Associated regions for multiple birth in Brown Swiss and Original Braunvieh cattle on chromosomes 15 and 11.

Twin and multiple births have negative effects on the performance and health of cows and calves. To decipher the genetic architecture of this trait in the two Swiss Brown Swiss cattle populations, we performed various association analyses based on de-regressed breeding values. Genome-wide association analyses were executed using ~600 K imputed SNPs for the maternal multiple birth trait in ~3500 Original Braunvieh and ~7800 Brown Swiss animals. Significantly associated QTL were observed on different chromosomes for both breeds. We have identified on chromosome 11 a QTL that explains ~6% of the total genetic variance of the maternal multiple birth trait in Original Braunvieh. For the Brown Swiss breed, we have discovered a QTL on chromosome 15 that accounts for ~4% of the total genetic variance. For Original Braunvieh, subsequent haplotype analysis revealed a 90-kb window on chromosome 11 at 88 Mb, where a likely regulatory region is located close to the ID2 gene. In Brown Swiss, a 130-kb window at 75 Mb on chromosome 15 was identified. Analysis of whole-genome sequence data using linkage-disequilibrium estimation revealed possible causal variants for the identified QTL. A presumably regulatory variant in the non-coding 5' region of the ID2 gene was strongly associated with the haplotype for Original Braunvieh. In Brown Swiss, an intron variant in PRDM11, one 3' UTR variant in SYT13 and three intergenic variants 5' upstream of SYT13 were identified as candidate variants for the trait multiple birth maternal. In this study, we report for the first time QTL for the trait of multiple births in Original Braunvieh and Brown Swiss cattle. Moreover, our findings are another step towards a better understanding of the complex genetic architecture of this polygenic trait.

A major QTL at the LHCGR/FSHR locus for multiple birth in Holstein cattle.

BACKGROUND: Twin and multiple births are rare in cattle and have a negative impact on the performance and health of cows and calves. Therefore, selection against multiple birth would be desirable in dairy cattle breeds such as Holstein. We applied different methods to decipher the genetic architecture of this trait using de-regressed breeding values for maternal multiple birth of ~ 2500 Holstein individuals to perform genome-wide association analyses using ~ 600 K imputed single nucleotide polymorphisms (SNPs). RESULTS: In the population studied, we found no significant genetic trend over time of the estimated breeding values for multiple birth, which indicates that this trait has not been selected for in the past. In addition to several suggestive non-significant quantitative trait loci (QTL) on different chromosomes, we identified a major QTL on chromosome 11 for maternal multiple birth that explains ~ 16% of the total genetic variance. Using a haplotype-based approach, this QTL was fine-mapped to a 70-kb window on chromosome 11 between 31.00 and 31.07 Mb that harbors two functional candidate genes (LHCGR and FSHR). Analysis of whole-genome sequence data by linkage-disequilibrium estimation revealed a regulatory variant in the 5'-region of LHCGR as a possible candidate causal variant for the identified major QTL. Furthermore, the identified haplotype showed significant effects on stillbirth and days to first service. CONCLUSIONS: QTL detection and subsequent identification of causal variants in livestock species remain challenging in spite of the availability of large-scale genotype and phenotype data. Here, we report for the first time a major QTL for multiple birth in Holstein cattle and provide evidence for a linked variant in the non-coding region of a functional candidate gene. This discovery, which is a first step towards the understanding of the genetic architecture of this polygenic trait, opens the path for future selection against this undesirable trait, and thus contributes to increased animal health and welfare.

A multilevel gamma-clustering layout algorithm for visualization of biological networks.

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ -clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.

A role for codon order in translation dynamics.

The genetic code is degenerate. Each amino acid is encoded by up to six synonymous codons; the choice between these codons influences gene expression. Here, we show that in coding sequences, once a particular codon has been used, subsequent occurrences of the same amino acid do not use codons randomly, but favor codons that use the same tRNA. The effect is pronounced in rapidly induced genes, involves both frequent and rare codons and diminishes only slowly as a function of the distance between subsequent synonymous codons. Furthermore, we found that in S. cerevisiae codon correlation accelerates translation relative to the translation of synonymous yet anticorrelated sequences. The data suggest that tRNA diffusion away from the ribosome is slower than translation, and that some tRNA channeling takes place at the ribosome. They also establish that the dynamics of translation leave a significant signature at the level of the genome.

Genome scan for loci regulating HDL cholesterol levels in Finnish extended pedigrees with early coronary heart disease.

