Isolation and characterization of novel polymorphic microsatellite loci in large yellow croaker (Larimichthys crocea)

The large yellow croaker (Larimichthys crocea) is one of the largest marine net-cage cultured species in the oceans around China. In the present study, we isolated and characterized 13 polymorphic microsatellite markers from genomic libraries of L. crocea. Loci were screened for 10 wild specimens from 2 sites in southeast of China. All loci were polymorphic. The number of alleles per locus ranged from 2 to 21. The expected heterozygosity ranged from 0.233 to 0.838 and observed heterozygosity ranged from 0.527 to 0.935. Eleven loci were highly informative (polymorphic information content >0.5). Significant deviation from Hardy-Weinberg equilibrium was observed at 3 loci after Bonferroni's correction. The microsatellite loci may be valuable tools for studying the genetic diversity and genetic structure for conservation planning of the fish.

On random-effects meta-analysis

Meta-analysis is widely used to compare and combine the results of multiple independent studies. To account for between-study heterogeneity, investigators often employ random-effects models, under which the effect sizes of interest are assumed to follow a normal distribution. It is common to estimate the mean effect size by a weighted linear combination of study-specific estimators, with the weight for each study being inversely proportional to the sum of the variance of the effect-size estimator and the estimated variance component of the random-effects distribution. Because the estimator of the variance component involved in the weights is random and correlated with study-specific effect-size estimators, the commonly adopted asymptotic normal approximation to the meta-analysis estimator is grossly inaccurate unless the number of studies is large. When individual participant data are available, one can also estimate the mean effect size by maximizing the joint likelihood. We establish the asymptotic properties of the meta-analysis estimator and the joint maximum likelihood estimator when the number of studies is either fixed or increases at a slower rate than the study sizes and we discover a surprising result: the former estimator is always at least as efficient as the latter. We also develop a novel resampling technique that improves the accuracy of statistical inference. We demonstrate the benefits of the proposed inference procedures using simulated and empirical data.

Doubly Robust Learning for Estimating Individualized Treatment with Censored Data

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or survival model, but not both; the method is shown to be Fisher consistent when either model is correct. Furthermore, we establish the convergence rate of the expected survival under the estimated optimal individualized treatment rule to the expected survival under the optimal individualized treatment rule. We illustrate the proposed methods using simulation study and data from a Phase III clinical trial on non-small cell lung cancer.

A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers

Copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) co-exist throughout the human genome and jointly contribute to phenotypic variations. Thus, it is desirable to consider both types of variants, as characterized by allele-specific copy numbers (ASCNs), in association studies of complex human diseases. Current SNP genotyping technologies capture the CNV and SNP information simultaneously via fluorescent intensity measurements. The common practice of calling ASCNs from the intensity measurements and then using the ASCN calls in downstream association analysis has important limitations. First, the association tests are prone to false-positive findings when differential measurement errors between cases and controls arise from differences in DNA quality or handling. Second, the uncertainties in the ASCN calls are ignored. We present a general framework for the integrated analysis of CNVs and SNPs, including the analysis of total copy numbers as a special case. Our approach combines the ASCN calling and the association analysis into a single step while allowing for differential measurement errors. We construct likelihood functions that properly account for case-control sampling and measurement errors. We establish the asymptotic properties of the maximum likelihood estimators and develop EM algorithms to implement the corresponding inference procedures. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to a genome-wide association study of schizophrenia. Extensions to next-generation sequencing data are discussed.

Penalized Q-Learning for Dynamic Treatment Regimens

A dynamic treatment regimen incorporates both accrued information and long-term effects of treatment from specially designed clinical trials. As these trials become more and more popular in conjunction with longitudinal data from clinical studies, the development of statistical inference for optimal dynamic treatment regimens is a high priority. In this paper, we propose a new machine learning framework called penalized Q-learning, under which valid statistical inference is established. We also propose a new statistical procedure: individual selection and corresponding methods for incorporating individual selection within penalized Q-learning. Extensive numerical studies are presented which compare the proposed methods with existing methods, under a variety of scenarios, and demonstrate that the proposed approach is both inferentially and computationally superior. It is illustrated with a depression clinical trial study.

Short-term therapeutic drug monitoring of mycophenolic acid reduces infection: a prospective, single-center cohort study in Chinese living-related kidney transplantation

BACKGROUND

The role of therapeutic drug monitoring (TDM) of mycophenolic acid (MPA) in kidney transplant recipients (KTRs) is not clear. We performed a prospective cohort study to evaluate the efficiency of MPA TDM in the Chinese population.

