High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.
Algorithms ; Cluster Analysis ; Databases, Nucleic Acid ; Genotype ; Humans ; Oligonucleotide Array Sequence Analysis ; statistics & numerical data ; Polymorphism, Single Nucleotide ; Software

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.

This study was conducted to determine if the main components of the somatotropic axis change during the early phase of pregnancy in the maternal blood system and whether differences exist on day 18 after pregnancy recognition by the maternal organism. Blood samples of pregnant heifers (Holstein Friesian; n = 10 after embryo transfer) were obtained on the day of ovulation (day 0), as well as on days 7, 14, 16 and 18 and during pregnant, non-pregnant and negative control cycles. The oncentrations of progesterone (P4), oestrogen, growth hormone (GH), insulin-like growth factor-1 and -2 (IGF1, -2) and IGF-binding protein-2, -3 and -4 (IGFBP2, -3, -4) were measured. The mRNA expressions of growth hormone receptor 1A, IGF1, IGF2, IGFBP2, IGFBP3 and IGFBP4 were detected using RT-qPCR in liver biopsy specimens (day 18). In all groups, total serum IGF1 decreased from day 0 to 16. Notably, IGFBP4 maternal blood concentrations were lower during pregnancy than during non-pregnant cycles and synchronized control cycles. It can be speculated that the lower IGFBP4 in maternal blood may result in an increase of free IGF1 for local action. Further studies regarding IGFBP4 concentration and healthy early pregnancy are warranted.
Axis ; Biopsy ; Embryonic Structures ; Female ; Growth Hormone ; Liver ; Ovulation ; Pregnancy* ; Progesterone ; Receptors, Somatotropin ; RNA, Messenger