The importance of the Gallus gallus (chicken) as a model organism and agricultural animal merits a continuation of sequence assembly improvement efforts. We present a new version of the chicken genome assembly (Gallus_gallus-5.0; GCA_000002315.3), built from combined long single molecule sequencing technology, finished BACs, and improved physical maps. In overall assembled bases, we see a gain of 183 Mb, including 16.4 Mb in placed chromosomes with a corresponding gain in the percentage of intact repeat elements characterized. Of the 1.21 Gb genome, we include three previously missing autosomes, GGA30, 31, and 33, and improve sequence contig length 10-fold over the previous Gallus_gallus-4.0. Despite the significant base representation improvements made, 138 Mb of sequence is not yet located to chromosomes. When annotated for gene content, Gallus_gallus-5.0 shows an increase of 4679 annotated genes (2768 noncoding and 1911 protein-coding) over those in Gallus_gallus-4.0. We also revisited the question of what genes are missing in the avian lineage, as assessed by the highest quality avian genome assembly to date, and found that a large fraction of the original set of missing genes are still absent in sequenced bird species. Finally, our new data support a detailed map of MHC-B, encompassing two segments: one with a highly stable gene copy number and another in which the gene copy number is highly variable. The chicken model has been a critical resource for many other fields of study, and this new reference assembly will substantially further these efforts.

We have developed a panel of 152 whole-genome radiation hybrids by fusing irradiated diploid pig lymphocytes or fibroblasts with recipient hamster permanent cells. The number and size of the porcine chromosome fragments retained in each hybrid clone were checked by fluorescence in situ hybridization with a SINE probe or by primed in situ labeling (PRINS) with SINE-specific primers. A strategy based on the interspersed repetitive sequence polymerase chain reaction (IRS-PCR) was developed for selected clones to determine if the large fragments painted by the SINE probe corresponded to one pig chromosome or to different fragments of several chromosomes. This strategy was buttressed by a double PRINS approach using primers specific for α-satellite sequences of two different groups of swine chromosomes. Genome retention frequency was estimated for each clone by PCR with 32 markers localized on different porcine chromosomes. Of the 152 hybrids produced, 126 were selected on the basis of cytogenetic content and chromosome retention frequency to construct a radiation hybrid map of swine chromosome 8. Our initial results for this chromosome indicate that the resolution of the radiation hybrid map is 18 times higher than that obtained by linkage analysis.

A whole-genome radiation hybrid (WG-RH) panel was used to generate a first-generation radiation map of the porcine (Sus scrofa) genome. Over 900 Type I and II markers were used to amplify the INRA-University of Minnesota porcine Radiation Hybrid panel (IMpRH) comprised of 118 hybrid clones. Average marker retention frequency of 29.3% was calculated with 757 scorable markers. The RHMAP program established 128 linkage groups covering each chromosome (n = 19) at a lod >/= 4.8. Fewer than 10% of the markers (59) could not be placed within any linkage group at a lod score >/=4.8. Linkage group order for each chromosome was determined by incorporating linkage data from the swine genetic map as well as physical assignments. The current map has an estimated ratio of approximately 70 kb/cR and a maximum theoretical resolution of 145 kb. This initial map forms a template for establishing accurate YAC and BAC contigs and eventual positional cloning of genes associated with complex traits.

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.

We describe a systems biology approach for the genetic dissection of complex traits based on applying gene network theory to the results from genome-wide associations. The associations of singlenucleotide polymorphisms (SNP) that were individually associated with a primary phenotype of interest, age at puberty in our study, were explored across 22 related traits. Genomic regions were surveyed for genes harboring the selected SNP. As a result, an association weight matrix (AWM) was constructed with as many rows as genes and as many columns as traits. Each {i, j} cell value in the AWM corresponds to the z-score normalized additive effect of the ith gene (via its neighboring SNP) on the jth trait. Columnwise, the AWM recovered the genetic correlations estimated via pedigree-based restricted maximum-likelihood methods. Rowwise, a combination of hierarchical clustering, gene network, and pathway analyses identified genetic drivers that would have been missed by standard genome-wide association studies. Finally, the promoter regions of the AWM-predicted targets of three key transcription factors (TFs), estrogen-related receptor γ (ESRRG), Pal3 motif, bound by a PPAR-γ homodimer, IR3 sites (PPARG), and Prophet of Pit 1, PROP paired-like homeobox 1 (PROP1), were surveyed to identify binding sites corresponding to those TFs. Applied to our case, the AWM results recapitulate the known biology of puberty, captured experimentally validated binding sites, and identified candidate genes and genegene interactions for further investigation.bovine | complex traits | fertility | reproduction | systems biology T he analysis of genome-wide association studies (GWASs) applied to complex traits remains a challenge (1). Addressing a complex trait by a single, often binary, phenotypic measure is common practice but is limiting. It is not easy to find the right balance between applying a conservative significance threshold that gives rise to a small number of strong and hopefully biologically meaningful associations and applying a relaxed threshold yielding numerous associations, many of which are new but potentially false. In addition, an increase in sample size coupled with a denser chip results in a larger number of associations that, on average, have a much smaller effect (2). Accepting a large number of associations while simultaneously reducing the number of false positives would be ideal. It is reasonable to propose that a holistic approach applied to a relaxed significance threshold could be the solution. Such a strategy would be particularly useful when investigating the genetic basis of complex traits that, by definition, are influenced by numerous genes and pathways.In our study, age at puberty was the complex phenotype considered. Puberty, or the progression to sexual maturity, is a developmental process with genetic drivers conserved among species (3). It is an important phenotype for the beef industry because late puberty has negative effects on reproduction rates and profitability (4). Age at puberty is moderately heritab...

