“I would rather be told than not know” - A qualitative study exploring parental views on identifying the future risk of childhood overweight and obesity during infancy

BACKGROUND: Risk assessment tools provide an opportunity to prevent childhood overweight and obesity through early identification and intervention to influence infant feeding practices. Engaging parents of infants is paramount for success however; the literature suggests there is uncertainty surrounding the use of such tools with concerns about stigmatisation, labelling and expressions of parental guilt. This study explores parents' views on identifying future risk of childhood overweight and obesity during infancy and communicating risk to parents. METHODS: Semi-structured qualitative interviews were conducted with 23 parents and inductive, interpretive and thematic analysis performed. RESULTS: Three main themes emerged from the data: 1) Identification of infant overweight and obesity risk. Parents were hesitant about health professionals identifying infant overweight as believed they would recognise this for themselves, in addition parents feared judgement from health professionals. Identification of future obesity risk during infancy was viewed positively however the use of a non-judgemental communication style was viewed as imperative. 2) Consequences of infant overweight. Parents expressed immediate anxieties about the impact of excess weight on infant ability to start walking. Parents were aware of the progressive nature of childhood obesity however, did not view overweight as a significant problem until the infant could walk as viewed this as a point when any excess weight would be lost due to increased energy expenditure. 3) Parental attributions of causality, responsibility, and control. Parents articulated a high level of personal responsibility for preventing and controlling overweight during infancy, which translated into self-blame. Parents attributed infant overweight to overfeeding however articulated a reluctance to modify infant feeding practices prior to weaning. CONCLUSION: This is the first study to explore the use of obesity risk tools in clinical practice, the findings suggest that identification, and communication of future overweight and obesity risk is acceptable to parents of infants. Despite this positive response, findings suggest that parents' acceptance to identification of risk and implementation of behaviour change is time specific. The apparent level of parental responsibility, fear of judgement and self-blame also highlights the importance of health professionals approach to personalised risk communication so feelings of self-blame are negated and stigmatisation avoided

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

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.Heredity advance online publication, 3 April 2013; doi:10.1038/hdy.2013.13

Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature.

We report mapping of a quantitative trait locus (QTL) with a major effect on bovine stature to a approximately 780-kb interval using a Hidden Markov Model-based approach that simultaneously exploits linkage and linkage disequilibrium. We re-sequenced the interval in six sires with known QTL genotype and identified 13 clustered candidate quantitative trait nucleotides (QTNs) out of >9,572 discovered variants. We eliminated five candidate QTNs by studying the phenotypic effect of a recombinant haplotype identified in a breed diversity panel. We show that the QTL influences fetal expression of seven of the nine genes mapping to the approximately 780-kb interval. We further show that two of the eight candidate QTNs, mapping to the PLAG1-CHCHD7 intergenic region, influence bidirectional promoter strength and affect binding of nuclear factors. By performing expression QTL analyses, we identified a splice site variant in CHCHD7 and exploited this naturally occurring null allele to exclude CHCHD7 as single causative gene