METHODS

A total of 183 living-related KTRs were studied; 101 KTRs received controlled-dose mycophenolate mofetil (MMF) (the CD group), and 82 patients received fixed-dose MMF (the FD group). MPA exposure was measured at days 3, 7, 14, and 30 in the CD group, and at day 30 in the FD group. The primary endpoint was treatment failure (a composite of acute rejection, graft loss, death, or MMF discontinuation) at 12 months post transplantation.

RESULTS

In the CD group, with a starting MMF dose of 2 g/day, approximately 35% of patients had high MPA levels, which were >60 mg × h/L, and mean MPA levels were 59.17 mg × h/L and 61.38 mg × h/L for the CD and FD groups, respectively (P = 0.588). After adjusting MMF dose, MPA exposures in the CD group at day 30 were lower than those in the FD group at day 30 (54.06 vs. 61.38, P = 0.004). At month 12, the CD group had fewer infections (16.8% vs. 31.7%, P = 0.018) with no difference in treatment failure, acute rejection, diarrhea, or anemia.

CONCLUSIONS

KTRs can benefit from short-term TDM of MPA in reducing infection, without increasing acute rejection.

Long-term outcome of HLA-haploidentical hematopoietic SCT without in vitro T-cell depletion for adult severe aplastic anemia after modified conditioning and supportive therapy

HLA-haploidentical hematopoietic SCT (HSCT) is an option for severe aplastic anemia (SAA) patients. Here, we evaluated the outcomes of 26 adult-SAA patients who received HLA-haploidentical HSCT in five transplant centers in southwestern China. Most of the patients in this study failed prior therapy and were transfused heavily before the transplantation. The patients received fludarabine+cyclophosphamide+antithymocyte globulin as conditioning regimens and then unmanipulated peripheral blood plus marrow transplantation. Micafungin, i.v. Ig and recombinant human TPO were used for post-grafting infection prevention and supportive care. Of 26 patients, 25 achieved engraftment at a median of 13 days (range, 11-19 days) after HSCT. One of 25 patients experienced graft rejection and did not achieve sustained engraftment after second HSCT. Therefore, the final engraftment rate was 92.3%. Three of 25 (12%) patients developed acute GVHD, 10 of 25 (40%) patients developed chronic GVHD (9 with limited whereas the other with extensive). The OS rate was 84.6% and the average follow-up time was 1313.2 (738-2005) days for surviving patients. This encouraging result suggests that HLA-haploidentical HSCT is an effective therapeutic option for adults with acquired SAA if an HLA-identical donor is not available.

Role of monokine induced by interferon-γ in liver injury induced by hepatitis B virus in mice

The chemokine monokine induced by interferon-γ (Mig) is involved in the recruitment of inflammatory cells and liver injury during hepatitis B virus (HBV) infection. HBV protein X contributes to Mig expression in vitro by activation of nuclear factor (NF)-κB; however, the molecular mechanisms by which HBV induces Mig expression in vivo are unknown. In this paper, we established a mouse model for HBV study by tail vein injection of HBV genome-containing adenovirus vectors. Host immune response to the secreted hepatitis B surface antigen and e antigen was detected and serum alanine aminotransferase (ALT) was elevated at different time points. We also demonstrated that peripheral and intrahepatic Mig expression was increased after Ad-HBV infection. This was followed by inflammatory cell migration and formation of inflammatory foci in the liver. In addition, NF-κB p65 subunit translocated from the cytoplasm to the nucleus, and phosphoinositide 3-kinase/Akt, extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) were to some extent phosphorylated after HBV injection. Following tail vein injection of Mig siRNA/in vivo-jetPEI-Gal complex, Mig expression was partially suppressed, inflammatory cell migration was inhibited, serum level of ALT were reduced. In conclusion, through NF-κB activation, HBV induced Mig expression in vivo, which recruited peripheral inflammatory cells to the liver and resulted in liver damage. Phosphorylation of phosphoinositide 3-kinase/Akt, ERK and JNK but not p38 might involved in the molecular mechanisms underlying HBV induced Mig expression in vivo.

Correcting for Population Stratification in Genomewide Association Studies

Genomewide association studies have become the primary tool for discovering the genetic basis of complex human diseases. Such studies are susceptible to the confounding effects of population stratification, in that the combination of allele-frequency heterogeneity with disease-risk heterogeneity among different ancestral subpopulations can induce spurious associations between genetic variants and disease. This article provides a statistically rigorous and computationally feasible solution to this challenging problem of unmeasured confounders. We show that the odds ratio of disease with a genetic variant is identifiable if and only if the genotype is independent of the unknown population substructure conditional on a set of observed ancestry-informative markers in the disease-free population. Under this condition, the odds ratio of interest can be estimated by fitting a semiparametric logistic regression model with an arbitrary function of a propensity score relating the genotype probability to ancestry-informative markers. Approximating the unknown function of the propensity score by B-splines, we derive a consistent and asymptotically normal estimator for the odds ratio of interest with a consistent variance estimator. Simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. An application to the well-known Wellcome Trust Case-Control Study is presented. Supplemental materials are available online.

