So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Genetics and analysis of quantitative traits

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Cardiac dysfunction resulting from exposure to cancer therapeutics
was first recognized in the 1960s, with the widespread introduction
of anthracyclines into the oncologic therapeutic armamentarium.
Heart failure (HF) associated with anthracyclines was then recognized
as an important side effect. As a result, physicians learned to limit their
doses to avoid cardiac dysfunction. Several strategies have been used
over the past decades to detect it. Two of them evolved over time
to be very useful: endomyocardial biopsies and monitoring of left ven-
tricular (LV) ejection fraction (LVEF) by cardiac imaging. Examination
of endomyocardial biopsies proved to be the most sensitive and spe-
cific parameter for the identification of anthracycline-induced LV
dysfunction and became the gold standard in the 1970s. However,
the interest in endomyocardial biopsy has diminished over time
because of the reduction in the cumulative dosages used to treat ma-
lignancies, the invasive nature of the procedure, and the remarkable
progress made in noninvasive cardiac imaging. The noninvasive
evaluation of LVEF has gained importance, and notwithstanding the
limitations of the techniques used for its calculation, has emerged as
the most widely used strategy for monitoring the changes in cardiac
function, both during and after the administration of potentially car-
diotoxic cancer treatment.

Expert Consensus for Multimodality Imaging Evaluation of Adult Patients during and after Cancer Therapy: A Report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.

When testing for reduction of the mean value structure in linear mixed models, it is common to use an asymptotic ?2 test. Such tests can, however, be very poor for small and moderate sample sizes. The pbkrtest package implements two alternatives to such approximate ?2 tests: The package implements (1) a Kenward-Roger approximation for performing F tests for reduction of the mean structure and (2) parametric bootstrap methods for achieving the same goal. The implementation is focused on linear mixed models with independent residual errors. In addition to describing the methods and aspects of their implementation, the paper also contains several examples and a comparison of the various methods.

A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models: The R Package pbkrtest

Residual maximum likelihood estimation of (co)variance components in multivariate mixed linear models using average information

Genotype by environment interaction for production and fertility was studied by use of a reaction norm model. Milk recording data, comprising 927 929 records, were analysed to predict reaction norms for young bulls of the Nordic Red dairy breeds. Random regressions were estimated for each bull, regressing phenotypic values of daughters on herd environment. The phenotypic measures were 305 days kg protein production and days open in first lactation. The herd environment was defined as the herd-year average of protein production and days open, respectively. Heritability of protein production and days open and genetic correlation between the two traits were estimated as functions of the herd environment. The results showed that the genetic parameters change over environments, which are measured on a continuous scale across countries. Grouping of observations is avoided and thereby the problem of genetic connectedness between groups or countries may be avoided. Although significant genetic variation for the s...

Genotype by Environment Interaction in Nordic Dairy Cattle Studied Using Reaction Norms

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Just Jensen

Papers

Residual maximum likelihood estimation of (co)variance components in multivariate mixed linear models using average information

Genotype by Environment Interaction in Nordic Dairy Cattle Studied Using Reaction Norms

DMU - A Package for Analyzing Multivariate Mixed Models in Quantitative Genetics and Genomics

Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML.

Genetic evaluation of dairy cattle using test-day models