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| 1 | Annual Report to the Nation on the status of cancer, 1975‐2008, featuring cancers associated with excess weight and lack of sufficient physical activity显示文摘 | ChristieEheman S. JaneHenley RachelBallard‐Barbash Eric J.Jacobs Maria J.Schymura Anne‐MichelleNoone LipingPan Robert N.Anderson Janet E.Fulton Betsy A.Kohler AhmedinJemal ElizabethWard MarcusPlescia Lynn A. G.Ries Brenda K.Edwards | 2012 | Cancer2012，，9： | 1 |
| 2 | Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions显示文摘Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP. | Anna Wolc Jesus Arango Petek Settar Janet E.Fulton Neil P.O'Sullivan Jack C.M.Dekkers Rohan Fernando Dorian J.Garrick | 2016 | Journal of Animal Science and Biotechnology2016，7，4： | 1 |
| 3 | Research highlights from the Status report for step it up! The surgeon general's call to action to promote walking and walkable communities显示文摘In September 2015,the Office of the Surgeon General,U.S.Department of Health and Human Services,released Step it up!The surgeon general’s call to action to promote walking and walkable communities(Call to Action)to increase walking among people across the USA.1The Call to Action also recognized that walkable communities can accommodate wheelchair rolling and are inclusive of persons with disabilities.The Status Report for the Call to Action was released in 2017。 | David R.Brown Susan A.Carlson Gayathri S.Kumar Janet E.Fulton | 2018 | Journal of Sport and Health Science2018，7，1： | 0 |
| 4 | Feasibility of using pedometers in a state-based surveillance system:2014 Arizona Behavioral Risk Factor Surveillance System显示文摘Background: Despite their utility in accessing ambulatory movement, pedometers have not been used consistently to monitor physical activity in U.S. surveillance systems. This study was designed to determine the feasibility of using pedometers to assess daily steps taken in a sub-sample of adults from Maricopa County who completed the 2014 Arizona Behavioral Risk Factor Surveillance System Survey.Methods: Respondents were sent an Omron HJ324 U pedometer, a logbook to record steps taken, and a walking questionnaire. The pedometer was worn for 7 days. Feasibility was assessed for acceptability(interest in study), demand(procedures followed correctly), implementation(time to complete study), and practicality(cost).Results: Acceptability was modest with 23.9%(830/3476) agreeing to participate. Among those participating(92.9%; 771/830), 50.1%(386/771)returned the logbook. Demand was modest with 39.3%(303/771) of logbooks returned with valid data. Implementation represented 5 months to recruit participants. The cost to obtain valid step-count data was USD61.60 per person. An average of 6363 ± 3049 steps/day were taken with most participants classified as sedentary(36.0%) or low active(35.6%).Conclusion: The feasibility of using pedometers in a state-based surveillance system is modest at best. Feasibility may potentially be improved with easy-to-use pedometers where data can be electronically downloaded. | Alberto Flórez-Pregonero Janet E.Fulton Joan M.Dorn Barbara E.Ainsworth | 2018 | Journal of Sport and Health Science2018，7，1： | 0 |
| 5 | Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens显示文摘Background: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL(quantitative trait loci) for recombination rate in two purebred layer chicken lines.Methods: A total of 1200 white-egg layers(WL) were genotyped with 580 K SNPs and 5108 brown-egg layers(BL)were genotyped with 42 K SNPs(single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.Results: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL,respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.Conclusions: Differences in the recombination rates, hotspot locations, and QTL regions associated with genomewide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait. | Ziqing Weng Anna Wolc Hailin Su Rohan L.FernANDo Jack C.M.Dekkers Jesus Arango Petek Settar Janet E.Fulton Neil P.O’Sullivan Dorian J.Garrick | 2019 | Journal of Animal Science and Biotechnology2019，10，2： | 0 |
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