Development of two major resources for pea genomics: the GenoPea 13.2K SNP Array and a high density, high resolution consensus genetic map.
Plant J. 2015 Nov 21;
Authors: Tayeh N, Aluome C, Falque M, Jacquin F, Klein A, Chauveau A, Bérard A, Houtin H, Rond C, Kreplak J, Boucherot K, Martin C, Baranger A, Pilet-Nayel ML, Warkentin TD, Brunel D, Marget P, Le Paslier MC, Aubert G, Burstin J
SNP arrays represent important genotyping tools for innovative strategies in both basic research and applied breeding. Pea is an important food, feed, and sustainable crop with a large (ca. 4.45 Gb) and not yet available genome sequence. In the present study, 12 pea recombinant inbred line populations were genotyped using the newly-developed GenoPea 13.2K SNP Array. Individual and consensus genetic maps were built and explored providing insights into the pea genome structure and organization. Largely collinear 3,918 to 8,503-SNP genetic maps were obtained from all mapping populations and only two of these exhibited putative chromosomal rearrangement signatures. Similar distortion patterns in different populations were noted. Twelve thousand eight hundred two transcript-derived SNP markers placed on a 15,079-marker high density, high resolution consensus map allowed the identification of ohnolog-rich regions within the pea genome and the localization of local duplicates. Dense syntenic networks with sequenced legume genomes were further uncovered and established paving the way for the identification of the molecular bases of important agronomic traits segregating in the mapping populations. The information gained on the genome structure and organization from this research will undoubtedly contribute to the understanding of the genome evolution and to its assembly. The GenoPea 13.2K SNP Array and individual and consensus genetic maps are valuable genomic tools for the pea community to strengthen pea as a model for genetics and physiology and enhance breeding. This article is protected by copyright. All rights reserved.
PMID: 26590015 [PubMed - as supplied by publisher]
Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array.
BMC Genomics. 2015;16(1):969
Authors: Tsai HY, Hamilton A, Tinch AE, Guy DR, Gharbi K, Stear MJ, Matika O, Bishop SC, Houston RD
BACKGROUND: The genetic architecture of complex traits in farmed animal populations is of interest from a scientific and practical perspective. The use of genetic markers to predict the genetic merit (breeding values) of individuals is commonplace in modern farm animal breeding schemes. Recently, high density SNP arrays have become available for Atlantic salmon, which facilitates genomic prediction and association studies using genome-wide markers and economically important traits. The aims of this study were (i) to use a high density SNP array to investigate the genetic architecture of weight and length in juvenile Atlantic salmon; (ii) to assess the utility of genomic prediction for these traits, including testing different marker densities; (iii) to identify potential candidate genes underpinning variation in early growth.
RESULTS: A pedigreed population of farmed Atlantic salmon (n = 622) were measured for weight and length traits at one year of age, and genotyped for 111,908 segregating SNP markers using a high density SNP array. The heritability of both traits was estimated using pedigree and genomic relationship matrices, and was comparable at around 0.5 and 0.6 respectively. The results of the GWA analysis pointed to a polygenic genetic architecture, with no SNPs surpassing the genome-wide significance threshold, and one SNP associated with length at the chromosome-wide level. SNPs surpassing an arbitrary threshold of significance (P < 0.005, ~ top 0.5 % of markers) were aligned to an Atlantic salmon reference transcriptome, identifying 109 SNPs in transcribed regions that were annotated by alignment to human, mouse and zebrafish protein databases. Prediction of breeding values was more accurate when applying genomic (GBLUP) than pedigree (PBLUP) relationship matrices (accuracy ~ 0.7 and 0.58 respectively) and 5,000 SNPs were sufficient for obtaining this accuracy increase over PBLUP in this specific population.
