Dataset on viscosity and starch polymer properties to predict texture through modeling.

Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers' and farmers' need. Here we showed the data related to the article entitled "Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture" [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained.

Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture.

Acceptance of new rice genotypes demanded by rice value chain depends on premium value of varieties that match consumer demands of regional preferences. High throughput prediction tools are not available to breeders to classify cooking and eating quality (CEQ) ideotypes and to capture texture of varieties. The pasting properties in combination with starch properties were used to develop two layered models in order to classify the rice varieties into twelve distinct CEQ ideotypes with unique sensory profiles. Classification models developed using random forest method depicted the overall accuracy of 96 %. These CEQ models were found to be robust to predict ideotypes in both Indica and Japonica diversity panels grown under dry and wet seasons and across the years. We conducted random forest modeling using 1.8 million high density SNPs and identified top 1000 SNP features which explained CEQ model classification with the accuracy of 0.81. Furthermore these CEQ models were found to be valuable to predict textural preferences of IRRI breeding lines released during 1960-2013 and mega varieties preferred in South and South East Asia.

Genome-wide association study of ankylosing spondylitis identifies non-MHC susceptibility loci.

To identify susceptibility loci for ankylosing spondylitis, we undertook a genome-wide association study in 2,053 unrelated ankylosing spondylitis cases among people of European descent and 5,140 ethnically matched controls, with replication in an independent cohort of 898 ankylosing spondylitis cases and 1,518 controls. Cases were genotyped with Illumina HumHap370 genotyping chips. In addition to strong association with the major histocompatibility complex (MHC; P < 10(-800)), we found association with SNPs in two gene deserts at 2p15 (rs10865331; combined P = 1.9 x 10(-19)) and 21q22 (rs2242944; P = 8.3 x 10(-20)), as well as in the genes ANTXR2 (rs4333130; P = 9.3 x 10(-8)) and IL1R2 (rs2310173; P = 4.8 x 10(-7)). We also replicated previously reported associations at IL23R (rs11209026; P = 9.1 x 10(-14)) and ERAP1 (rs27434; P = 5.3 x 10(-12)). This study reports four genetic loci associated with ankylosing spondylitis risk and identifies a major role for the interleukin (IL)-23 and IL-1 cytokine pathways in disease susceptibility.

A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose.

We report the first genome-wide association study (GWAS) whose sample size (1,053 Swedish subjects) is sufficiently powered to detect genome-wide significance (p<1.5 x 10(-7)) for polymorphisms that modestly alter therapeutic warfarin dose. The anticoagulant drug warfarin is widely prescribed for reducing the risk of stroke, thrombosis, pulmonary embolism, and coronary malfunction. However, Caucasians vary widely (20-fold) in the dose needed for therapeutic anticoagulation, and hence prescribed doses may be too low (risking serious illness) or too high (risking severe bleeding). Prior work established that approximately 30% of the dose variance is explained by single nucleotide polymorphisms (SNPs) in the warfarin drug target VKORC1 and another approximately 12% by two non-synonymous SNPs (*2, *3) in the cytochrome P450 warfarin-metabolizing gene CYP2C9. We initially tested each of 325,997 GWAS SNPs for association with warfarin dose by univariate regression and found the strongest statistical signals (p<10(-78)) at SNPs clustering near VKORC1 and the second lowest p-values (p<10(-31)) emanating from CYP2C9. No other SNPs approached genome-wide significance. To enhance detection of weaker effects, we conducted multiple regression adjusting for known influences on warfarin dose (VKORC1, CYP2C9, age, gender) and identified a single SNP (rs2108622) with genome-wide significance (p = 8.3 x 10(-10)) that alters protein coding of the CYP4F2 gene. We confirmed this result in 588 additional Swedish patients (p<0.0029) and, during our investigation, a second group provided independent confirmation from a scan of warfarin-metabolizing genes. We also thoroughly investigated copy number variations, haplotypes, and imputed SNPs, but found no additional highly significant warfarin associations. We present power analysis of our GWAS that is generalizable to other studies, and conclude we had 80% power to detect genome-wide significance for common causative variants or markers explaining at least 1.5% of dose variance. These GWAS results provide further impetus for conducting large-scale trials assessing patient benefit from genotype-based forecasting of warfarin dose.

Common variants in the region around Osterix are associated with bone mineral density and growth in childhood.

Peak bone mass achieved in adolescence is a determinant of bone mass in later life. In order to identify genetic variants affecting bone mineral density (BMD), we performed a genome-wide association study of BMD and related traits in 1518 children from the Avon Longitudinal Study of Parents and Children (ALSPAC). We compared results with a scan of 134 adults with high or low hip BMD. We identified associations with BMD in an area of chromosome 12 containing the Osterix (SP7) locus, a transcription factor responsible for regulating osteoblast differentiation (ALSPAC: P = 5.8 x 10(-4); Australia: P = 3.7 x 10(-4)). This region has previously shown evidence of association with adult hip and lumbar spine BMD in an Icelandic population, as well as nominal association in a UK population. A meta-analysis of these existing studies revealed strong association between SNPs in the Osterix region and adult lumbar spine BMD (P = 9.9 x 10(-11)). In light of these findings, we genotyped a further 3692 individuals from ALSPAC who had whole body BMD and confirmed the association in children as well (P = 5.4 x 10(-5)). Moreover, all SNPs were related to height in ALSPAC children, but not weight or body mass index, and when height was included as a covariate in the regression equation, the association with total body BMD was attenuated. We conclude that genetic variants in the region of Osterix are associated with BMD in children and adults probably through primary effects on growth.

