Patterns of Risk of Cancer in Patients with Metal-on-Metal Hip Replacements versus Other Bearing Surface Types: A Record Linkage Study between a Prospective Joint Registry and General Practice Electronic Health Records in England

Background

There are concerns that metal-on-metal hip implants may cause cancer. The objective of this study was to evaluate patterns and timing of risk of cancer in patients with metal-on-metal total hip replacements (THR).

Methods

In a linkage study between the English National Joint Registry (NJR) and the Clinical Practice Research Datalink (CPRD), we selected all THR surgeries (NJR) between 2003 and 2010 (n = 11,540). THR patients were stratified by type of bearing surface. Patients were followed up for cancer and Poisson regression was used to derive adjusted relative rates (RR).

Results

The risk of cancer was similar in patients with hip resurfacing (RR 0.69; 95% Confidence Interval [CI] 0.39–1.22) or other types of bearing surfaces (RR 0.96; 95% CI 0.64–1.43) compared to individuals with stemmed metal-on-metal THR. The pattern of cancer risk over time did not support a detrimental effect of metal hip implants. There was substantial confounding: patients with metal-on-metal THRs used fewer drugs and had less comorbidity.

Conclusions

Metal-on-metal THRs were not associated with an increased risk of cancer. There were substantial baseline differences between the different hip implants, indicating possibility of confounding in the comparisons between different types of THR implants.     

Robust smooth segmentation approach for array CGH data analysis

MOTIVATION: Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach. METHODS: To achieve a robust segmentation, we use a doubly heavy-tailed random-effect model. The first heavy-tailed structure on the errors deals with outliers in the observations, and the second deals with possible jumps in the underlying pattern associated with different segments. We develop a fast and reliable computational procedure based on the iterative weighted least-squares algorithm with band-limited matrix inversion. RESULTS: Using simulated and real data sets, we demonstrate how smoothseg can aid in identification of regions with genomic alteration and in classification of samples. For the real data sets, smoothseg leads to smaller false discovery rate and classification error rate than the circular binary segmentation (CBS) algorithm. In a realistic simulation setting, smoothseg is better than wavelet smoothing and CBS in identification of regions with genomic alterations and better than CBS in classification of samples. For comparative analyses, we demonstrate that segmenting the t-statistics performs better than segmenting the data. AVAILABILITY: The R package smoothseg to perform smooth segmentation is available from http://www.meb.ki.se/~yudpaw.     

Evolutionary duplication of lipo oligochitin-like receptor genes in soybean differentiates their function in cell division and cell invasion

Gene duplication in evolution has long been viewed as a mechanism for functional divergence. We recently cloned two related lipo-oligochitin receptor genes (GmNFR1α and GmNFR1β) in Glycine max (soybean) that allowed the distinction of two nodulation factor (NF) responses during early legume nodule ontogeny, namely invasion of the root hair and concomitant cortical cell divisions. Root-controlled GmNFR1α mutants nod49 and rj1 failed to form curled root hairs, infection threads and nodules but develop subepidermal cortical cell divisions (CCD) and mycorrhizal associations. In contrast GmNFR1β mutant PI437.654 had full symbiotic abilities. However, GmNFR1α mutants formed normal nodules at reduced frequency when inoculated with high Bradyrhizobium titers. The mutation was complemented in Agrobacterium rhizogenes K599 transformed roots using both CaMV 35S and the native GmNFR1α promoters. GmNFR1α may encode a high affinity NF receptor responsible for the entire nodulation cascade while GmNFR1β with lower affinity to NF suffices to induce cell divisions but not early infection events.Key words: gene duplication, LysM receptor kinase, Glycine max L. Merr., nodulation, cell division, cell invasionNodulation and symbiotic nitrogen fixation provide a major conduit for nitrogen into the earth''s biosphere, capable of replacing synthetic fertilizer augmentation of high input food production. With ever-increasing fossil fuel costs needed for fertilizer production, storage, distribution and application, the understanding and concomitant optimization of the natural symbiotic process of plant-bacterium interaction is gaining emphasis.     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Enhancement of carbon dioxide fixation by alteration of illumination duringChlorella vulgaris-Buitenzorg's growth

Alteration of illumination with optimum carbon dioxide fixation-based curve in this research successfully enhanced the CO₂-fixation (q_co2) capability ofChlorella vulgaris Buitenzorg cultivated in a bubble column photo bioreactor. The level of CO₂ fixation was up to 1.91 times that observed from cultivation with intensification of illumination on an optimum growth-based curve. During 144 h of cultivation, alteration of light intensity on an optimum CO₂-fixation-based curve produced a q_CO2 of 6.68 h^?1. Increases in light intensity based on a curve of optimum CO₂-fixation produced a final cell concentration of about 5.78 g/L. Both cultivation methods were carried out under ambient pressure at a temperature of 29°C with a superficial gas velocity of 2.4 m/h (U_G). Cells were grown on Beneck medium in a 1.0 L Bubble Column Photo bioreactor illuminated by aPhillips Halogen Lamp (20 W/12 V/50 Hz). The inlet gas had a carbon dioxide content of 10%.     

