Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle.

BACKGROUND: To better understand the genetic determination of udder health, we performed a genome-wide association study (GWAS) on a population of 2354 German Holstein bulls for which daughter yield deviations (DYD) for somatic cell score (SCS) were available. For this study, we used genetic information of 44 576 informative single nucleotide polymorphisms (SNPs) and 11 725 inferred haplotype blocks. RESULTS: When accounting for the sub-structure of the analyzed population, 16 SNPs and 10 haplotypes in six genomic regions were significant at the Bonferroni threshold of P ≤ 1.14 × 10-6. The size of the identified regions ranged from 0.05 to 5.62 Mb. Genomic regions on chromosomes 5, 6, 18 and 19 coincided with known QTL affecting SCS, while additional genomic regions were found on chromosomes 13 and X. Of particular interest is the region on chromosome 6 between 85 and 88 Mb, where QTL for mastitis traits and significant SNPs for SCS in different Holstein populations coincide with our results. In all identified regions, except for the region on chromosome X, significant SNPs were present in significant haplotypes. The minor alleles of identified SNPs on chromosomes 18 and 19, and the major alleles of SNPs on chromosomes 6 and X were favorable for a lower SCS. Differences in somatic cell count (SCC) between alternative SNP alleles reached 14 000 cells/mL. CONCLUSIONS: The results support the polygenic nature of the genetic determination of SCS, confirm the importance of previously reported QTL, and provide evidence for the segregation of additional QTL for SCS in Holstein cattle. The small size of the regions identified here will facilitate the search for causal genetic variations that affect gene functions.

Short communication: validation of somatic cell score-associated loci identified in a genome-wide association study in German Holstein cattle.

Recently, we identified 6 genomic loci affecting daughter yield deviations (DYD) for somatic cell score (SCS) in a genome-wide association study (GWAS) performed with German Holstein bulls. In the current study, we tested if these loci were associated with SCS in cows using their own performance data. The study was performed with 1,412 German Holstein cows, of which 483 were daughters of 71 bulls that had been used in the GWAS. We tested 10 single nucleotide polymorphisms (SNP) representing 6 genomic regions that were associated with DYD for SCS in bulls. All tested SNP were significant in cows. Seven of them, located on Bos taurus autosomes (BTA) 6, 13, and 19, had the same direction of effect as those previously reported in the bull population. The most significant associations were detected on BTA6 and BTA19, accounting for 1.8% of the total genetic variance. The major allele of the 2 SNP on BTA6 and the minor allele of the 2 SNP on BTA19 were favorable for lower SCS. The differences between the homozygous genotype classes were up to 15,000 cells/mL. The verification of SNP associated with SCS in this study provides further evidence for the functional role of the linked genomic regions for immune response and contributes to identification of causative mutations. In particular, SNP with minor frequency of the favorable allele possess high potential to reduce SCS in German Holstein cattle by selection.

Alternative splicing of mutually exclusive exons--a review.

Alternative splicing (AS) of pre-mRNAs in higher eukaryotes and several viruses is one major source of protein diversity. Usually, the following major subtypes of AS are distinguished: exon skipping, intron retention, and alternative 3' and 5' splice sites. Moreover, mutually exclusive exons (MXEs) represent a rare subtype. In the splicing of MXEs, two (or more) splicing events are not independent anymore, but are executed or disabled in a coordinated manner. In this review, several bioinformatics approaches for analyzing MXEs are presented and discussed. In particular, we revisit suitable definitions and nomenclatures, and bioinformatics tools for finding MXEs, adjacent and non-adjacent MXEs, clustered and grouped MXEs. Moreover, the molecular mechanisms for splicing MXEs proposed in the literature are reviewed and discussed.

Impact of variation at the FTO locus on milk fat yield in Holstein dairy cattle.

This study explores the biological role of the Fat Mass and Obesity associated (FTO) gene locus on milk composition in German Holstein cattle. Since FTO controls energy homeostasis and expenditure and the FTO locus has repeatedly shown association with obesity in human studies, we tested FTO as a candidate gene in particular for milk fat yield, which represents a high amount of energy secreted during lactation. The study was performed on 2,402 bulls and 860 cows where dense milk composition data were available. Genetic information was taken from a 2 Mb region around FTO. Five SNPs and two haplotype blocks in a 725 kb region covering FTO and the neighboring genes RPGRIP1L, U6ATAC, and 5 S rRNA were associated with milk fat yield and also affected protein yield in the same direction. Interestingly, higher frequency SNP alleles and haplotypes within the FTO gene increased milk fat and protein yields by up to 2.8 and 2.2 kg per lactation, respectively, while the most frequent haplotype in the upstream block covering exon 1 of FTO to exon 15 of RPGRIP1L had opposite effects with lower fat and milk yield. Both haplotype blocks were also significant in cows. The loci accounted for about 1% of the corresponding trait variance in the population. The association signals not only provided evidence for at least two causative mutations in the FTO locus with a functional effect on milk but also milk protein yield. The pleiotropic effects suggest a biological function on the usage of energy resources and the control of energy balance rather than directly affecting fat and protein synthesis. The identified effect of the obesity gene locus on milk energy content suggests an impact on infant nutrition by breast feeding in humans.

