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Eric Steven Lander is an American mathematician and geneticist who is a professor of biology at the Massachusetts Institute of Technology (MIT), and a professor of systems biology at Harvard Medical School. Eric Lander is founding director emeritus of the Broad Institute of MIT and Harvard. He is a 1987 MacArthur Fellow and Rhodes Scholar.
deCODE genetics is a biopharmaceutical company based in Reykjavík, Iceland. The company was founded in 1996 by Kári Stefánsson with the aim of using population genetics studies to identify variations in the human genome associated with common diseases, and to apply these discoveries "to develop novel methods to identify, treat and prevent diseases."
The Eli and Edythe L. Broad Institute of MIT and Harvard, often referred to as the Broad Institute, is a biomedical and genomic research center located in Cambridge, Massachusetts, United States. The institute is independently governed and supported as a 501(c)(3) nonprofit research organization under the name Broad Institute Inc., and it partners with the Massachusetts Institute of Technology, Harvard University, and the five Harvard teaching hospitals.
In genomics, a genome-wide association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.
Pardis Christine Sabeti is an Iranian American computational biologist, medical geneticist, and evolutionary geneticist. She developed a bioinformatic statistical method which identifies sections of the genome that have been subject to natural selection and an algorithm which explains the effects of genetics on the evolution of disease.
In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.
Gilean Alistair Tristram McVean is a professor of statistical genetics at the University of Oxford, fellow of Linacre College, Oxford and co-founder and director of Genomics plc. He also co-chaired the 1000 Genomes Project analysis group.
In statistics, the maximal information coefficient (MIC) is a measure of the strength of the linear or non-linear association between two variables X and Y.
Genome-wide complex trait analysis (GCTA) Genome-based restricted maximum likelihood (GREML) is a statistical method for variance component estimation in genetics which quantifies the total narrow-sense (additive) contribution to a trait's heritability of a particular subset of genetic variants. This is done by directly quantifying the chance genetic similarity of unrelated individuals and comparing it to their measured similarity on a trait; if two unrelated individuals are relatively similar genetically and also have similar trait measurements, then the measured genetics are likely to causally influence that trait, and the correlation can to some degree tell how much. This can be illustrated by plotting the squared pairwise trait differences between individuals against their estimated degree of relatedness. The GCTA framework can be applied in a variety of settings. For example, it can be used to examine changes in heritability over aging and development. It can also be extended to analyse bivariate genetic correlations between traits. There is an ongoing debate about whether GCTA generates reliable or stable estimates of heritability when used on current SNP data. The method is based on the outdated and false dichotomy of genes versus the environment. It also suffers from serious methodological weaknesses, such as susceptibility to population stratification.
In genetics, a polygenic score (PGS), also called a polygenic index (PGI), polygenic risk score (PRS), genetic risk score, or genome-wide score, is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype, typically calculated as a weighted sum of trait-associated alleles. It reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. In other words, it gives an estimate of how likely an individual is to have a given trait only based on genetics, without taking environmental factors into account. Polygenic scores are widely used in animal breeding and plant breeding due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from genome-wide association study (GWAS) data.
Manolis Kellis is a professor of Computer Science at the Massachusetts Institute of Technology (MIT) in the area of Computational Biology and a member of the Broad Institute of MIT and Harvard. He is the head of the Computational Biology Group at MIT and is a Principal Investigator in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Naomi Ruth Wray is an Australian statistical geneticist at the University of Queensland, where she is a Professorial Research Fellow at the Institute for Molecular Bioscience and an Affiliate Professor in the Queensland Brain Institute. She is also a National Health and Medical Research Council (NHMRC) Principal Research Fellow and, along with Peter Visscher and Jian Yang, is one of the three executive team members of the NHMRC-funded Program in Complex Trait Genomics.