ASTD: The Alternative Splicing and Transcript Diversity database.

The Alternative Splicing and Transcript Diversity database (ASTD) gives access to a vast collection of alternative transcripts that integrate transcription initiation, polyadenylation and splicing variant data. Alternative transcripts are derived from the mapping of transcribed sequences to the complete human, mouse and rat genomes using an extension of the computational pipeline developed for the ASD (Alternative Splicing Database) and ATD (Alternative Transcript Diversity) databases, which are now superseded by ASTD. For the human genome, ASTD identifies splicing variants, transcription initiation variants and polyadenylation variants in 68%, 68% and 62% of the gene set, respectively, consistent with current estimates for transcription variation. Users can access ASTD through a variety of browsing and query tools, including expression state-based queries for the identification of tissue-specific isoforms. Participating laboratories have experimentally validated a subset of ASTD-predicted alternative splice forms and alternative polyadenylation forms that were not previously reported. The ASTD database can be accessed at http://www.ebi.ac.uk/astd.

AltTrans: transcript pattern variants annotated for both alternative splicing and alternative polyadenylation.

BACKGROUND: The three major mechanisms that regulate transcript formation involve the selection of alternative sites for transcription start (TS), splicing, and polyadenylation. Currently there are efforts that collect data & annotation individually for each of these variants. It is important to take an integrated view of these data sets and to derive a data set of alternate transcripts along with consolidated annotation. We have been developing in the past computational pipelines that generate value-added data at genome-scale on individual variant types; these include AltSplice on splicing and AltPAS on polyadenylation. We now extend these pipelines and integrate the resultant data sets to facilitate an integrated view of the contributions from splicing and polyadenylation in the formation of transcript variants. DESCRIPTION: The AltSplice pipeline examines gene-transcript alignments and delineates alternative splice events and splice patterns; this pipeline is extended as AltTrans to delineate isoform transcript patterns for each of which both introns/exons and 'terminating' polyA site are delineated; EST/mRNA sequences that qualify the transcript pattern confirm both the underlying splicing and polyadenylation. The AltPAS pipeline examines gene-transcript alignments and delineates all potential polyA sites irrespective of underlying splicing patterns. Resultant polyA sites from both AltTrans and AltPAS are merged. The generated database reports data on alternative splicing, alternative polyadenylation and the resultant alternate transcript patterns; the basal data is annotated for various biological features. The data (named as integrated AltTrans data) generated for both the organisms of human and mouse is made available through the Alternate Transcript Diversity web site at http://www.ebi.ac.uk/atd/. CONCLUSION: The reported data set presents alternate transcript patterns that are annotated for both alternative splicing and alternative polyadenylation. Results based on current transcriptome data indicate that the contribution of alternative splicing is larger than that of alternative polyadenylation.

ASD: a bioinformatics resource on alternative splicing.

Alternative splicing is an important regulatory mechanism of mammalian gene expression. The alternative splicing database (ASD) consortium is systematically collecting and annotating data on alternative splicing. We present the continuation and upgrade of the ASD [T. A. Thanaraj, S. Stamm, F. Clark, J. J. Riethoven, V. Le Texier, J. Muilu (2004) Nucleic Acids Res. 32, D64-D69] that consists of computationally and manually generated data. Its largest parts are AltSplice, a value-added database of computationally delineated alternative splicing events. Its data include alternatively spliced introns/exons, events, isoform splicing patterns and isoform peptide sequences. AltSplice data are generated by examining gene-transcript alignments. The data are annotated for various biological features including splicing signals, expression states, (SNP)-mediated splicing and cross-species conservation. AEdb forms the manually curated component of ASD. It is a literature-based data set containing sequence and properties of alternatively spliced exons, functional enumeration of observed splicing events, characterization of observed splicing regulatory elements, and a collection of experimentally clarified minigene constructs. ASD includes a workbench, which is an analysis tool that enables users to carry out splicing related analysis such as characterization of introns for various splicing signals, identification of splicing regulatory elements on a given RNA sequence, prediction of putative exons and prediction of putative translation start codons. The different ASD modules are integrated and can be accessed through user-friendly interfaces and visualization tools. ASD data has been integrated with Ensembl genome annotation project as a Distributed Annotation System (DAS) resource and can be viewed on Ensembl genome browser. The ASD resource is presented at (http://www.ebi.ac.uk/asd).