CONCLUSIONS: The high density SNP array can effectively capture the additive genetic variation in complex traits. However, the traits of weight and length both appear to be very polygenic with only one SNP surpassing the chromosome-wide threshold. Genomic prediction using the array is effective, leading to an improvement in accuracy compared to pedigree methods, and this improvement can be achieved with only a small subset of the markers in this population. The results have practical relevance for genomic selection in salmon and may also provide insight into variation in the identified genes underpinning body growth and development in salmonid species.
PMID: 26582102 [PubMed - as supplied by publisher]
Construction of a versatile SNP array for pyramiding useful genes of rice.
Plant Sci. 2016 Jan;242:131-9
Authors: Kurokawa Y, Noda T, Yamagata Y, Angeles-Shim R, Sunohara H, Uehara K, Furuta T, Nagai K, Jena KK, Yasui H, Yoshimura A, Ashikari M, Doi K
DNA marker-assisted selection (MAS) has become an indispensable component of breeding. Single nucleotide polymorphisms (SNP) are the most frequent polymorphism in the rice genome. However, SNP markers are not readily employed in MAS because of limitations in genotyping platforms. Here the authors report a Golden Gate SNP array that targets specific genes controlling yield-related traits and biotic stress resistance in rice. As a first step, the SNP genotypes were surveyed in 31 parental varieties using the Affymetrix Rice 44K SNP microarray. The haplotype information for 16 target genes was then converted to the Golden Gate platform with 143-plex markers. Haplotypes for the 14 useful allele are unique and can discriminate among all other varieties. The genotyping consistency between the Affymetrix microarray and the Golden Gate array was 92.8%, and the accuracy of the Golden Gate array was confirmed in 3 F2 segregating populations. The concept of the haplotype-based selection by using the constructed SNP array was proofed.
PMID: 26566831 [PubMed - in process]
Impact of SNP array karyotyping on the diagnosis and the outcome of chronic myelomonocytic leukemia with low risk cytogenetic features or no metaphases.
Am J Hematol. 2015 Oct 28;
Authors: Palomo L, Xicoy B, Garcia O, Mallo M, Ademà V, Cabezón M, Arnan M, Pomares H, Larrayoz MJ, Calasanz MJ, Maciejewski JP, Huang D, Shih LY, Ogawa S, Cervera J, Such E, Coll R, Grau J, Solé F, Zamora L
Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic disorder with heterogeneous clinical, morphological and genetic characteristics. Clonal cytogenetic abnormalities are found in 20-30% of patients with CMML. Patients with low risk cytogenetic features (normal karyotype and isolated loss of Y chromosome) account for approximately 80% of CMML patients and often fall into the low risk categories of CMML prognostic scores. We hypothesized that single nucleotide polymorphism arrays (SNP-A) karyotyping could detect cryptic chromosomal alterations with prognostic impact in these subgroup of patients. SNP-A were performed in 128 CMML patients at diagnosis with low risk karyotypes or uninformative results for conventional G-banding cytogenetics (CC). Copy number alterations (CNAs) and regions of copy number neutral loss of heterozygosity (CNN-LOH) were detected in 67% of patients. Recurrent CNAs included gains in regions 8p12 and 21q22 as well as losses in 10q21.1 and 12p13.2. Interstitial CNN-LOHs were recurrently detected in the following regions: 4q24-4q35, 7q32.1-7q36.3 and 11q13.3-11q25. Statistical analysis showed that some of the alterations detected by SNP-A associated with the patients' outcome. A shortened overall survival (OS) and progression free survival (PFS) was observed in cases where the affected size of the genome (considering CNAs and CNN-LOHs) was >11 Mb. In addition, presence of interstitial CNN-LOH was predictive of poor OS. Presence of CNAs associated with poorer OS and PFS in the patients with myeloproliferative CMML. Overall, SNP-A analysis increased the diagnostic yield in patients with low risk cytogenetic features or uninformative CC and added prognostic value to this subset of patients. This article is protected by copyright. All rights reserved.
PMID: 26509444 [PubMed - as supplied by publisher]