Multidimensional local false discovery rate for microarray studies

MOTIVATION: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability. METHODS: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data. RESULTS: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics. AVAILABILITY: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at http://www.meb.ki.se/~yudpaw.     

Genetic structures of the CIMMYT international yield trial targeted to irrigated environments

International yield trials are assembled by CIMMYT to disseminate promising wheat breeding materials worldwide. To assess the genomic structure and linkage disequilibrium (LD) within this germplasm, wheat lines disseminated during 25 years of the Elite Spring Wheat Yield Trial (ESWYT) targeted for irrigated environments of the world were genotyped with the high-throughput Diversity Arrays Technology (DArT) marker system. Analyses of population structure assigned the ESWYT germplasm into five major sub-populations that are shaped by prominent CIMMYT wheat lines and their descendants. Based on genetic distance, we concluded that a constant level of genetic diversity was maintained over the years of ESWYT dissemination. Genetic distance between the individual ESWYT lines significantly increased when the ESWYT were grouped according to the differences in years of ESWYT dissemination, suggesting a systematic change in allele frequencies over time, most probably due to breeding and directional selection. By means of multiple regression analyses, 78 markers displaying a significant change in allele frequency across years were identified and interpreted as an indicator for constant selection. The markers identified were partly associated with grain yield, leaf, stem, and yellow rust and point to key genomic regions for further investigation. Large numbers of adjacent DArT marker pairs showed significant LD across the ESWYT population and within each of the five sub-populations identified. Sub-population differentiation measured by the fixation index and average genetic distance were highly correlated with LD levels, suggesting that the sub-populations themselves explain much of the LD.     

Phylogeography of the eight-barbel loach <Emphasis Type="Italic">Lefua nikkonis</Emphasis> (Cypriniformes: Nemacheilidae): how important were straits in northern Japan as biogeographical barriers?

Many straits in the Japanese archipelago have been proposed as biogeographical boundaries, but there is disagreement regarding their importance as historic barriers against dispersal of terrestrial and freshwater taxa. Mitochondrial DNA haplotype and phylogenetic analyses of Lefua nikkonis, a primary freshwater fish inhabiting northern Japan and descendent from Siberia, revealed that the species is genetically structured within its geographic range, but that two major haplotypes are widely distributed across the Ishikari Lowland of Hokkaido Island as well as across the Tsugaru Strait between Hokkaido and Honshu Islands, two well-known biogeographical boundaries of northern Japan. The two major haplotypes were separated from each other by only one mutational step, and many other haplotypes, including those endemic to the region south of these barriers, have diverged from the major haplotypes, suggesting rapid range expansion and local differentiation. Divergence-time estimates, based on vicariance of the Honshu endemic congener L. echigonia via uplift of the Central Highlands, demonstrated that the southward dispersal of L. nikkonis from Hokkaido Island to Honshu Island occurred less than 0.08–0.19 Mya, suggesting that a land bridge emerged at the Tsugaru Strait during the Riss glaciation. Given that other freshwater taxa crossed the strait earlier (during the Middle Pleistocene), it is likely that land bridges in the strait have repeatedly emerged. The fact that L. nikkonis invaded only the northern part of Honshu, and that many other freshwater species also have the limit of their distribution ranges in this area as well, indicates that a faunal transition zone might persist even without the Tsugaru Strait. Thus, straits and lowlands in northern Japan are likely to have been less important as dispersal barriers to freshwater taxa than is currently thought.     

Decreased expression of Apaf‐1 with progression of melanoma

Defects in apoptotic system may contribute in the pathogenesis and resistance of malignant melanoma cells to chemotherapy. Apoptotic protease‐activating factor‐1 (Apaf‐1) is a cell death effector that acts with cytochrome c and caspase‐9 to mediate apoptosis. Recently it was shown that metastatic melanomas often lose Apaf‐1 and are concomitantly resistant to apoptosis. It is not known, however, whether Apaf‐1 protein is lost during melanoma progression from localized to metastatic tumor. To this end, we evaluated Apaf‐1 protein expression by immunohistochemistry in 10 cases of human nevi, 11 melanomas in situ, 26 primary melanomas and 15 metastases. Significant decreases in Apaf‐1 expression was observed when comparing nevi and melanomas (chi‐square = 33.719; P < 0.0001). Moreover, primary melanomas with greater tumor thickness showed lesser expression of Apaf‐1 (chi‐square = 16.182; P < 0.003). Intriguingly, we were unable to detect Apaf‐1 expression in lesions of metastatic melanomas. These data demonstrated that there is an inverse correlation between Apaf‐1 expression and pathologic stage of melanoma. This suggests that the decreased expression of Apaf‐1 seen in correlation with melanoma progression renders melanoma more resistant to chemotherapy.