Author
Henry T. Greely
Other affiliations: Pennsylvania State University, Washington and Lee University, Louisiana State University
Bio: Henry T. Greely is an academic researcher from Stanford University. The author has contributed to research in topics: Genetic testing & Neuroethics. The author has an hindex of 41, co-authored 166 publications receiving 7497 citations. Previous affiliations of Henry T. Greely include Pennsylvania State University & Washington and Lee University.
Papers published on a yearly basis
Papers
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Council on Education for Public Health1, Cancer Research UK2, French Institute of Health and Medical Research3, Peking Union Medical College4, Chinese Academy of Sciences5, University of Geneva6, National Cancer Research Institute7, Temple University8, University of Los Andes9, Florida International University10, Harbin Medical University11, Yale University12, Stanford University13, University of Turin14, Marshfield Clinic15
TL;DR: A resource of 1064 cultured lymphoblastoid cell lines from individuals in different world populations and corresponding milligram quantities of DNA is deposited at the Foundation Jean Dausset (CEPH) in Paris.
Abstract: A resource of 1064 cultured lymphoblastoid cell lines (LCLs) ([1][1]) from individuals in different world populations and corresponding milligram quantities of DNA is deposited at the Foundation Jean Dausset (CEPH) ([2][2]) in Paris. LCLs were collected from various laboratories by the Human Genome
1,002 citations
TL;DR: Society must respond to the growing demand for cognitive enhancement by rejecting the idea that 'enhancement' is a dirty word, argue Henry Greely and colleagues.
Abstract: Society must respond to the growing demand for cognitive enhancement. That response must start by rejecting the idea that 'enhancement' is a dirty word, argue Henry Greely and colleagues.
753 citations
TL;DR: Although challenges remain, the results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients.
686 citations
TL;DR: The meeting identified immediate steps to take toward ensuring that the application of genome engineering technology is performed safely and ethically, and identified those areas where action is essential to prepare for future developments.
Abstract: A framework for open discourse on the use of CRISPR-Cas9 technology to manipulate the human genome is urgently needed
527 citations
TL;DR: This work presents a novel and scalable approach called “ ‘spatially directed cell reprograming’” that can be applied to the rapidly changing environment and has real-time implications for clinical practice and scientific discovery.
221 citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: The meaning of the terms "method" and "method bias" are explored and whether method biases influence all measures equally are examined, and the evidence of the effects that method biases have on individual measures and on the covariation between different constructs is reviewed.
Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
8,719 citations
TL;DR: Viewing the microbiota from an ecological perspective could provide insight into how to promote health by targeting this microbial community in clinical treatments.
Abstract: Trillions of microbes inhabit the human intestine, forming a complex ecological community that influences normal physiology and susceptibility to disease through its collective metabolic activities and host interactions. Understanding the factors that underlie changes in the composition and function of the gut microbiota will aid in the design of therapies that target it. This goal is formidable. The gut microbiota is immensely diverse, varies between individuals and can fluctuate over time — especially during disease and early development. Viewing the microbiota from an ecological perspective could provide insight into how to promote health by targeting this microbial community in clinical treatments.
3,890 citations
National Institutes of Health1, Cardiff University2, VU University Amsterdam3, Erasmus University Rotterdam4, University of Manchester5, University College London6, University of Helsinki7, University of Oulu8, Johns Hopkins University9, Georgetown University10, Illumina11, University Hospital of Wales12, University of Eastern Finland13, University of Miami14, University of Turin15, University of Cagliari16, The Catholic University of America17, Microsoft18, University of Toronto19, University of Würzburg20, University of Washington21, Aneurin Bevan University Health Board22
TL;DR: The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases, and a large hexanucleotide repeat expansion in the first intron of C9ORF72 is shown.
3,784 citations
TL;DR: The Discriminant Analysis of Principal Components (DAPC) is introduced, a multivariate method designed to identify and describe clusters of genetically related individuals that performs generally better than STRUCTURE at characterizing population subdivision.
Abstract: The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.
3,770 citations