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Background: The cattle (Bos taurus) genome was originally selected for sequencing due to its economic importance and unique biology as a model organism for understanding other ruminants, or mammals. Currently, there are two cattle genome sequence assemblies (UMD3.1 and Btau4.6) from groups using dissimilar assembly algorithms, which were complemented by genetic and physical map resources. However, past comparisons between these assemblies revealed substantial differences. Consequently, such discordances have engendered ambiguities when using reference sequence data, impacting genomic studies in cattle and motivating construction of a new optical map resource--BtOM1.0--to guide comparisons and improvements to the current sequence builds. Accordingly, our comprehensive comparisons of BtOM1.0 against the UMD3.1 and Btau4.6 sequence builds tabulate large-to-immediate scale discordances requiring mediation., Results: The optical map, BtOM1.0, spanning the B. taurus genome (Hereford breed, L1 Dominette 01449) was assembled from an optical map dataset consisting of 2,973,315 (439 X; raw dataset size before assembly) single molecule optical maps (Rmaps; 1 Rmap = 1 restriction mapped DNA molecule) generated by the Optical Mapping System. The BamHI map spans 2,575.30 Mb and comprises 78 optical contigs assembled by a combination of iterative (using the reference sequence: UMD3.1) and de novo assembly techniques. BtOM1.0 is a high-resolution physical map featuring an average restriction fragment size of 8.91 Kb. Comparisons of BtOM1.0 vs. UMD3.1, or Btau4.6, revealed that Btau4.6 presented far more discordances (7,463) vs. UMD3.1 (4,754). Overall, we found that Btau4.6 presented almost double the number of discordances than UMD3.1 across most of the 6 categories of sequence vs. map discrepancies, which are: COMPLEX (misassembly), DELs (extraneous sequences), INSs (missing sequences), ITs (Inverted/Translocated sequences), ECs (extra restriction cuts) and MCs (missing restriction cuts)., Conclusion: Alignments of UMD3.1 and Btau4.6 to BtOM1.0 reveal discordances commensurate with previous reports, and affirm the NCBI's current designation of UMD3.1 sequence assembly as the "reference assembly" and the Btau4.6 as the "alternate assembly." The cattle genome optical map, BtOM1.0, when used as a comprehensive and largely independent guide, will greatly assist improvements to existing sequence builds, and later serve as an accurate physical scaffold for studies concerning the comparative genomics of cattle breeds.

The specific genes regulating the quantitative variation in macronutrient preference and food intake are virtually unknown. We fine mapped a previously identified mouse chromosome 17 region harboring quantitative trait loci (QTL) with large effects on preferential macronutrient intake-carbohydrate (Mnic1), total kilcalories (Kcal2), and total food volume (Tfv1) using interval-specific strains. These loci were isolated in the [C57BL/6J.CAST/EiJ-17.1-(D17Mit19-D17Mit50); B6.CAST-17.1] strain, possessing a ∼ 40.1 Mb region of CAST DNA on the B6 genome. In a macronutrient selection paradigm, the B6.CAST-17.1 subcongenic mice eat 30% more calories from the carbohydrate-rich diet, ∼ 10% more total calories, and ∼ 9% more total food volume per body weight. In the current study, a cross between carbohydrate-preferring B6.CAST-17.1 and fat-preferring, inbred B6 mice was used to generate a subcongenic-derived F2 mapping population; genotypes were determined using a high-density, custom SNP panel. Genetic linkage analysis substantially reduced the 95% confidence interval for Mnic1 (encompassing Kcal2 and Tfv1) from 40.1 to 29.5 Mb and more precisely established its boundaries. Notably, no genetic linkage for self-selected fat intake was detected, underscoring the carbohydrate-specific effect of this locus. A second key finding was the separation of two energy balance QTLs: Mnic1/Kcal2/Tfv1 for food intake and a newly discovered locus regulating short term body weight gain. The Mnic1/Kcal2/Tfv1 QTL was further de-limited to 19.0 Mb, based on the absence of nutrient intake phenotypes in subcongenic HQ17IIa mice. Analyses of available sequence data and gene ontologies, along with comprehensive expression profiling in the hypothalamus of non-recombinant, cast/cast and b6/b6 F2 controls, focused our attention on candidates within the QTL interval. Zfp811, Zfp870, and Btnl6 showed differential expression and also contain stop codons, but have no known biology related to food intake regulation. The genes Decr2, Ppard and Agapt1 are more appealing candidates because of their involvement in lipid metabolism and down-regulation in carbohydrate-preferring animals.

