The aim of the workshop is to bring scientist together from industry, research institutes and universities that work on investigation and / or implementation of QTL mapping and marker assisted selection, in order to exchange their latest research results or share their experiences in analysis of marker data.
One of the key components of the workshop will be presentations from the following three invited speakers:
Animal Breeding & Genetics
Dr. A. de Vries
CRV Holding BV - Company page
alfred.de.vries@crv4all.com
TITLE
The application of Genomic Selection in dairy cattle
ABSTRACT
For a long time, genetic improvement of dairy cattle has relied on progeny testing of bulls. Test data from daughters are used to calculate BLUP breeding values for a large number of productive, functional and type traits. Progeny testing gives high accuracies of selection, but is expensive and slow. Usually, bulls are already 5 years old when they can be promoted to a commercial bull. By that time the investment per bull is close to 25,000 euro.
The use of DNA-markers allow earlier selection steps, and thus can lead to a more efficient breeding programme. A lot of research has been done to find QTLs to be exploited in marker-assisted breeding programmes. However, the variation explained with the small sets of markers was too limited.
A new approach is the use of high density marker sets covering the entire genome. Early 2007, CRV and the University of Liege developed a custom made 60K SNP BeadChip (Illumina) using publicly available SNPs. The marker effects were derived from a reference panel of 3600 bulls with reliable breeding values. Recent validations show that the marker effects can predict breeding values of bulls with up to 70% reliablity. Selection based on these genome-wide markers is referred to as Genomic Selection.
The relatively high reliabilities with Genomic Selection offer big opportunities for cattle breeding organizations. Genotyping costs per animal are very low compared to phenotypic testing of a bull. Moreover, genotyping can be done shortly after birth. CRV is now exploiting these new opportunities after a redesign of the breeding scheme. We have doubled our selection pool of young males and females. All selection candidates animals are genotyped, and only the top 20% enter the phenotypic testing programme. With this new selection scheme, genetic improvement can be accelerated with 30-40%. Morover, it can reduce inbreeding, as more families get a chance to contribute to the next generation.
Human Genetics
Prof. R. Ophoff1,2
1 Department of Medical Genetics, UMC Utrecht NL
2 Centre for Neurobehavioral Genetics, UCLA Los Angeles, USA
ophoff@ucla.edu - Personal page
TITLE
Genomic Perspectives of Schizophrenia
ABSTRACT
Schizophrenia is a complex disorder, caused by both genetic and environmental factors and their interactions. Research on pathogenesis has traditionally focused on neurotransmitter systems in the brain, particularly those involving dopamine. Schizophrenia has been considered a separate disease for over a century, but in the absence of clear biomarkers, diagnosis has historically been based on signs and symptoms. A fundamental message emerging from genome-wide association studies (GWASs) of copy number variations (CNVs) associated with the disease is that its genetic basis does not necessarily conform to classical nosological disease boundaries. Certain CNVs confer not only high relative risk of schizophrenia but also of other psychiatric disorders. The structural variations associated with schizophrenia can involve several genes and the phenotypic syndromes, or the “genomic disorders“, have not yet been characterized. Single nucleotide polymorphism (SNP)-based GWASs with the potential to implicate individual genes in complex diseases may reveal underlying biological pathways. However, the identification of common variants associated with neuropsychiatric traits has not been successful so far. The question emerges whether alternative (genomic) approaches are needed for the identification of the genetic susceptibility of schizophrenia.
Plant Breeding & Genetics
Prof. M. Koornneef1,2 , dr. J. Keurentjes1,3 & dr. M. Reymond2
1 Laboratory of Genetics, Wageningen University NL
2 Max Planck Institute for Plant Breeding Research, Cologne GE
3 Laboratory of Plant Physiology, Wageningen University NL
koornneef@mpiz-koeln.mpg.de - Personal page
TITLE
Quantitative genetics in Arabidopsis: from QTL to QTN
ABSTRACT
A large natural genetic variation is present among Arabidopsis accessions and which is assumed to reflect part of the adaptation of the different accessions to their natural growth environment. The variety of populations developed and their use for mapping quantitative trait loci (QTL) will be discussed. In addition, the feasibility to reveal the gene responsible for the effect of a QTL by a combination of map based-cloning coupled with mutant approaches has now been demonstrated for several traits. For this purpose, the Arabidopsis genome resources represent powerful advantages. The full genome sequence and the resequencing of accessions allows the creation of high density polymorphism maps required and an efficient identification of candidate genes (QTGs) and even candidates nucleotide polymorphisms (QTN) . Up to now, cloned QTLs include those encoding genes for physiological traits such as flowering time, seed dormancy, frost tolerance etc. However, QTL analysis has now also been extended to molecular traits such as metabolites (mQTL), enzyme activities, proteins (pQTL) and gene expression (eQTL). Combining morphological or phenological traits with “omic” traits allows the construction of molecular genetic networks based on the co-regulation by the same genetic factors at different levels (transcription, transduction) and may assist the identification of the gene responsible for the effect of the QTL. The use of this approach will be demonstrated on the elucidation of the genetic regulation of glucosinolates, flowering time and primary metabolism. This integrative strategy allows the development of system biology approaches