An updated Axiom buffalo genotyping array map and mapping of cattle quantitative trait loci to the new water buffalo reference genome assembly.

The objectives of this study were to provide the buffalo research community with an updated SNP map for the Axiom Buffalo Genotyping (ABG) array with genomic positions for SNP currently unmapped and to map all cattle QTL from the CattleQTLdb onto the buffalo reference assembly. To update the ABG array map, all SNP probe sequences from the ABG array were re-aligned against the UOA_WB_1 assembly. With the new map, the number of mapped markers increased by approximately 10% and went from 106 778 to 116 708, which reduced the average marker spacing by approximately 2 kb. A comparison of results between signatures of autozygosity study using the ABG and the new map showed that, when the additional markers were used there was an increase in the autozygosity peaks and additional peaks in BBU5 and BBU11 could be identified. After sequence alignment and quality control, 64 650 (UMD3.1) and 76 530 (ARS_UCD1.2) cattle QTL were mapped onto the buffalo genome. The mapping of the bovine QTL database onto the buffalo genome should be useful for genome-wide association studies in buffalo and, given the high homology between the two species, the positions of cattle QTL on the buffalo genome can serve as a stepping stone towards a water buffalo QTL database.

Inbreeding coefficients and runs of homozygosity islands in Brazilian water buffalo.

Characterization of autozygosity is relevant to monitor genetic diversity and manage inbreeding levels in breeding programs. Identification of autozygosity hotspots can unravel genomic regions targeted by selection for economically important traits and can help identify candidate genes for selection. In this study, we estimated the inbreeding levels of a Brazilian population of Murrah buffalo undergoing selection for milk production traits, particularly milk yield. We also studied the distribution of runs of homozygosity (ROH) islands and identified putative genes and quantitative trait loci (QTL) under selection. We genotyped 422 Murrah buffalo for 51,611 SNP; 350 of these had ROH longer than 10 Mb, indicating the occurrence of inbreeding in the last 5 generations. The mean length of the ROH per animal was 4.28 ± 1.85 Mb. Inbreeding coefficients were calculated from the genomic relationship matrix, the pedigree, and the ROH, with estimates varying between 0.242 and 0.035. Inbreeding estimates from the pedigree had a low correlation with the genomic estimates, and estimates from the genomic relationship matrix were much higher than those from the pedigree or the ROH. Signatures of selection were identified in 6 genomic regions, located on chromosomes 1, 2, 3, 5, 16, and 18, encompassing a total of 190 genes and 174 QTL. Many of the genes (e.g., APRT and ACSF3) and QTL identified are related to milk production traits, such as milk yield, milk fat yield and percentage, and milk protein yield and percentage. Other genes are associated with reproduction and immune response traits as well as morphological aspects of the buffalo species. Inbreeding levels in this population are still low but are increasing due to selection and should be managed to avoid future losses due to inbreeding depression. The proximity of genes linked to milk production traits with genes associated with reproduction and immune system traits suggests the need to include these latter genes in the breeding program to avoid negatively affecting them due to selection for production traits.

Genomewide association analysis of growth traits in Charolais beef cattle.

The objective of this study was to perform a genomewide association study (GWAS) for growth traits in Charolais beef cattle and to identify SNP markers and genes associated with these traits. Our study included 855 animals genotyped using 76,883 SNP from the GeneSeek Genomic Profiler Bovine HD panel. The examined phenotypic data included birth, weaning, and yearling weights as well as pre- and postweaning ADG. After quality control, 68,337 SNP and 823 animals were retained in the analysis. The association analysis was performed using the principal components method via the egscore function of the GenABEL version 1.8-0 package in the R environment. Eighteen SNP located in 13 BTA were associated with growth traits ( < 5 × 10). The most important genes in these genomic regions were (), (), (), (), and ( [angiotensinase C]), due to their relationships with perinatal and postnatal survival, bone growth, cell adhesion, regulation of adipogenesis, and appetite. In conclusion, this study is the first to describe a GWAS conducted in beef cattle in Mexico and represents a basis for further and future research. This study detected new QTL associated with growth traits and identified 5 positional and functional candidate genes that are potentially involved in variations of the analyzed traits. Future analyses of these regions could help to identify useful markers for marker-assisted selection and will contribute to the knowledge of the genetic basis of growth in cattle and be a foundation for genomic predictions in Mexican Charolais cattle.

A simple genetic algorithm for multiple sequence alignment.

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of 'residues' (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task.

A simple genetic algorithm for multiple sequence alignment

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of ‘residues’ (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task.