent-based approaches show that genetic most recent common ancestors for a given non-recombining locus are typically much more ancient. It is not immediately clear how these two perspectives interact. This paper investigates relationships between the number of descendant alleles of an ancestor allele and the number of genealogical descendants of the individual who possessed that allele for a simple diploid genetic model extending the genealogical model of Joseph Chang.
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