Abstract
Dairy cattle, and Holsteins in particular, were the first major agricultural industry to fully embrace genomic selection (GS). Few of us had predicted that, by May 2019, the number of ...genotyped animals would exceed 3 million. Farmers are changing the frequency of individual alleles and making use of genomic information to select better bulls; identify elite embryo donors; determine if a cow should be bred with conventional, sexed or beef semen; become an embryo recipient or be culled. Our industry has traditionally been an open system, where top genetics are sourced from the general population and phenotypes are provided on a voluntary basis. Contractual agreements have allowed this system to continue and have been extended to access of data, differential pricing, international collaboration and more. Phenotypes no longer come from well designed, highly organized progeny testing programs but rather from paid contributors who provide data that are quality certified and representative of the population. Over time, we’ve improved our genome map, SNP chips, reference populations, statistical models, computing ability, data pipeline, and most importantly our knowledge of genetics. Combining different types of data from multiple sources continues to be a challenge. Interdisciplinary approach and collaboration with other scientists are now the norm. GS has caused a paradigm shift within the dairy industry. But, after 10 years, we are in a better position to more quickly integrate new scientific knowledge. Making our industry and the production of dairy products more efficient and sustainable.
Dairy cattle, and Holsteins in particular, were the first major agricultural industry to fully embrace genomic selection (GS). Few of us had predicted that, by May 2019, the number of genotyped ...animals would exceed 3 million. Farmers are changing the frequency of individual alleles and making use of genomic information to select better bulls; identify elite embryo donors; determine if a cow should be bred with conventional, sexed or beef semen; become an embryo recipient or be culled. Our industry has traditionally been an open system, where top genetics are sourced from the general population and phenotypes are provided on a voluntary basis. Contractual agreements have allowed this system to continue and have been extended to access of data, differential pricing, international collaboration and more. Phenotypes no longer come from well designed, highly organized progeny testing programs but rather from paid contributors who provide data that are quality certified and representative of the population. Over time, we've improved our genome map, SNP chips, reference populations, statistical models, computing ability, data pipeline, and most importantly our knowledge of genetics. Combining different types of data from multiple sources continues to be a challenge. Interdisciplinary approach and collaboration with other scientists are now the norm. GS has caused a paradigm shiftwithin the dairy industry. But, after 10 years, we are in a better position to more quickly integrate new scientific knowledge. Making our industry and the production of dairy products more efficient and sustainable.
Spending more time active (and less sedentary) is associated with health benefits such as improved cardiovascular health and lower risk of all-cause mortality. It is unclear whether these ...associations differ depending on whether time spent sedentary or in moderate-vigorous physical activity (MVPA) is accumulated in long or short bouts. In this study, we used a novel method that accounts for substitution (i.e., more time in MVPA means less time sleeping, in light activity or sedentary) to examine whether length of sedentary and MVPA bouts associates with all-cause mortality.
We used data on 79,503 adult participants from the population-based UK Biobank cohort, which recruited participants between 2006 and 2010 (mean age at accelerometer wear 62.1 years SD = 7.9, 54.5% women; mean length of follow-up 5.1 years SD = 0.73). We derived (1) the total time participants spent in activity categories-sleep, sedentary, light activity, and MVPA-on average per day; (2) time spent in sedentary bouts of short (1 to 15 minutes), medium (16 to 40 minutes), and long (41+ minutes) duration; and (3) MVPA bouts of very short (1 to 9 minutes), short (10 to 15 minutes), medium (16 to 40 minutes), and long (41+ minutes) duration. We used Cox proportion hazards regression to estimate the association of spending 10 minutes more average daily time in one activity or bout length category, coupled with 10 minutes less time in another, with all-cause mortality. Those spending more time in MVPA had lower mortality risk, irrespective of whether this replaced time spent sleeping, sedentary, or in light activity, and these associations were of similar magnitude (e.g., hazard ratio HR 0.96 95% CI: 0.94, 0.97; P < 0.001 per 10 minutes more MVPA, coupled with 10 minutes less light activity per day). Those spending more time sedentary had higher mortality risk if this replaced light activity (HR 1.02 95% CI: 1.01, 1.02; P < 0.001 per 10 minutes more sedentary time, with 10 minutes less light activity per day) and an even higher risk if this replaced MVPA (HR 1.06 95% CI: 1.05, 1.08; P < 0.001 per 10 minutes more sedentary time, with 10 minutes less MVPA per day). We found little evidence that mortality risk differed depending on the length of sedentary or MVPA bouts. Key limitations of our study are potential residual confounding, the limited length of follow-up, and use of a select sample of the United Kingdom population.
We have shown that time spent in MVPA was associated with lower mortality, irrespective of whether it replaced time spent sleeping, sedentary, or in light activity. Time spent sedentary was associated with higher mortality risk, particularly if it replaced MVPA. This emphasises the specific importance of MVPA. Our findings suggest that the impact of MVPA does not differ depending on whether it is obtained from several short bouts or fewer longer bouts, supporting the recent removal of the requirement that MVPA should be accumulated in bouts of 10 minutes or more from the UK and the United States policy. Further studies are needed to investigate causality and explore health outcomes beyond mortality.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Evidence suggests that in utero exposure to undernutrition and overnutrition might affect adiposity in later life. Epigenetic modification is suggested as a plausible mediating mechanism.
We used ...multivariable linear regression and a negative control design to examine offspring epigenome-wide DNA methylation in relation to maternal and offspring adiposity in 1018 participants.
Compared with neonatal offspring of normal weight mothers, 28 and 1621 CpG sites were differentially methylated in offspring of obese and underweight mothers, respectively false discovert rate (FDR)-corrected P-value < 0.05), with no overlap in the sites that maternal obesity and underweight relate to. A positive association, where higher methylation is associated with a body mass index (BMI) outside the normal range, was seen at 78.6% of the sites associated with obesity and 87.9% of the sites associated with underweight. Associations of maternal obesity with offspring methylation were stronger than associations of paternal obesity, supporting an intrauterine mechanism. There were no consistent associations of gestational weight gain with offspring DNA methylation. In general, sites that were hypermethylated in association with maternal obesity or hypomethylated in association with maternal underweight tended to be positively associated with offspring adiposity, and sites hypomethylated in association with maternal obesity or hypermethylated in association with maternal underweight tended to be inversely associated with offspring adiposity.
Our data suggest that both maternal obesity and, to a larger degree, underweight affect the neonatal epigenome via an intrauterine mechanism, but weight gain during pregnancy has little effect. We found some evidence that associations of maternal underweight with lower offspring adiposity and maternal obesity with greater offspring adiposity may be mediated via increased DNA methylation.
Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due ...to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase ALT, aspartate aminotransferase AST, alkaline phosphatase ALP, and γ-glutamyl transferase GGT). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.
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