Genetic effects and correlations between production and fertility traits and their dependency on the lactation-stage in Holstein Friesians.

BACKGROUND: This study focused on the dynamics of genome-wide effects on five milk production and eight fertility traits as well as genetic correlations between the traits. For 2,405 Holstein Friesian bulls, estimated breeding values (EBVs) were used. The production traits were additionally assessed in 10-day intervals over the first 60 lactation days, as this stage is physiologically the most crucial time in milk production. RESULTS: SNPs significantly affecting the EBVs of the production traits could be separated into three groups according to the development of the size of allele effects over time: 1) increasing effects for all traits; 2) decreasing effects for all traits; and 3) increasing effects for all traits except fat yield. Most of the significant markers were found within 22 haplotypes spanning on average 135,338 bp. The DGAT1 region showed high density of significant markers, and thus, haplotype blocks. Further functional candidate genes are proposed for haplotype blocks of significant SNPs (KLHL8, SICLEC12, AGPAT6 and NID1). Negative genetic correlations were found between yield and fertility traits, whilst content traits showed positive correlations with some fertility traits. Genetic correlations became stronger with progressing lactation. When correlations were estimated within genotype classes, correlations were on average 0.1 units weaker between production and fertility traits when the yield increasing allele was present in the genotype. CONCLUSIONS: This study provides insight into the expression of genetic effects during early lactation and suggests possible biological explanations for the presented time-dependent effects. Even though only three markers were found with effects on fertility, the direction of genetic correlations within genotype classes between production and fertility traits suggests that alleles increasing the milk production do not affect fertility in a more negative way compared to the decreasing allele.

NovelSNPer: A Fast Tool for the Identification and Characterization of Novel SNPs and InDels.

Typically, next-generation resequencing projects produce large lists of variants. NovelSNPer is a software tool that permits fast and efficient processing of such output lists. In a first step, NovelSNPer determines if a variant represents a known variant or a previously unknown variant. In a second step, each variant is classified into one of 15 SNP classes or 19 InDel classes. Beside the classes used by Ensembl, we introduce POTENTIAL_START_GAINED and START_LOST as new functional classes and present a classification scheme for InDels. NovelSNPer is based upon the gene structure information stored in Ensembl. It processes two million SNPs in six hours. The tool can be used online or downloaded.

Exhaustive analysis of the modular structure of the spliceosomal assembly network: a petri net approach.

Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier. Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant computation leads to pathways that have previously not been described as connected routes, although their constituents were known. T-clusters divide the network into modules, which we interpret as building blocks in spliceosome maturation. We conclude that Petri net representations of large biological networks and system invariants, are well-suited as a means for validating the integration of experimental knowledge into a consistent model. Based on this network model, the design of further experiments is facilitated.

Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located?

Recent technological progress has permitted the efficient performance of genome-wide association studies (GWAS) to map genetic variants associated with common diseases. Here, we analyzed 2,893 single nucleotide polymorphisms (SNPs) that have been identified in 593 published GWAS as associated with a disease phenotype with respect to their genomic location. In absolute numbers, most significant SNPs are located in intergenic regions and introns. When compared to their representation on the chips, there is essentially overrepresentation of nonsynonymous coding SNPs (nsSNPs), synonymous coding SNPs, and SNPs in untranscribed regions upstream of genes among the disease associated SNPs. A Gene Ontology term analysis showed that genes putatively causing a phenotype often code for membrane associated proteins or signal transduction genes.

BDNF contributes to the genetic variance of milk fat yield in german holstein cattle.

The gene encoding the brain-derived neurotrophic factor (BDNF) has been repeatedly associated with human obesity. As such, it could also contribute to the regulation of energy partitioning and the amount of secreted milk fat during lactation, which plays an important role in milk production in dairy cattle. Therefore, we performed an association study using estimated breeding values (EBVs) of bulls and yield deviations of German Holstein dairy cattle to test the effect of BDNF on milk fat yield (FY). A highly significant effect (corrected p-value = 3.362 × 10(-4)) was identified for an SNP 168 kb up-stream of the BDNF transcription start. The association tests provided evidence for an additive allele effect of 5.13 kg of fat per lactation on the EBV for milk FY in bulls and 6.80 kg of fat of the own production performance in cows explaining 1.72 and 0.60% of the phenotypic variance in the analyzed populations, respectively. The analyses of bulls and cows consistently showed three haplotype groups that differed significantly from each other, suggesting at least two different mutations in the BDNF region affecting the milk FY. The FY increasing alleles also had low but significant positive effects on protein and total milk yield which suggests a general role of the BDNF region in energy partitioning, rather than a specific regulation of fat synthesis. The results obtained in dairy cattle suggest similar effects of BDNF on milk composition in other species, including man.

