Socioeconomic factors and self-reported health outcomes in African Americans with rheumatoid arthritis from the Southeastern United States: The contribution of childhood socioeconomic status

Background: There is abundant evidence that low socioeconomic status (SES) is associated with worse health outcomes among people with Rheumatoid Arthritis (RA); however, the influence of socioeconomic disadvantage in early life has yet to be studied within that population. Methods: Data originated from the cross-sectional arm of the Consortium Evaluation of African-Americans with Rheumatoid Arthritis (CLEAR II), which recruited African-Americans with RA from six sites in the Southeastern United States. We used linear regression models to evaluate associations of parental homeownership status and educational level at participant time of birth with participant-reported fatigue (Visual Analog scale, cm), pain (Visual Analog scale, cm), disability (Health Assessment Questionnaire) and helplessness (Rheumatology Attitudes Index), independently of participant homeownership status and educational level. Models included random effects to account for intra-site correlations, and were adjusted for variables identified using backward selection, from: age, disease-duration, sex, medication use, body-mass index, smoking history. Results: Our sample included 516 CLEAR II participants with full data on demographics and covariates. 89 % of participants were women, the mean age was 54.7 years and mean disease duration was 10.8 years. In age adjusted models, parental non-homeownership was associated with greater fatigue (β = 0.75, 95 % CI = 0.36-1.14), disability (β = 0.12, 95 % CI = 0.04-0.19) and helplessness (β = 0.12, 95 % CI = 0.03-0.21), independently of participant homeownership and education; parental education had a further small influence on self-reported fatigue (β = 0.20, 95 % CI = 0.15-0.24). Conclusions: Parental homeownership, and to a small extent parental education, had modest but meaningful relationships with self-reported health among CLEAR II participants

Genotype imputation for the prediction of genomic breeding values in non-genotyped and low-density genotyped individuals

<p>Abstract</p> <p>Background</p> <p>There is wide interest in calculating genomic breeding values (GEBVs) in livestock using dense, genome-wide SNP data. The general framework for genomic selection assumes all individuals are genotyped at high-density, which may not be true in practice. Methods to add additional genotypes for individuals not genotyped at high density have the potential to increase GEBV accuracy with little or no additional cost. In this study a long haplotype library was created using a long range phasing algorithm and used in combination with segregation analysis to impute dense genotypes for non-genotyped dams in the training dataset (S1) and for non-genotyped or low-density genotyped individuals in the prediction dataset (S2), using the 14^th QTL-MAS Workshop dataset. Alternative low-density scenarios were evaluated for accuracy of imputed genotypes and prediction of GEBVs.</p> <p>Results</p> <p>In S1, females in the training population were not genotyped and prediction individuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or 10 Mb). The proportion of correctly imputed genotypes for training females did not change when genotypes were added for individuals in the prediction set whereas the number of correctly imputed genotypes in the prediction set increased slightly (S1). The S2 scenario assumed the complete training set was genotyped for all SNPs and the prediction set was not genotyped or genotyped at low-density. The number of correctly imputed genotypes increased with genotyping density in the prediction set. Accuracy of genomic breeding values for the prediction set in each scenario were the correlation of GEBVs with true breeding values and were used to evaluate the potential loss in accuracy with reduced genotyping. For both S1 and S2 the GEBV accuracies were similar when the prediction set was not genotyped and increased with the addition of low-density genotypes, with the increase larger for S2 than S1.</p> <p>Conclusions</p> <p>Genotype imputation using a long haplotype library and segregation analysis is promising for application in sparsely-genotyped pedigrees. The results of this study suggest that dense genotypes can be imputed for selection candidates with some loss in genomic breeding value accuracy, but with levels of accuracy higher than traditional BLUP estimated breeding values. Accurate genotype imputation would allow for a single low-density SNP panel to be used across traits.</p

Microbiome‑driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions

BACKGROUND: Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored. RESULTS: This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of −0.41±0.12 sd. CONCLUSION: This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-022-01352-6

A Bayesian palaeoenvironmental transfer function model for acidified lakes

A Bayesian approach to palaeoecological environmental reconstruction deriving from the unimodal responses generally exhibited by organisms to an environmental gradient is described. The approach uses Bayesian model selection to calculate a collection of probability-weighted, species-specific response curves (SRCs) for each taxon within a training set, with an explicit treatment for zero abundances. These SRCs are used to reconstruct the environmental variable from sub-fossilised assemblages. The approach enables a substantial increase in computational efficiency (several orders of magnitude) over existing Bayesian methodologies. The model is developed from the Surface Water Acidification Programme (SWAP) training set and is demonstrated to exhibit comparable predictive power to existing Weighted Averaging and Maximum Likelihood methodologies, though with improvements in bias; the additional explanatory power of the Bayesian approach lies in an explicit calculation of uncertainty for each individual reconstruction. The model is applied to reconstruct the Holocene acidification history of the Round Loch of Glenhead, including a reconstruction of recent recovery derived from sediment trap data.The Bayesian reconstructions display similar trends to conventional (Weighted Averaging Partial Least Squares) reconstructions but provide a better reconstruction of extreme pH and are more sensitive to small changes in diatom assemblages. The validity of the posteriors as an apparently meaningful representation of assemblage-specific uncertainty and the high computational efficiency of the approach open up the possibility of highly constrained multiproxy reconstructions

Similarities in drinking behavior of twin's friends: moderation of heritability of alcohol use

Previous research has indicated that friends' drinking may influence alcohol use in adolescents and young adults. We explored whether similarities in the drinking behavior of friends of twins influence the genetic architecture of alcohol use in adolescence and young adulthood. Survey data from The Netherlands Twin Register were available for 1,526 twin pairs aged 16-25 years. We categorized the twin pairs as concordant (both report similar alcohol use in their friends) or discordant for the alcohol use of their friends. Genetic moderator models were tested by carrying out multi-group analyzes in Mplus. Findings showed a significant moderation effect. Genetic factors were more and common environment less important in the explanation of variation in alcohol use in twins discordant for alcohol use of friends than in twins concordant for alcohol use of friend

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Gastroesophageal reflux disease in 2006: The imperfect diagnosis

There continues to be significant controversy related to diagnostic testing for gastroesophageal reflux disease (GERD). Clearly, barium contrast fluoroscopy is superior to any other test in defining the anatomy of the upper gastrointestinal (UGI) tract. Although fluoroscopy can demonstrate gastroesophageal reflux (GER), this observation does not equate to GERD. Fluoroscopy time should not be prolonged to attempt to demonstrate GER during barium contrast radiography. There are no data to justify prolonging fluoroscopy time to perform provocative maneuvers to demonstrate reflux during barium contrast UGI series. Symptoms of GERD may be associated with physiologic esophageal acid exposure measured by intraesophageal pH monitoring, and a significant percentage of patients with abnormal esophageal acid exposure have no or minimal clinical symptoms of reflux. Abnormal acid exposure defined by pH monitoring over a 24-h period does not equate to GERD. In clinical practice presumptive diagnosis of GERD is reasonably assumed by substantial reduction or elimination of suspected reflux symptoms during therapeutic trial of acid reduction therapy