Fruit size and firmness QTL alleles of breeding interest identified in a sweet cherry ‘Ambrunés’ × ‘Sweetheart’ population

The Spanish local cultivar ‘Ambrunés’ stands out due to its high organoleptic quality and fruit firmness. These characteristics make it an important parent for breeding cherries with excellent fresh and post-harvest quality. In this work, an F1 sweet cherry population (n = 140) from ‘Ambrunés’ × ‘Sweetheart’ was phenotyped for 2 years for fruit diameter, weight and firmness and genotyped with the RosBREED cherry Illumina Infinium® 6K SNP array v1. These data were used to construct a linkage map and to carry out quantitative trait locus (QTL) mapping of these fruit quality traits. Genotyping of the parental cultivars revealed that ‘Ambrunés’ is highly heterozygous, and its genetic map is the longest reported in the species using the same SNP array. Phenotypic data analyses confirmed a high heritability of fruit size and firmness and a distorted segregation towards softer and smaller fruits. However, individuals with larger and firmer fruits than the parental cultivars were observed, revealing the presence of alleles of breeding interest. In contrast to other genetic backgrounds in which a negative correlation was observed between firmness and size, in this work, no correlation or low positive correlation was detected between both traits. Firmness, diameter and weight QTLs detected validated QTLs previously found for the same traits in the species, and major QTLs for the three traits were located on a narrow region of LG1 of ‘Ambrunés’. Haplotype analyses of these QTLs revealed haplotypes of breeding interest in coupling phase in ‘Ambrunés’, which can be used for the selection of progeny with larger and firmer fruits

Indicators of breast cancer severity and appropriateness of surgery based on hospital administrative data in the Lazio Region, Italy

BACKGROUND: Administrative data can serve as an easily available source for epidemiological and evaluation studies. The aim of this study is to evaluate the use of hospital administrative data to determine breast cancer severity and the appropriateness of surgical treatment. METHODS: the study population consisted of 398 patients randomly selected from a cohort of women hospitalized for first-time breast cancer surgery in the Lazio Region, Italy. Tumor severity was defined in three different ways: 1) tumor size; 2) clinical stage (TNM); 3) severity indicator based on HIS data (SI). Sensitivity, specificity, and positive predictive value (PPV) of the severity indicator in evaluating appropriateness of surgery were calculated. The accuracy of HIS data was measured using Kappa statistic. RESULTS: Most of 387 cases were classified as T1 and T2 (tumor size), more than 70% were in stage I or II and the SI classified 60% of cases in medium-low category. Variation from guidelines indications identified under and over treatments. The accuracy of the SI to predict under-treatment was relatively good (58% of all procedures classified as under-treatment using pT where also classified as such using SI), and even greater predicting over-treatment (88.2% of all procedures classified as over treatment using pT where also classified as such using SI). Agreement between clinical chart and hospital discharge reports was K = 0.35. CONCLUSION: Our findings suggest that administrative data need to be used with caution when evaluating surgical appropriateness, mainly because of the limited ability of SI to predict tumor size and the questionable quality of HIS data as observed in other studies

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure

Development and Evaluation of a 9K SNP Addition to the Peach Ipsc 9K SNP Array v1

The IPSC 9K peach SNP array released by the international community has been a valuable tool in research and application. Even though majority of SNPs (84%) were polymorphic in the evaluation panels there were many genomic regions with low coverage, including those important for breeding. The existing peach array has been updated with 9K additional SNPs covering previously identified gaps and including recently identified SNPs important for breeding. SNPs (1,808,996) identified by sequencing 49 genomes of additional peach accessions were used as the main source of additional SNPs. Focal point strategy was used to select 8,971 SNPs within 40kb window from the 2,821 focal points distributed across the genome. Additional 129 SNPs were chosen to saturate either regions important for breeding or close the gaps larger than 100kb. The array was validated with 1,770 peach and 26 Prunus accessions (almond, plum, apricot, wild relatives). The add-on contained 7,862 SNPs evenly spread across 8 peach pseudo-molecules with only one SNP positioned on scaffold 13 covering 224.99Mbp of peach genome. The 9K add-on improved the 9K peach array by increasing the total number of usable SNPs by 7,206. The number of SNPs per chromosome increased on average by 50% with only on average 0.18% increase in total physical coverage. Number of gaps larger than 0.3 Mbp was reduced to 2 one on each chromosome 3 and 8. Overall genotyping efficiency in all material was >90% except in almond, 82%. Number of informative markers, assessed by ASSIsT software, were highest in peach 64% and lowest in almond 10%, with 61% of markers being informative in wild Prunus (12) and 35% in apricot (4) and 2 - 33% in Japanese and European plum, respectively. Among 36.2% discarded markers 33% were monomorphic and 30% shifted homozygous in material used. Those markers could be informative in different background raising total number of informative markers. Ann addition of new SNPs to array improved the density and usefulness of the array in Prunus species. The practical applications of new 16K Illumina SNP peach array will be discussed. Specified Source(s) of Funding: USDA-NIFA-SCRI-Ros- BREED (2014-51181-22378

