Nature versus nurture is a long-standing debate in biology and society about the relative influence on human beings of their genetic inheritance (nature) and the environmental conditions of their development (nurture). The alliterative expression "nature and nurture" in English has been in use since at least the Elizabethan period and goes back to medieval French. The complementary combination of the two concepts is an ancient concept. Nature is what people think of as pre-wiring and is influenced by genetic inheritance and other biological factors. Nurture is generally taken as the influence of external factors after conception e.g. the product of exposure, experience and learning on an individual.
Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"
Twin studies are studies conducted on identical or fraternal twins. They aim to reveal the importance of environmental and genetic influences for traits, phenotypes, and disorders. Twin research is considered a key tool in behavioral genetics and in related fields, from biology to psychology. Twin studies are part of the broader methodology used in behavior genetics, which uses all data that are genetically informative – siblings studies, adoption studies, pedigree, etc. These studies have been used to track traits ranging from personal behavior to the presentation of severe mental illnesses such as schizophrenia.
In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations. As a simple example, one would expect the age and height of a sample of teenagers from a high school to have a Pearson correlation coefficient significantly greater than 0, but less than 1.
Quantitative genetics is the study of quantitative traits, which are phenotypes that vary continuously—such as height or mass—as opposed to phenotypes and gene-products that are discretely identifiable—such as eye-colour, or the presence of a particular biochemical.
Genetic variation is the difference in DNA among individuals or the differences between populations among the same species. The multiple sources of genetic variation include mutation and genetic recombination. Mutations are the ultimate sources of genetic variation, but other mechanisms, such as genetic drift, contribute to it, as well.
In genetics, concordance is the probability that a pair of individuals will both have a certain characteristic given that one of the pair has the characteristic. Concordance can be measured with concordance rates, reflecting the odds of one person having the trait if the other does. Important clinical examples include the chance of offspring having a certain disease if the mother has it, if the father has it, or if both parents have it. Concordance among siblings is similarly of interest: what are the odds of a subsequent offspring having the disease if an older child does? In research, concordance is often discussed in the context of both members of a pair of twins. Twins are concordant when both have or both lack a given trait. The ideal example of concordance is that of identical twins, because the genome is the same, an equivalence that helps in discovering causation via deconfounding, regarding genetic effects versus epigenetic and environmental effects.
The heritability of autism is the proportion of differences in expression of autism that can be explained by genetic variation; if the heritability of a condition is high, then the condition is considered to be primarily genetic. Autism has a strong genetic basis. Although the genetics of autism are complex, autism spectrum disorder (ASD) is explained more by multigene effects than by rare mutations with large effects.
Research on the heritability of IQ inquires into the degree of variation in IQ within a population that is due to genetic variation between individuals in that population. There has been significant controversy in the academic community about the heritability of IQ since research on the issue began in the late nineteenth century. Intelligence in the normal range is a polygenic trait, meaning that it is influenced by more than one gene, and in the case of intelligence at least 500 genes. Further, explaining the similarity in IQ of closely related persons requires careful study because environmental factors may be correlated with genetic factors.
The study of height and intelligence examines correlations between human height and human intelligence. Some epidemiological research on the subject has shown that there is a small but statistically significant positive correlation between height and intelligence after controlling for socioeconomic class and parental education. The cited study, however, does not draw any conclusions about height and intelligence, but rather suggests "a continuing effect of post-natal growth on childhood cognition beyond the age of 9 years." This correlation arises in both the developed and developing world and persists across age groups. An individual's taller stature has been attributed to higher economic status, which often translates to a higher quality of nutrition. This correlation, however, can be inverted to characterize one's socioeconomic status as a consequence of stature, where shorter stature can attract discrimination that affects many factors, among them employment, and treatment by educators. One such theory argues that since height strongly correlates with white and gray matter volume, it may act as a biomarker for cerebral development which itself mediates intelligence. Competing explanations include that certain genetic factors may influence both height and intelligence, or that both height and intelligence may be affected in similar ways by adverse environmental exposures during development. Measurements of the total surface area and mean thickness of the cortical grey matter using a magnetic resonance imaging (MRI) revealed that the height of individuals had a positive correlation with the total cortical surface area. This supports the idea that genes that influence height also influence total surface area of the brain, which in turn influences intelligence, resulting in the correlation. Other explanations further qualify the positive correlation between height and intelligence, suggesting that because the correlation becomes weaker with higher socioeconomic class and education level, environmental factors could partially override any genetic factors affecting both characteristics.
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient, is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical dependence based on the τ coefficient. It is a measure of rank correlation: the similarity of the orderings of the data when ranked by each of the quantities. It is named after Maurice Kendall, who developed it in 1938, though Gustav Fechner had proposed a similar measure in the context of time series in 1897.
In probability theory and statistics, partial correlation measures the degree of association between two random variables, with the effect of a set of controlling random variables removed. When determining the numerical relationship between two variables of interest, using their correlation coefficient will give misleading results if there is another confounding variable that is numerically related to both variables of interest. This misleading information can be avoided by controlling for the confounding variable, which is done by computing the partial correlation coefficient. This is precisely the motivation for including other right-side variables in a multiple regression; but while multiple regression gives unbiased results for the effect size, it does not give a numerical value of a measure of the strength of the relationship between the two variables of interest.
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.
In statistical genetics, inclusive composite interval mapping (ICIM) has been proposed as an approach to QTL mapping for populations derived from bi-parental crosses. QTL mapping is based on genetic linkage map and phenotypic data to attempt to locate individual genetic factors on chromosomes and to estimate their genetic effects.
Genetic variance is a concept outlined by the English biologist and statistician Ronald Fisher in his fundamental theorem of natural selection. In his 1930 book The Genetical Theory of Natural Selection, Fisher postulates that the rate of change of biological fitness can be calculated by the genetic variance of the fitness itself. Fisher tried to give a statistical formula about how the change of fitness in a population can be attributed to changes in the allele frequency. Fisher made no restrictive assumptions in his formula concerning fitness parameters, mate choices or the number of alleles and loci involved.
Genome-wide complex trait analysis (GCTA) Genome-based restricted maximum likelihood (GREML) is a statistical method for heritability estimation in genetics, which quantifies the total additive contribution of a set of genetic variants to a trait. GCTA is typically applied to common single nucleotide polymorphisms (SNPs) on a genotyping array and thus termed "chip" or "SNP" heritability.
Family resemblance refers to physical similarities shared between close relatives, especially between parents and children and between siblings. In psychology, the similarities of personality are also observed.
In behavioural genetics, DeFries–Fulker (DF) regression, also sometimes called DeFries–Fulker extremes analysis, is a type of multiple regression analysis designed for estimating the magnitude of genetic and environmental effects in twin studies. It is named after John C. DeFries and David Fulker, who first proposed it in 1985. It was originally developed to assess heritability of reading disability in twin studies, but it has since been used to assess the heritability of other cognitive traits, and has also been applied to non-twin methodologies.
Personality traits are patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances.