We assessed the relationships of polar bears (Ursus maritimus), brown bears (U. arctos), and black bears (U. americanus) with high throughput genomic sequencing data with an average coverage of 25× for each species. A total of 1.4 billion 100-bp paired-end reads were assembled using the polar bear and annotated giant panda (Ailuropoda melanoleuca) genome sequences as references. We identified 13.8 million single nucleotide polymorphisms (SNP) in the 3 species aligned to the polar bear genome. These data indicate that polar bears and brown bears share more SNP with each other than either does with black bears. Concatenation and coalescence-based analysis of consensus sequences of approximately 1 million base pairs of ultraconserved elements in the nuclear genome resulted in a phylogeny with black bears as the sister group to brown and polar bears, and all brown bears are in a separate clade from polar bears. Genotypes for 162 SNP loci of 336 bears from Alaska and Montana showed that the species are genetically differentiated and there is geographic population structure of brown and black bears but not polar bears.

HG.CAST-(D9Mit249-D9Mit133) (HG9) congenic mice are homozygous for CAST/EiJ chromosome (Chr) 9 alleles from approximately 9 to 84 Mbp on a C57BL6/J-hg/hg (HG) background. This region contains the carcass fat in high growth mice (Carfhg2) quantitative trait locus (QTL), and while its obesity-promoting effects have been confirmed in HG9 mice, its underlying genetic basis remains elusive. To refine the location of Carfhg2, we preformed a linkage analysis in two congenic F2 intercrosses and progeny-tested a recombinant F2 male. These analyses narrowed Carfhg2 to between 33.0 and 40.8 Mbp on Chr 9. To identify candidate genes we measured the expression of 44 transcripts surrounding the Carfhg2 peak in adipose, brain, liver, and muscle tissues from F2 mice using Biomark 48.48 Dynamic Arrays. In total, 68% (30 of the 44) of genes were regulated by a significant expression QTL (eQTL) in at least one tissue. To prioritize genes with eQTL we used Network Edge Orienting, a causality modeling tool. These analyses advance our goal of identifying the molecular basis of Carfhg2.

Background: Many studies have found segregating quantitative trait loci (QTL) for milk production traits in different dairy cattle populations. However, even for relatively large effects with a saturated marker map the confidence interval for QTL location by linkage analysis spans tens of map units, or hundreds of genes. Combining mapping and arraying has been suggested as an approach to identify candidate genes. Thus, gene expression analysis in the mammary gland of genes positioned in the confidence interval of the QTL can bridge the gap between fine mapping and quantitative trait nucleotide (QTN) determination., Results: We hybridized Affymetrix microarray (MG-U74v2), containing 12,488 murine probes, with RNA derived from mammary gland of virgin, pregnant, lactating and involuting C57BL/6J mice in a total of nine biological replicates. We combined microarray data from two additional studies that used the same design in mice with a total of 75 biological replicates. The same filtering and normalization was applied to each microarray data using GeneSpring software. Analysis of variance identified 249 differentially expressed probe sets common to the three experiments along the four developmental stages of puberty, pregnancy, lactation and involution. 212 genes were assigned to their bovine map positions through comparative mapping, and thus form a list of candidate genes for previously identified QTLs for milk production traits. A total of 82 of the genes showed mammary gland-specific expression with at least 3-fold expression over the median representing all tissues tested in GeneAtlas., Conclusion: This work presents a web tool for candidate genes for QTL (cgQTL) that allows navigation between the map of bovine milk production QTL, potential candidate genes and their level of expression in mammary gland arrays and in GeneAtlas. Three out of four confirmed genes that affect QTL in livestock (ABCG2, DGAT1, GDF8, IGF2) were over expressed in the target organ. Thus, cgQTL can be used to determine priority of candidate genes for QTN analysis based on differential expression in the target organ.

