Molecular Marker part Flashcards
Explain the main principle of linkage mapping and mention the steps to perform this analysis.
Pages 1 and 6 of linkage mapping.
a. Genetic linkage describes the tendency of a certain loci or alles to be inherited together. Genetic loci on the same chromosome are physically close to one another and tend to stay together during meiosis and are thus genetically linked. A linkage map is constructed by following steps:
i. Analysis of single locus models (to observe segregation pattern)
ii. Analysis of two-locus model (observing segregation classes and deviation from Mendel- testing for significance)
iii. Linkage grouping (identification of markers that are linked in the same group, usage of LOD with threshold value-> determination of the order->mapping)
iv. Locus ordering (minimum sum of adjacent recombination frequency SARF)
v. Multi-point analysis (Considering only adjacent loci or using all the information)
Explain the main principle of QTL mapping (three points) the answer is more or less covered in
the page 3 from QTL mapping.
OR: Explain the major steps (including pre-requisites and basic principle) of QTL mapping.
a. Quantitative trait loci mapping/analysis is a statistical method that links phenotypic data(trait measurements) and genotypic data (usually molecular markers) in an attempt to explain the genetic basis of variation in complex traits. The goal of QTL mapping is to identify the action, interaction, number and precise location of these regions.
QTL are the DNA regions underlying a quantitative character. QTL mapping seeks to discover:
how many QTL influence a trait
where they are located in the genome
what is their contribution to pheno- and genotypic variation
what is the type of gene action
what is their function
The steps to answer these questions:
in a basic ANOVA method is to analyse a segregating population for markers associated with (or
without) the trait of interest by performing a series of t-tests at each marker loci to compare the
averages of the two marker genotype groups
the more commonly applied method uses a LOD score (logarithm of odds ratio) which provides a
correlation of phenotype and the marker genotype for each individual in the experimental cross.
What is association mapping and why LD is so important in this methodology? Page 5 of
association mapping
a. Association mapping, also known as “Linkage Disequilibrium mapping”, is a method of mapping QTL that takes advantage of linkage disequilibrium to link phenotypes to genotypes.
Association mapping looks for genetic markers with higher frequencies in population with target
trait, compared to a reference population (without trait), suggesting an association between a
target phenotype and allelic variation.
LD refers to the association of alleles at separate loci. By investigating LD, it is the first step for
association mapping, to understand the mapping resolution that can be realized and the required
marker density
Mention two applications of marker assisted backcrossing. Slide 8 of marker-assisted
backcrossing.
1: Marker assisted foreground selection. Requires markers closely linked to the target gene in order
to trace its presence. E.g. recessive traits
2: Marker assisted background selection. Requires markers spread across the whole genome to
select against the genetic background of donor. E.g. To speed up selection in introgression of
disease resistance gene
What is the difference between expected and realized relationship? Which methodology in
Genomic Prediction profits from this difference? Slides 13 and 14 from genomic prediction
a. The realised relationship matrix disentangles genetic effect that are confounded in the expected relationship due to environmental or family effects. The contribution of each parent is measured.
The difference between expected and realized relationship in breeding can impact the accuracy of trait predictions and selection effectiveness due to incomplete pedigree info, recombination, and mutation. Genomic selection uses genomic data to predict breeding value by accounting for the realized relationship, improving selection efficiency
Explain what a SNP is and give an example. Name two methods for SNP
detection.
A SNP is a single nucleotide polymorphism, one of the most abundant forms of DNA polymorphisms
in a genome. SNPs are biallelic.
Example: C/A SNP, at a specific base position in a genome, the C nucleotide may appear in most
individuals, but in a minority of individuals, the position is occupied by an A.
Methods for SNP detection:
Allele-specific PCR
Single-base extension assay
Taqman
KASP
Illumina Technology
Microarray-based genotyping
Describe the principal workflow of genomic prediction. b) Briefly describe the
problem of overfitting and its consequences.
Genomic prediction combines marker data with phenotypic and pedigree data (when available) in an
attempt to increase the accuracy of the prediction of breeding and genotypic values. A training
population is used to train a model to make selection on selection candidates:
-The training population is phenotyped and genotyped and marker effects are estimated
-This information is used with genotypic information from the selection candidates to calculate GBVs
-From here, selection decisions are made
Overfitting can occur when genetic variance is estimated from many trait-irrelevant markers; in other
words it is when the model mistakes noise for signal which typically manifests as good performance
in the training dataset but poor performance in independent datasets.
What is a SNP? Name 4 approaches for their detection.
SNP is a single nucleotide polymorphism, which means, that there are different types of alleles in a
population, which are only varying in a single nucleotide. Detection approaches are e.g. allele specific
PCR, single base extension assay, Taqman, genoyping, KASP, Illumina
What is the expected proportion of genome for BC1 and BC2? Draw BC1
BC2 distribution.
The expected proportion in BC 1 is 75 % of the recurrent parent genome, 87.5 % in BC 2, respectively.
The broadness of the curves depends on the genetic architecture of the species (number of
chromosomes etc.)
Explain QTL approach for multiple testing correction.
The idea behind multiple testing is to account for false positive QTL detections. This can be done by
using the Bonferroni correction, which, however, assumes unlinked markers. Another approach is to
use permutation analysis. Thereby, randomly permutating the trait values will only allow QTL effects
due to chance. This allows the development of comparison-wise and experiment-wise critical values,
bur requires many shuffles, depending on alpha.
