Evaluation of a multidisciplinary Tier 3 weight management service for adults with morbid obesity, or obesity and comorbidities, based in primary care

A multidisciplinary Tier 3 weight management service in primary care recruited patients with a body mass index ≥40 kg·m−2, or 30 kg·m−2 with obesity-related co-morbidity to a 1-year programme. A cohort of 230 participants was recruited and evaluated using the National Obesity Observatory Standard Evaluation Framework. The primary outcome was weight loss of at least 5% of baseline weight at 12 months. Diet was assessed using the two-item food frequency questionnaire, activity using the General Practice Physical Activity questionnaire and quality of life using the EuroQol-5D-5L questionnaire. A focus group explored the participants' experiences. Baseline mean weight was 124.4 kg and mean body mass index was 44.1 kg·m−2. A total of 102 participants achieved 5% weight loss at 12 months. The mean weight loss was 10.2 kg among the 117 participants who completed the 12-month programme. Baseline observation carried forward analysis gave a mean weight loss of 5.9 kg at 12 months. Fruit and vegetable intake, activity level and quality of life all improved. The dropout rate was 14.3% at 6 months and 45.1% at 1 year. Focus group participants described high levels of satisfaction. It was possible to deliver a Tier 3 weight management service for obese patients with complex co-morbidity in a primary care setting with a full multidisciplinary team, which obtained good health outcomes compared with existing services

International genomic evaluation methods for dairy cattle

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations are rapidly replacing traditional evaluation systems used for dairy cattle selection. Higher reliabilities from larger genotype files promote cooperation across country borders. Genomic information can be exchanged across countries using simple conversion equations, by modifying multi-trait across-country evaluation (MACE) to account for correlated residuals originating from the use of foreign evaluations, or by multi-trait analysis of genotypes for countries that use the same reference animals.</p> <p>Methods</p> <p>Traditional MACE assumes independent residuals because each daughter is measured in only one country. Genomic MACE could account for residual correlations using daughter equivalents from genomic data as a fraction of the total in each country and proportions of bulls shared. MACE methods developed to combine separate within-country genomic evaluations were compared to direct, multi-country analysis of combined genotypes using simulated genomic and phenotypic data for 8,193 bulls in nine countries.</p> <p>Results</p> <p>Reliabilities for young bulls were much higher for across-country than within-country genomic evaluations as measured by squared correlations of estimated with true breeding values. Gains in reliability from genomic MACE were similar to those of multi-trait evaluation of genotypes but required less computation. Sharing of reference genotypes among countries created large residual correlations, especially for young bulls, that are accounted for in genomic MACE.</p> <p>Conclusions</p> <p>International genomic evaluations can be computed either by modifying MACE to account for residual correlations across countries or by multi-trait evaluation of combined genotype files. The gains in reliability justify the increased computation but require more cooperation than in previous breeding programs.</p

TREEPLAN� - A Genetic Evaluation System for Forest Trees

The TREEPLAN� genetic evaluation system is designed specifically for the efficient and accurate prediction of breeding and other genetic values in trees. TREEPLAN� uses the preferred statistical method of best linear unbiased prediction (BLUP) using an individual tree additive genetic effect. Although BLUP methods are well developed theoretically, other software is suitable only for breeding value estimation and prediction on small and/or highly structured (balanced) data sets. Packages such as ASREML and SAS have hardware and software limitations that make them unsuitable for routine prediction on large data sets with complex pedigree structures and overlapping generations. TREEPLAN� fits a reduced individual tree model for purposes of efficiency. TREEPLAN� can model multiple genetic groups, handle clonal data, fit multi-trait models with more than 50 traits, accommodate heterogeneous variances, fit site specific statistical and genetic models, and also weights information across environments (accounts for genotype by environment interaction) and time (allows for age:age correlations). The Southern Tree Breeding Association (STBA) is routinely using TREEPLAN� for genetic evaluation in Australian tree improvement programs for Pinus radiata, Eucalyptus globulus and E. nitens. TREEPLAN� has allowed data across generations and years to be combined in a multi-trait analysis to produce single lists of breeding values for each trait and environment combination. TREEPLAN� is easy to use and has the �industrial strength� to handle large amounts of unbalanced data with the complex pedigree structures that are usually associated with national or regional tree improvement programs. TREEPLAN� is fully integrated with a web based data management system that efficiently handles data and pedigree information. The analytical power and flexibility of the TREEPLAN� system has made routine genetic evaluation in trees a straightforward process.Papers and abstracts from the 27th Southern Forest Tree Improvement Conference held at Oklahoma State University in Stillwater, Oklahoma on June 24-27, 2003

A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes

<p>Abstract</p> <p>Background</p> <p>Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data.</p> <p>Methods</p> <p>A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information.</p> <p>Results</p> <p>The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available.</p> <p>Conclusions</p> <p>The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.</p

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

<p>Abstract</p> <p>Background</p> <p>Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.</p> <p>Methods</p> <p>An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis.</p> <p>Results</p> <p>Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored.</p> <p>Conclusions</p> <p>The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.</p