Previous speed congenic analysis has suggested that the expression of growth and obesity quantitative trait loci (QTL) on distal mouse chromosomes (MMU) 2 and 11, segregating between the CAST/EiJ (CAST) and C57BL/6J-hg/hg (HG) strains, is dependent on sex. To confirm, fine map, and further evaluate QTL x sex interactions, we constructed congenic by recipient F2 crosses for the HG.CAST-(D2Mit329-D2Mit457)N(6) (HG2D) and HG.CAST-(D11Mit260-D11Mit255)N(6) (HG11) congenic strains. Over 700 F2 mice were densely genotyped and phenotyped for a panel of 40 body and organ weight, skeletal length, and obesity-related traits at 9 weeks of age. Linkage analysis revealed 20 QTL affecting a representative subset of phenotypes in HG2DF2 and HG11F2 mice. The effect of sex was quantified by comparing two linear models: the first model included sex as an additive covariate and the second incorporated sex as an additive and an interactive covariate. Of the 20 QTL, 8 were sex biased, sex specific, or sex antagonistic. Most traits were regulated by single QTL; however, two closely linked loci were identified for five traits in HG2DF2 mice. Additionally, the confidence intervals for most QTL were significantly reduced relative to the original mapping results, setting the stage for quantitative trait gene (QTG) discovery. These results highlight the importance of assessing the contribution of sex in complex trait analyses.

The high growth (hg) mutation increases body size in mice by 30-50%. Given the complexity of the genetic regulation of animal growth, it is likely that the effect of this major locus is mediated by other quantitative trait loci (QTL) with smaller effects within a web of gene interactions. In this article, we extend our functional mapping model to characterize modifier QTL that interact with the hg locus during ontogenetic growth. Our model is derived within the maximum-likelihood context, incorporated by mathematical aspects of growth laws and implemented with the EM algorithm. In an F2 population founded by a congenic high growth (HG) line and non-HG line, a highly additive effect due to the hg gene was detected on growth trajectories. Three QTL located on chromosomes 2 and X were identified to trigger significant additive and/or dominant effects on the process of growth. The most significant finding made from our model is that these QTL interact with the hg locus to affect the shapes of the growth process. Our model provides a powerful means for understanding the genetic architecture and regulation of growth rate and body size in mammals.

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

In this study, an in silico approach was developed to identify homologies existing between livestock microsatellite flanking sequences and GenBank nucleotide sequences. Initially, 1955 bovine, 1570 porcine and 1121 chicken microsatellites were downloaded and the flanking sequences were compared with the nr and dbEST databases of GenBank. A total of 74 bovine, 44 porcine and 37 chicken microsatellite flanking sequences passed our criteria and had at least one significant match to human genomic sequence, genes/expressed sequence tags (ESTs) or both. GenBank annotation and BLAT searches of the UCSC human genome assembly revealed that 38 bovine, 13 porcine and 17 chicken microsatellite flanking sequences were highly similar to known human genes. Map locations were available for 67 bovine, 44 porcine and 21 chicken microsatellite flanking sequences, providing useful links in the comparative maps of humans and livestock. In support of our approach, 112 alignments with both microsatellite and match mapping information were located in the expected chromosomal regions based on previously reported syntenic relationships. The development of this in silico mapping approach has significantly increased the number of genes and EST sequences anchored to the bovine, porcine and chicken genome maps and the number of links in various human-livestock comparative maps.

Lines of mice selected for many generations for high or low growth in several laboratories around the world have been collected, and from these, inbred lines are being developed by recurrent full-sib mating in Edinburgh. There are seven high selected lines and four low lines (each low line is from the same base population as one of the high lines), and the histories of each are summarized. Mean body weight of males at 70 days of age in the Edinburgh laboratory in the heaviest inbred line (77 g) is 4.8-fold higher than in the lightest line (16 g), and 1.9-fold higher than in the least extreme high line (41 g). Litter size, food intake, and fat content also differ substantially. These inbred extreme selected lines are a uniquely valuable resource for QTL or gene mapping, candidate gene identification, and elucidation of epistatic effects.