Coronary heart disease (CHD) is the leading cause of mortality in Western societies. Its risk is inversely correlated with plasma high-density lipoprotein cholesterol (HDL-C) levels, and approximately 50% of the variability in these levels is genetically determined. In this study, the aim was to carry out a whole-genome scan for the loci regulating plasma HDL-C levels in 35 well-defined Finnish extended pedigrees (375 members genotyped) with probands having low HDL-C levels and premature CHD. The additive genetic heritability of HDL-C was 43%. A variance component analysis revealed four suggestive quantitative trait loci (QTLs) for HDL-C levels, with the highest LOD score, 3.1, at the chromosomal locus 4p12. Other suggestive LOD scores were 2.1 at 2q33, 2.1 at 6p24 and 2.0 at 17q25. Three suggestive loci for the qualitative low HDL-C trait were found, with a nonparametric multipoint score of 2.6 at the chromosomal locus 10p15.3, 2.5 at 22q11 and 2.1 at 6p12. After correction for statin use, the strongest evidence of linkage was shown on chromosomes 4p12, 6p24, 6p12, 15q22 and 22q11. To search for the underlying gene on chromosome 6, we analyzed two functional and positional candidate genes (peroxisome proliferator-activated receptor-delta (PPARD), and retinoid X receptor beta, (RXRB)), but found no significant evidence of association. In conclusion, we identified seven chromosomal regions for HDL-C regulation exceeding the level for suggestive evidence of linkage.

Prediction of transcription factor binding sites using genetical genomics methods.

In this paper, we wanted to test whether it is possible to use genetical genomics information such as expression quantitative trait loci (eQTL) mapping results as input to a transcription factor binding site (TFBS) prediction algorithm. Furthermore, this new approach was compared to the more traditional cluster based TFBS prediction. The results of eQTL mapping are used as input to one of the top ranking TFBS prediction algorithms. Genes with observed expression profiles showing the same eQTL region are collected into eQTL groups. The promoter sequences of all the genes within the same eQTL group are used as input in the transcription factor binding site search. This approach is tested with a real data set of a recombinant inbred line population of Arabidopsis thaliana. The predicted motifs are compared to results obtained from the conventional approach of first clustering the gene expression values and then using the promoter sequences of the genes within the same cluster as input for the transcription factor binding site prediction. Our eQTL based approach produced different motifs compared to the cluster based method. Furthermore the score of the eQTL based motifs was higher than the score of the cluster based motifs. In a comparison to already predicted motifs from the AtcisDB database, the eQTL based and the cluster based method produced about the same number of hits with binding sites from AtcisDB. In conclusion, the results of this study clearly demonstrate the usefulness of eQTL to predict transcription factor binding sites.

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding.

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed "expression assisted selection," and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed "expression-assisted evaluation."

Scoring functions for transcription factor binding site prediction.

BACKGROUND: Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations. RESULTS: Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study. CONCLUSION: LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.

Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana.

We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

Limitations of codon adaptation index and other coding DNA-based features for prediction of protein expression in Saccharomyces cerevisiae.

The relationship between codon usage and protein/mRNA expression in S. cerevisiae has been extensively studied. Recently, protein expression data for the whole yeast genome was published. We investigate which properties of coding DNA sequences can be used to predict expression levels. The new algorithm by Carbone et al. for computing dominating codon bias in a genome is evaluated. It is concluded that it works at least as well as existing methods, and eliminates the need to arbitrarily choose a set of highly expressed genes. Also, the hypothesis that information on codon pair frequencies can be used to predict expression is investigated. Our conclusion is that codon pairs do not contribute more information than do single codon frequencies. Overall correlation between predicted and actual expression data using properties of coding DNA sequences is around 0.65. Hence, while being a useful source of information, the expression levels predicted by these methods should only be used as a rule of thumb.

ATP-binding cassette transporter A1 locus is not a major determinant of HDL-C levels in a population at high risk for coronary heart disease.

ATP-binding cassette transporter A1 (ABCA1) transports cellular cholesterol to lipid-poor apolipoproteins. Mutations in the ABCA1 gene are linked to rare phenotypes, familial hypoalphalipoproteinemia (FHA) and Tangier disease (TD), characterized by markedly decreased plasma high-density lipoprotein cholesterol (HDL-C) levels. The aim was to test if the ABCA1 locus is a major locus regulating HDL-C levels in the homogenous Finnish population with a high prevalence of coronary heart disease (CHD). Firstly, the ABCA1 locus was tested for linkage to HDL-C levels in 35 families with premature CHD and low HDL-C levels. Secondly, 62 men with low HDL-C levels and CHD were screened for the five mutations known to cause FHA. Thirdly, polymorphisms of the ABCA1 gene were tested for an association with HDL-C levels in a population sample of 515 subjects. The ABCA1 locus was not linked to HDL-C levels in the CHD families, and no carriers of the FHA mutations were found. The AA596 genotype was associated with higher HDL-C levels compared with the GG and GA genotypes in the women, but not in the men. The G596A genotypes explained 4% and the A2589G genotypes 3% of the variation in plasma HDL-C levels in women. The data suggest that the ABCA1 locus is of minor importance in the regulation of HDL-C in Finns.

Bayesian QTL mapping using skewed Student-t distributions.

In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.