The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI. In addition, correlated traits such as BW, height, serum IGF1 concentration, condition score, and fatness were also examined. In the Brahman and Tropical Composite cattle, 169 [false positive rate (FPR) = 0.262] and 84 (FPR = 0.581) SNP, respectively, were significant (P < 0.001) for AGECL. In Brahman, 41% of these significant markers mapped to a single chromosomal region on BTA14. In Tropical Composites, 16% of these significant markers were located on BTA5. For PPAI, 66 (FPR = 0.67) and 113 (FPR = 0.432) SNP were significant (P < 0.001) in Brahman and Tropical Composite, respectively, whereas for PW, 68 (FPR = 0.64) and 113 (FPR = 0.432) SNP were significant (P < 0.01). In Tropical Composites, the largest concentration of PPAI markers were located on BTA5 [19% (PPAI) and 23% (PW)], and BTA16 [17% (PPAI) and 18% (PW)]. In Brahman cattle, the largest concentration of markers for postpartum anestrus was located on BTA3 (14% for PPAI and PW) and BTA14 (17% PPAI). Very few of the significant markers for female reproduction traits for the Brahman and Tropical Composite breeds were located in the same chromosomal regions. However, fatness and BW traits as well as serum IGF1 concentration were found to be associated with similar genome regions within and between breeds. Clusters of SNP associated with multiple traits were located on BTA14 in Brahman and BTA5 in Tropical Composites.

The INRA-Minnesota Porcine Radiation Hybrid (IMpRH) Server provides both a mapping tool (IMpRH mapping tool) and a database (IMpRH database) of officially submitted results. The mapping tool permits the mapping of a new marker relatively to markers previously mapped on the IMpRH panel. The IMpRH database is the official database for submission of new results and queries. The database not only permits the sharing of public data but also semi-private and private data.

ABSTRACT:A genome wide-association study for production traits in cattle was carried out using genotype data from the 10K Affymetrix (Santa Clara, CA) and the 50K Illumina (San Diego, CA) SNP chips. The results for residual feed intake (RFI), BW, and hip height in 3 beef breed types (Bos indicus, Bos taurus, and B. indicus × B. taurus), and for stature in dairy cattle, are presented. The aims were to discover SNP associated with all traits studied, but especially RFI, and further to test the consistency of SNP effects across different cattle populations and breed types. The data were analyzed within data sets and within breed types by using a mixed model and fitting 1 SNP at a time. In each case, the number of significant SNP was more than expected by chance alone. A total of 75 SNP from the reference population with 50K chip data were significant (P < 0.001) for RFI, with a false discovery rate of 68%. These 75 SNP were mapped on 24 different BTA. Of the 75 SNP, the 9 most significant SNP were detected on BTA 3, 5, 7, and 8, with P ≤ 6.0 × 10 −5 . In a population of Angus cattle divergently selected for high and low RFI and 10K chip data, 111 SNP were significantly (P < 0.001) associated with RFI, with a false discovery rate of 7%. Approximately 103 of these SNP were therefore likely to represent true positives. Because of the small number of SNP common to both the 10K and 50K SNP chips, only 27 SNP were significantly (P < 0.05) associated with RFI in the 2 populations. However, other chromosome regions were found that contained SNP significantly associated with RFI in both data sets, although no SNP within the region showed a consistent effect on RFI. The SNP effects were consistent between data sets only when estimated within the same breed type.