A medium-density linkage map of the ovine genome has been developed. Marker data for 550 new loci were generated and merged with the previous sheep linkage map. The new map comprises 1093 markers representing 1062 unique loci (941 anonymous loci, 121 genes) and spans 3500 cM (sex-averaged) for the autosomes and 132 cM (female) on the X chromosome. There is an average spacing of 3.4 cM between autosomal loci and 8.3 cM between highly polymorphic [polymorphic information content (PIC) > or = 0.7] autosomal loci. The largest gap between markers is 32.5 cM, and the number of gaps of > 20 cM between loci, or regions where loci are missing from chromosome ends, has been reduced from 40 in the previous map to 6. Five hundred and seventy-three of the loci can be ordered on a framework map with odds of > 1000 : 1. The sheep linkage map contains strong links to both the cattle and goat maps. Five hundred and seventy-two of the loci positioned on the sheep linkage map have also been mapped by linkage analysis in cattle, and 209 of the loci mapped on the sheep linkage map have also been placed on the goat linkage map. Inspection of ruminant linkage maps indicates that the genomic coverage by the current sheep linkage map is comparable to that of the available cattle maps. The sheep map provides a valuable resource to the international sheep, cattle, and goat gene mapping community.

The attainment of a specific mature body size is one of the most fundamental differences among species of mammals. Moreover, body size seems to be the central factor underlying differences in traits such as growth rate, energy metabolism and body composition. An important proportion of this variability is of genetic origin. The goal of the genetic analysis of animal growth is to understand its "genetic architecture", that is the number and position of loci affecting the trait, the magnitude of their effects, allele frequencies and types of gene action. In this review, the different strategies developed to identify and characterize genes involved in the regulation of growth in the mouse are described, with emphasis on the methods developed to map loci contributing to the regulation of quantitative traits (QTLs).

The high growth (hg) locus in mice produces a 30-50% increase in weight gain of homozygous individuals. Here we report that the microsatellite marker D10Mit69 is deleted in high growth mice. The deletion of D10Mit69 was uncovered in a screen of the high growth mouse and its progenitor strains for available markers in the hg region. We demonstrate that hg and D10Mit69 cosegregate in a cross of congenic strains C57BL/6J-hghg x C57BL/6J. These results suggest that deletion of a region around D10Mit69 is responsible for the high growth effect. Marker D10Mit69 will be utilized as an entry point to physical cloning of the hg-containing segment. A dense map of markers around hg constructed here should allow identification of markers in homologous regions in domestic animals and humans, which may be utilized to assess the role of the hg locus in these other species.

The random amplified polymorphic DNA (RAPD) assay utilizes the polymerase chain reaction (PCR) and short primers of arbitrary nucleotide sequence to amplify DNA. In this study, the RAPD assay was used to identify and map polymorphic markers in the AgResearch International Mapping Flock (IMF) sheep pedigrees. Sires and dams of eight of the full-sib IMF pedigrees were screened with 131 different 10-mer oligonucleotide primers. An average of 85 RAPD polymorphisms was identified between each parental pair, and 53 markers were contributed to the AgResearch IMF collaboration. Forty-five of the RAPD markers were mapped in the AgResearch IMF genetic linkage map, and at least one marker was located on 17 of the 26 autosomes and both sex chromosomes. Three lines of evidence were used to check for the homology of scored polymorphisms in different pedigrees, pedigree evaluation, segregation analysis, and Southern blot analysis. These results demonstrate that the RAPD assay is a powerful approach for identifying polymorphisms that can be used as markers for constructing a sheep genetic linkage map.

The high growth locus (hg) causes a major increase in weight gain and body size in mice. As a first step to map-based cloning of hg, we developed a genetic map of the hg-containing region using interval mapping of 403 F2 from a C57BL/6J-hghg x CAST/EiJ cross. The maximum likelihood position of hg was at the chromosome 10 marker D10Mit41 (LOD = 24.8) in the F2 females and 1.5 cM distal to D10Mit41 (LOD = 9.56) in the F2 males with corresponding LOD 2 support intervals of 3.7 and 5.4 cM, respectively. The peak LOD scores were significantly higher than the estimated empirical threshold LOD values. The localization of hg by interval mapping was supported by a test cross of F2 mice recombinant between the LOD 2 support interval and the flanking marker. The interval mapping and test-cross results indicate that hg is not allelic with candidate genes Igf1 or decorin (Dcn), a gene that was mapped close to hg in this study. The hg inheritance was recessive in females, although we could not reject recessive or additive inheritance in males. Possible causes for sex differences in peak LOD scores and for the distortion of transmission ratios observed in F2 males are discussed. The genetic map of the hg region will facilitate further fine mapping and cloning of hg, and allow searches for a homologous quantitative trait locus affecting growth in humans and domestic animals.