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Alexander Gusev | |
|---|---|
| Education | Columbia University (PhD) |
| Scientific career | |
| Fields | Statistical genetics Oncogenomics Human genetics |
| Institutions | Harvard Medical School |
| Thesis | Quantifying recent variation and relatedness in human populations (2012) |
| Doctoral advisor | Itsik Pe'er |
Alexander (Sasha) Gusev is a computational biologist and an Assistant Professor of Medicine at Harvard Medical School. [1]
Alexander Gusev has developed computational methods that use genetic data to decipher disease mechanisms. [2] For example, he has identified 34 new genes associated with increased risk of earliest-stage ovarian cancer. [3] He has developed computational methods that integrate molecular data to facilitate functional interpretation of findings from genome-wide association studies. [4] He has contributed to the development of the transcriptome-wide association study approach to mapping disease-associated genes. [5] In addition, he studies the interactions between germline (host) and somatic events (tumor) - which are typically studied separately - and their effects on cancer progression and treatment response to advance precision oncology.
In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome that is present in a sufficiently large fraction of considered population.
A DNA segment is identical by state (IBS) in two or more individuals if they have identical nucleotide sequences in this segment. An IBS segment is identical by descent (IBD) in two or more individuals if they have inherited it from a common ancestor without recombination, that is, the segment has the same ancestral origin in these individuals. DNA segments that are IBD are IBS per definition, but segments that are not IBD can still be IBS due to the same mutations in different individuals or recombinations that do not alter the segment.
In molecular biology, SNP array is a type of DNA microarray which is used to detect polymorphisms within a population. A single nucleotide polymorphism (SNP), a variation at a single site in DNA, is the most frequent type of variation in the genome. Around 335 million SNPs have been identified in the human genome, 15 million of which are present at frequencies of 1% or higher across different populations worldwide.
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.
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.
Behavioural genetics, also referred to as behaviour genetics, is a field of scientific research that uses genetic methods to investigate the nature and origins of individual differences in behaviour. While the name "behavioural genetics" connotes a focus on genetic influences, the field broadly investigates the extent to which genetic and environmental factors influence individual differences, and the development of research designs that can remove the confounding of genes and environment. Behavioural genetics was founded as a scientific discipline by Francis Galton in the late 19th century, only to be discredited through association with eugenics movements before and during World War II. In the latter half of the 20th century, the field saw renewed prominence with research on inheritance of behaviour and mental illness in humans, as well as research on genetically informative model organisms through selective breeding and crosses. In the late 20th and early 21st centuries, technological advances in molecular genetics made it possible to measure and modify the genome directly. This led to major advances in model organism research and in human studies, leading to new scientific discoveries.
Expression quantitative trait loci (eQTLs) are genomic loci that explain variation in expression levels of mRNAs.
The missing heritability problem is the fact that single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This is a problem that has significant implications for medicine, since a person's susceptibility to disease may depend more on the combined effect of all the genes in the background than on the disease genes in the foreground, or the role of genes may have been severely overestimated.
Phosphatase and actin regulator 1 (PHACTR1) is a protein that in humans is encoded by the PHACTR1 gene on chromosome 6. It is most significantly expressed in the globus pallidus of the brain. PHACTR1 is an actin and protein phosphatase 1 (PP1) binding protein that binds actin and regulates the reorganization of the actin cytoskeleton. This protein has been associated with coronary artery disease and migraines through genome-wide association studies. The PHACTR1 gene also contains one of 27 SNPs associated with increased risk of coronary artery disease.
Predictive genomics is at the intersection of multiple disciplines: predictive medicine, personal genomics and translational bioinformatics. Specifically, predictive genomics deals with the future phenotypic outcomes via prediction in areas such as complex multifactorial diseases in humans. To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in genome-wide association (GWA) studies. The identification of associated single-nucleotide polymorphisms underpin GWA studies in complex diseases that have ranged from Type 2 Diabetes (T2D), Age-related macular degeneration (AMD) and Crohn's disease.
Michael Edward "Mike" Goddard is a professorial fellow in animal genetics at the University of Melbourne, Australia.
In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score or genetic risk score. The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated alleles.
Complex traits, also known as quantitative traits, are traits that do not behave according to simple Mendelian inheritance laws. More specifically, their inheritance cannot be explained by the genetic segregation of a single gene. Such traits show a continuous range of variation and are influenced by both environmental and genetic factors. Compared to strictly Mendelian traits, complex traits are far more common, and because they can be hugely polygenic, they are studied using statistical techniques such as quantitative genetics and quantitative trait loci (QTL) mapping rather than classical genetics methods. Examples of complex traits include height, circadian rhythms, enzyme kinetics, and many diseases including diabetes and Parkinson's disease. One major goal of genetic research today is to better understand the molecular mechanisms through which genetic variants act to influence complex traits.
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".
The Omnigenic Model, first proposed by Evan A. Boyle, Yang I. Li, and Jonathan K. Pritchard, describes a hypothesis regarding the heritability of complex traits. Expanding beyond polygenes, the authors propose that all genes expressed within a cell affect the expression of a given trait. In addition, the model states that the peripheral genes, ones that do not have a direct impact on expression, explain more heritability of traits than core genes, ones that have a direct impact on expression. The process that the authors propose that facilitates this effect is called “network pleiotropy”, in which peripheral genes can affect core genes, not by having a direct effect, but rather by virtue of being mediated within the same cell.
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.
N-Acetylated Alpha-Linked Acidic Dipeptidase Like 2 (NAALADL2) is a protein, encoded by the gene NAALADL2 in humans. NAALADL2 shares 25%–26% sequence identity and 45% sequence similarity with the glutamate carboxypeptidase II family which includes prostate cancer marker PSMA (FOLH1/NAALAD1). The NAALADL2 gene is a giant gene spanning 1.37 Mb which is approximately 49 times larger than the average gene size of 28 kb. Gene length is correlated with the number of transcript variants of a gene, as such, NAALADL2 undergoes extensive alternative splicing and has 12 splice variants as defined by Ensembl.
Hilary Kiyo Finucane is an American computational biologist who is Co-Director of the Program in Medical and Population Genetics at the Broad Institute. Her group combines genetic data with molecular data to understand the origins and mechanisms of disease.
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).
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