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

Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Genetics have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits--describing the methods, limitations, and recent applications to biological problems.

https://focus.psychiatryonline.org/doi/pdf/10.1176/foc.4.3.442

Genetic dissection of complex traits

Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Geneticists have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits—describing the methods, limitations, and recent applications to biological problems.

Genetic Dissection of Complex Traits

Oilseed rape (Brassica napus L.) was formed ~7500 years ago by hybridization between B. rapa and B. oleracea, followed by chromosome doubling, a process known as allopolyploidy. Together with more ancient polyploidizations, this conferred an aggregate 72× genome multiplication since the origin of angiosperms and high gene content. We examined the B. napus genome and the consequences of its recent duplication. The constituent An and Cn subgenomes are engaged in subtle structural, functional, and epigenetic cross-talk, with abundant homeologous exchanges. Incipient gene loss and expression divergence have begun. Selection in B. napus oilseed types has accelerated the loss of glucosinolate genes, while preserving expansion of oil biosynthesis genes. These processes provide insights into allopolyploid evolution and its relationship with crop domestication and improvement.

http://science.sciencemag.org/content/sci/345/6199/950.full.pdf

Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome

LEARNING The objectives of BIOS 781 are to present: OBJECTIVES: 1. basic population and quantitative genetic principles, including classical genetics, chromosomal theory of inheritance, and meiotic recombination 2. an exposure to QTL mapping methods of complex quantitative traits and linkage methods to detect co-segregation with disease 3. methods for assessing marker-disease linkage disequilibrium, including case-control approaches 4. methods for genome-wide association and stratification control.

Genetic Data Analysis

Summary: Breeding programs face the challenge of integrating information from genomics and from quantitative trait loci (QTL) analysis in order to identify genomic sequences controlling the variation of important traits. Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. BioMercator has been developed to automate map compilation and QTL meta-analysis, and to visualize co-locations between genes and QTL through a graphical interface.

Availability: Available upon request (http://moulon/~bioinfo/BioMercator/). Free of charge for academic use.

BioMercator: integrating genetic maps and QTL towards discovery of candidate genes

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F2 populations of 150 F2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R
 2 increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize.

Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes.We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.

/pdf/novel-measures-of-linkage-disequilibrium-that-correct-the-3dz57g5srf.pdf

Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness

We describe a method for constructing the confidence interval of the QTL location parameter. This method is developed in the local asymptotic framework, leading to a linear model at each position of the putative QTL. The idea is to construct a likelihood ratio test, using statistics whose asymptotic distribution does not depend on the nuisance parameters and in particular on the effect of the QTL. We show theoretical properties of the confidence interval built with this test, and compare it with the classical confidence interval using simulations. We show in particular, that our confidence interval has the correct probability of containing the true map location of the QTL, for almost all QTLs, whereas the classical confidence interval can be very biased for QTLs having small effect.

https://www.genetics.org/content/genetics/138/4/1301.full.pdf

Constructing Confidence Intervals for Qtl Location

Statistical methods for the detection of genes influencing quantitative trait (QTLs) with the aid of genetic markers are well developed for the analysis of a single trait. In practice, many experimental data contain observations on multiple correlated traits and methods that permit joint analysis of all traits are now required. Generalization of the maximum likelihood method to a multitrait analysis is a good approach, but the increase in complexity, due to the number of parameters to be estimated simultaneously, could restrain its practical use when the number of traits is large. We propose an alternative method based on two separate steps. The first step is to estimate the (co)variance matrix of the traits and use this estimate to obtain the canonical variables associated to the traits. The second step is to apply a single-trait maximum likelihood method to each of the canonical variables and to combine the results. Working in a local asymptotic framework for the effects of the putative pleiotropic QTL, i.e., for a pleiotropic QTL whose effect is too small to be detected with certainty, we prove that the combined analysis with canonical variables is asymptotically equivalent to a multitrait maximum likehood analysis. A threshold for the mapping of the pleiotropic QTL is also given. The probability of detecting a QTL is not always increased by the addition of more correlated traits. As an example, a theoretical comparison between the power of a multitrait analysis with two variables and the power of a single-trait analysis is presented. Experimental data collected to study the polygenic resistance of tomato plants to bacterial wilt are used to illustrate the combined analysis with canonical variables.

https://www.jstor.org/stable/info/2533998

Brigitte Mangin

Papers

BioMercator: integrating genetic maps and QTL towards discovery of candidate genes

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize.

Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness

Constructing Confidence Intervals for Qtl Location

Pleiotropic qtl analysis