Modern synthesis (20th century)The modern synthesis was the early 20th-century synthesis of Charles Darwin's theory of evolution and Gregor Mendel's ideas on heredity into a joint mathematical framework. Julian Huxley coined the term in his 1942 book, Evolution: The Modern Synthesis. The synthesis combined the ideas of natural selection, Mendelian genetics, and population genetics. It also related the broad-scale macroevolution seen by palaeontologists to the small-scale microevolution of local populations.
Cross elasticity of demandIn economics, the cross (or cross-price) elasticity of demand measures the effect of changes in the price of one good on the quantity demanded of another good. This reflects the fact that the quantity demanded of good is dependent on not only its own price (price elasticity of demand) but also the price of other "related" good. The cross elasticity of demand is calculated as the ratio between the percentage change of the quantity demanded for a good and the percentage change in the price of another good, ceteris paribus:The sign of the cross elasticity indicates the relationship between two goods.
Complex traitsComplex 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.
Molecular epidemiologyMolecular epidemiology is a branch of epidemiology and medical science that focuses on the contribution of potential genetic and environmental risk factors, identified at the molecular level, to the etiology, distribution and prevention of disease within families and across populations. This field has emerged from the integration of molecular biology into traditional epidemiological research. Molecular epidemiology improves our understanding of the pathogenesis of disease by identifying specific pathways, molecules and genes that influence the risk of developing disease.
Molecular diagnosticsMolecular diagnostics is a collection of techniques used to analyze biological markers in the genome and proteome, and how their cells express their genes as proteins, applying molecular biology to medical testing. In medicine the technique is used to diagnose and monitor disease, detect risk, and decide which therapies will work best for individual patients, and in agricultural biosecurity similarly to monitor crop- and livestock disease, estimate risk, and decide what quarantine measures must be taken.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Genetic correlationIn multivariate quantitative genetics, a genetic correlation (denoted or ) 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.
Quantitative geneticsQuantitative genetics deals with quantitative traits, which are phenotypes that vary continuously (such as height or mass)—as opposed to discretely identifiable phenotypes and gene-products (such as eye-colour, or the presence of a particular biochemical). Both branches use the frequencies of different alleles of a gene in breeding populations (gamodemes), and combine them with concepts from simple Mendelian inheritance to analyze inheritance patterns across generations and descendant lines.
Patient-reported outcomeA patient-reported outcome (PRO) is a health outcome directly reported by the patient who experienced it. It stands in contrast to an outcome reported by someone else, such as a physician-reported outcome, a nurse-reported outcome, and so on. PRO methods, such as questionnaires, are used in clinical trials or other clinical settings, to help better understand a treatment's efficacy or effectiveness. The use of digitized PROs, or electronic patient-reported outcomes (ePROs), is on the rise in today's health research setting.
Markov modelIn probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.