Benjamin Michael Neale is a statistical geneticist with a specialty in psychiatric genetics. He is an institute member at the Broad Institute as well as an associate professor at both Harvard Medical School and the Analytic and Translational Genetics Unit at Massachusetts General Hospital. Neale specializes in genome-wide association studies (GWAS). He was responsible for the data analysis of the first GWAS on attention-deficit/hyperactivity-disorder, and he developed new analysis software such as PLINK, which allows for whole-genome data to be analyzed for specific gene markers. Related to his work on GWAS, Neale is the lead of the ADHD psychiatric genetics and also a member of the Psychiatric GWAS Consortium analysis committee.
In statistical genetics, linkage disequilibrium score regression is a technique that aims to quantify the separate contributions of polygenic effects and various confounding factors, such as population stratification, based on summary statistics from genome-wide association studies (GWASs). The approach involves using regression analysis to examine the relationship between linkage disequilibrium scores and the test statistics of the single-nucleotide polymorphisms (SNPs) from the GWAS. Here, the "linkage disequilibrium score" for a SNP "is the sum of LD r2 measured with all other SNPs".
In population genetics, cryptic relatedness occurs when individuals in a genetic association study are more closely related to another population than assumed by the investigators. This can act as a confounding factor in both case-control and genome-wide association studies, as well as in studies of genetic diversity. Along with population stratification, it is one of the most prominent confounding factors that can lead to inflated false positive rates in gene-association studies. It is often corrected for by including a polygenic component in the statistical model being used to detect genetic associations. Other approaches that have been developed to attempt to control for cryptic relatedness are the genomic control method and the use of extended likelihood ratio tests.
The GWAS catalog is a free online database that compiles data of genome-wide association studies (GWAS), summarizing unstructured data from different literature sources into accessible high quality data. It was created by the National Human Genome Research Institute (NHGRI) in 2008 and have become a collaborative project between the NHGRI and the European Bioinformatics Institute (EBI) since 2010. As of September 2018, it has included 71,673 SNP–trait associations in 3,567 publications.
Stephanie J. London is an American epidemiologist and physician-scientist specializing in environmental health, respiratory diseases, and genetic susceptibility. She is the deputy chief of the epidemiology branch at the National Institute of Environmental Health Sciences.
Transcriptome-wide association study (TWAS) is a genetic methodology that can be used to compare the genetic components of gene expression and the genetic components of a trait to determine if an association is present between the two components. TWAS are useful for the identification and prioritization of candidate causal genes in candidate gene analysis following genome-wide association studies. TWAS looks at the RNA products of a specific tissue and gives researchers the abilities to look at the genes being expressed as well as gene expression levels, which varies by tissue type. TWAS are valuable and flexible bioinformatics tools that looks at the associations between the expressions of genes and complex traits and diseases. By looking at the association between gene expression and the trait expressed, genetic regulatory mechanisms can be investigated for the role that they play in the development of specific traits and diseases.
Eric R. Gamazon is a statistical geneticist in Vanderbilt University, with faculty affiliations in the Division of Genetic Medicine, Data Science Institute, and Center for Precision Medicine. He is a Life Member of Clare Hall, Cambridge University after election to a Visiting Fellowship (2018).
Alexander (Sasha) Gusev is a computational biologist and an Assistant Professor of Medicine at Harvard Medical School.
References
- 1 2 3 4 5 6 7 8 "Linking datasets together to understand disease". Broad Institute. 2019-01-16. Retrieved 2020-08-16.
- ↑ "The Kendall Square Codebreakers | Magazine | The Harvard Crimson". www.thecrimson.com. Retrieved 2020-08-16.
- 1 2 "Relating to Relationships". Weizmann Institute. 2012-03-05.
- ↑ "Hilary Finucane Receives NIH Award – Women In Math". math.mit.edu. Retrieved 2020-08-16.
- ↑ "Faces of the Foundation: Hilary Finucane". The Fannie and John Hertz Foundation | Empowering Limitless Progress. Retrieved 2020-08-16.
- ↑ "Ten researchers from MIT and Broad receive NIH Director's Awards". MIT News. Retrieved 2020-08-16.
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