Use of hierarchical models to evaluate performance of cardiac surgery centres in the Italian CABG outcome study

<p>Abstract</p> <p>Background</p> <p>Hierarchical modelling represents a statistical method used to analyze nested data, as those concerning patients afferent to different hospitals. Aim of this paper is to build a hierarchical regression model using data from the "Italian CABG outcome study" in order to evaluate the amount of differences in adjusted mortality rates attributable to differences between centres.</p> <p>Methods</p> <p>The study population consists of all adult patients undergoing an isolated CABG between 2002–2004 in the 64 participating cardiac surgery centres.</p> <p>A risk adjustment model was developed using a classical single-level regression. In the multilevel approach, the variable "clinical-centre" was employed as a group-level identifier. The intraclass correlation coefficient was used to estimate the proportion of variability in mortality between groups. Group-level residuals were adopted to evaluate the effect of clinical centre on mortality and to compare hospitals performance. Spearman correlation coefficient of ranks (<it>ρ</it>) was used to compare results from classical and hierarchical model.</p> <p>Results</p> <p>The study population was made of 34,310 subjects (mortality rate = 2.61%; range 0.33–7.63). The multilevel model estimated that 10.1% of total variability in mortality was explained by differences between centres. The analysis of group-level residuals highlighted 3 centres (VS 8 in the classical methodology) with estimated mortality rates lower than the mean and 11 centres (VS 7) with rates significantly higher. Results from the two methodologies were comparable (<it>ρ </it>= 0.99).</p> <p>Conclusion</p> <p>Despite known individual risk-factors were accounted for in the single-level model, the high variability explained by the variable "clinical-centre" states its importance in predicting 30-day mortality after CABG.</p

Do coder characteristics influence validity of ICD-10 hospital discharge data?

<p>Abstract</p> <p>Background</p> <p>Administrative data are widely used to study health systems and make important health policy decisions. Yet little is known about the influence of coder characteristics on administrative data validity in these studies. Our goal was to describe the relationship between several measures of validity in coded hospital discharge data and 1) coders' volume of coding (≥13,000 vs. <13,000 records), 2) coders' employment status (full- vs. part-time), and 3) hospital type.</p> <p>Methods</p> <p>This descriptive study examined 6 indicators of face validity in ICD-10 coded discharge records from 4 hospitals in Calgary, Canada between April 2002 and March 2007. Specifically, mean number of coded diagnoses, procedures, complications, Z-codes, and codes ending in 8 or 9 were compared by coding volume and employment status, as well as hospital type. The mean number of diagnoses was also compared across coder characteristics for 6 major conditions of varying complexity. Next, kappa statistics were computed to assess agreement between discharge data and linked chart data reabstracted by nursing chart reviewers. Kappas were compared across coder characteristics.</p> <p>Results</p> <p>422,618 discharge records were coded by 59 coders during the study period. The mean number of diagnoses per record decreased from 5.2 in 2002/2003 to 3.9 in 2006/2007, while the number of records coded annually increased from 69,613 to 102,842. Coders at the tertiary hospital coded the most diagnoses (5.0 compared with 3.9 and 3.8 at other sites). There was no variation by coder or site characteristics for any other face validity indicator. The mean number of diagnoses increased from 1.5 to 7.9 with increasing complexity of the major diagnosis, but did not vary with coder characteristics. Agreement (kappa) between coded data and chart review did not show any consistent pattern with respect to coder characteristics.</p> <p>Conclusions</p> <p>This large study suggests that coder characteristics do not influence the validity of hospital discharge data. Other jurisdictions might benefit from implementing similar employment programs to ours, e.g.: a requirement for a 2-year college training program, a single management structure across sites, and rotation of coders between sites. Limitations include few coder characteristics available for study due to privacy concerns.</p

Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data

BACKGROUND: Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges.METHODS: Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities.RESULTS: For the 17 the Charlson co-morbidities, the sensitivity - median (min-max) - was 36.5% (17.4-64.1) in 1999, 42.5% (22.2-64.6) in 2001 and 42.8% (8.4-75.6) in 2003. For the 29 Elixhauser co-morbidities, the sensitivity was 34.2% (1.9-64.1) in 1999, 38.6% (10.5-66.5) in 2001 and 41.6% (5.1-76.5) in 2003. Between 1999 and 2003, sensitivity estimates increased for 30 co-morbidities and decreased for 6 co-morbidities. The increase in sensitivities was statistically significant for six conditions and the decrease significant for one. Kappa values were increased for 29 co-morbidities and decreased for seven.CONCLUSIONS: Accuracy of administrative data in recording clinical conditions improved slightly between 1999 and 2003. These findings are of relevance to all jurisdictions introducing new coding systems, because they demonstrate a phenomenon of improved administrative data accuracy that may relate to a coding 'learning curve' with the new coding system

Divergência genética entre cultivares locais e cultivares melhoradas de feijão.

A grande variabilidade genética presente no germoplasma de feijão (Phaseolus vulgaris L.) em uso na agricultura familiar no Brasil tem sido plenamente reconhecida. A eficiência da conservação e o aproveitamento desta variabilidade aumentam quando esta é devidamente caracterizada. O objetivo deste trabalho foi caracterizar a variabilidade genética de parte do germoplasma existente em poder de produtores de feijão no Rio Grande do Sul, e de cultivares produzidas pela pesquisa, e reuni-las em grupos de similaridade genética. Foi avaliada a divergência genética de 37 cultivares locais (land races) e 14 cultivares indicadas pela pesquisa no Estado, utilizando 40 descritores morfológicos; a grande maioria desses descritores são necessários à proteção legal. Empregou-se análise multivariada, por intermédio de componentes principais e método de agrupamento. O uso destas técnicas possibilitou identificar descritores ineficientes ou redundantes no estudo da variabilidade genética e reunir as cultivares estudadas em quatro grupos distintos de similaridade genética. As cultivares locais revelaram variabilidade superior à encontrada nas cultivares oriundas da pesquisa, o que sugere a importância da sua inclusão em programas de melhoramento

Risk adjustment for inter-hospital comparison of primary cesarean section rates: need, validity and parsimony

BACKGROUND: Cesarean section rates is often used as an indicator of quality of care in maternity hospitals. The assumption is that lower rates reflect in developed countries more appropriate clinical practice and general better performances. Hospitals are thus often ranked on the basis of caesarean section rates. The aim of this study is to assess whether the adjustment for clinical and sociodemographic variables of the mother and the fetus is necessary for inter-hospital comparisons of cesarean section (c-section) rates and to assess whether a risk adjustment model based on a limited number of variables could be identified and used. METHODS: Discharge abstracts of labouring women without prior cesarean were linked with abstracts of newborns discharged from 29 hospitals of the Emilia-Romagna Region (Italy) from 2003 to 2004. Adjusted ORs of cesarean by hospital were estimated by using two logistic regression models: 1) a full model including the potential confounders selected by a backward procedure; 2) a parsimonious model including only actual confounders identified by the "change-in-estimate" procedure. Hospital rankings, based on ORs were examined. RESULTS: 24 risk factors for c-section were included in the full model and 7 (marital status, maternal age, infant weight, fetopelvic disproportion, eclampsia or pre-eclampsia, placenta previa/abruptio placentae, malposition/malpresentation) in the parsimonious model. Hospital ranking using the adjusted ORs from both models was different from that obtained using the crude ORs. The correlation between the rankings of the two models was 0.92. The crude ORs were smaller than ORs adjusted by both models, with the parsimonious ones producing more precise estimates. CONCLUSION: Risk adjustment is necessary to compare hospital c-section rates, it shows differences in rankings and highlights inappropriateness of some hospitals. By adjusting for only actual confounders valid and more precise estimates could be obtained