CandiSNPer: a web tool for the identification of candidate SNPs for causal variants.

SUMMARY: Human single nucleotide polymorphism (SNP) chips which are used in genome-wide association studies (GWAS) permit the genotyping of up to 4 million SNPs simultaneously. To date, about 1000 human SNPs have been identified as statistically significantly associated with a disease or another trait of interest. The identified SNP is not necessarily the causal variant, but it is rather in linkage disequilibrium (LD) with it. CandiSNPer is a software tool that determines the LD region around a significant SNP from a GWAS. It provides a list with functional annotation and LD values for the SNPs found in the LD region. This list contains not only the SNPs for which genotyping data are available, but all SNPs with rs-IDs, thus increasing the likelihood to include the causal variant. Furthermore, plots showing the LD values are generated. CandiSNPer facilitates the preselection of candidate SNPs for causal variants. AVAILABILITY AND IMPLEMENTATION: The CandiSNPer server is freely available at http://www2.hu-berlin.de/wikizbnutztier/software/CandiSNPer. The source code is available to academic users 'as is' upon request. The web site is implemented in Perl and R and runs on an Apache server. The Ensembl database is queried for SNP data via Perl APIs.

Tracking chromosomal positions of oligomers - a case study with Illumina's BovineSNP50 beadchip.

BACKGROUND: High density genotyping arrays have become established as a valuable research tool in human genetics. Currently, more than 300 genome wide association studies were published for human reporting about 1,000 SNPs that are associated with a phenotype. Also in animal sciences high density genotyping arrays are harnessed to analyse genetic variation. To exploit the full potential of this technology single nucleotide polymorphisms (SNPs) on the chips should be well characterized and their chromosomal position should be precisely known. This, however, is a challenge if the genome sequence is still subject to changes. RESULTS: We have developed a mapping strategy and a suite of software scripts to update the chromosomal positions of oligomer sequences used for SNP genotyping on high density arrays. We describe the mapping procedure in detail so that scientists with moderate bioinformatics skills can reproduce it. We furthermore present a case study in which we re-mapped 54,001 oligomer sequences from Ilumina's BovineSNP50 beadchip to the bovine genome sequence. We found in 992 cases substantial discrepancies between the manufacturer's annotations and our results. The software scripts in the Perl and R programming languages are provided as supplements. CONCLUSIONS: The positions of oligomer sequences in the genome are volatile even within one build of the genome. To facilitate the analysis of data from a GWAS or from an expression study, especially with species whose genome assembly is still unstable, it is recommended to update the oligomer positions before data analysis.

Exhaustive analysis of the modular structure of the spliceosomal assembly network: a Petri net approach.

Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier.Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant computation leads to pathways that have previously not been described as connected routes, although their constituents were known. T-clusters divide the network into modules, which we interpret as building blocks in spliceosome maturation. We conclude that Petri net representations of large biological networks and system invariants, are well-suited as a means for validating the integration of experimental knowledge into a consistent model. Based on this network model, the design of further experiments is facilitated.

EASED: Extended Alternatively Spliced EST Database.

We established a database of alternative splice forms (ASforms) for nine eukaryotic organisms. ASforms are defined by comparing high-scoring ESTs with mRNA sequences using BLAST, taking known exon-intron information (from the Ensembl database). Filtering programs compare the ends of each aligned sequence pair for deletions or insertions in the EST sequence, which indicate the existence of alternative splice forms with respect to the exon-intron boundaries. Moreover, we defined the alternative splice profile of each human sequence. It indicates the number of alternatively spliced ESTs (NAE), the number of constitutively spliced ESTs (NCE) as well as the number of alternative splice sites (NSS) per mRNA. NAE and NCE correspond to the EST coverage and can be used as a quality indicator for the predicted alternative splice variants. The NSS value specifies the splice propensity of a gene. Additionally, the tissue type information of all ESTs was included. This allows (i) restriction of the search to certain tissues and (ii) calculation of the tissue-NAEs, tissue-NCEs and tissue-NSS. These scores are suitable for the estimation of tissue specificity of certain ASforms. Furthermore, the developmental stage and disease information of the ESTs is available. EASED is accessible at http://eased.bioinf.mdc-berlin.de/.