Effect of myofiber characteristics and thickness of perimysium and endomysium on meat tenderness of chickens

The objective of the present study was to evaluate the role of myofiber characteristics and the thickness of 2 major muscle membranes, perimysium and endomysium, in determining the breast meat tenderness of chickens. Birds from 2 breeds (White Leghorn and a line of broiler) were chosen. Chicks were sexed and wing-banded at hatch and were grown in separate cages in a single house. Sixty broilers and 60 White Leghorns were harvested at 6 wk of age, respectively, whereas another 60 White Leghorns were slaughtered at 18 wk of age. An equal number of males and females was maintained for each group. Body weight, breast muscle weight, pH, drip loss, cooking loss, Warner-Bratzler shear force value (SFV), total energy of shear force, fiber diameter, sarcomere length, myofiber density, and the thickness of endomysium and perimysium of the breast were determined for each bird. At 6 wk of age, histological examination indicated that the size of myofiber and thickness of endomysium and perimysium of broilers were larger than that those of White Leghorns (P < 0.01), whereas the SFV, drip loss, and cooking loss of broilers were smaller (P < 0.01). A comparison between the White Leghorns at 18 wk and the broilers at 6 wk, which were at similar BW but different ages, showed that the breast muscle weight of broilers was larger (P < 0.01) than that of White Leghorns. For breast muscle, the endomysium of broilers at 6 wk was thicker than that of White Leghorns at 18 wk (P < 0.01), whereas the perimysium was thinner (P < 0.01). The SFV, drip loss, and the cooking loss of broilers were smaller than those of White Leghorns at similar BW (P < 0.01). Meat tenderness was negatively correlated with myofiber density (-0.27) and the thickness of endomysium (-0.29) and positively correlated with the thickness of perimysium (0.20). It is suggested that muscle membrane should be considered in evaluating meat tenderness of the chicken.

On the relative efficiency of using summary statistics versus individual-level data in meta-analysis

Meta-analysis is widely used to synthesize the results of multiple studies. Although meta-analysis is traditionally carried out by combining the summary statistics of relevant studies, advances in technologies and communications have made it increasingly feasible to access the original data on individual participants. In the present paper, we investigate the relative efficiency of analyzing original data versus combining summary statistics. We show that, for all commonly used parametric and semiparametric models, there is no asymptotic efficiency gain by analyzing original data if the parameter of main interest has a common value across studies, the nuisance parameters have distinct values among studies, and the summary statistics are based on maximum likelihood. We also assess the relative efficiency of the two methods when the parameter of main interest has different values among studies or when there are common nuisance parameters across studies. We conduct simulation studies to confirm the theoretical results and provide empirical comparisons from a genetic association study.

A general framework for studying genetic effects and gene-environment interactions with missing data

Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. We consider 3 major study designs-cross-sectional, case-control, and cohort designs-and construct appropriate likelihood functions for all common phenotypes (e.g. case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Expectation-Maximization (EM) algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Illustration with a genome-wide association study of lung cancer is provided.

Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data

To identify genetic variants with modest effects on complex human diseases, a growing number of networks or consortia are created for sharing data from multiple genome-wide association studies on the same disease or related disorders. A central question in this enterprise is whether to obtain summary results or individual participant data from relevant studies. We show theoretically and numerically that meta-analysis of summary results is statistically as efficient as joint analysis of individual participant data (provided that both analyses are performed properly under the same modeling assumptions). We illustrate this equivalence with case-control data from the Finland-United States Investigation of NIDDM Genetics (FUSION) study. Collating only summary results will increase the number and representativeness of available studies, simplify data collection and analysis, reduce resource utilization, and accelerate discovery.

The effect of exercise on the cerebral vasculature of healthy aged subjects as visualized by MR angiography

BACKGROUND AND PURPOSE

Prior studies suggest that aerobic exercise may reduce both the brain atrophy and the decline in fractional anisotropy observed with advancing age. It is reasonable to hypothesize that exercise-induced changes to the vasculature may underlie these anatomic differences. The purpose of this blinded study was to compare high-activity and low-activity healthy elderly volunteers for differences in the cerebrovasculature as calculated from vessels extracted from noninvasive MR angiograms (MRAs).

MATERIALS AND METHODS

Fourteen healthy elderly subjects underwent MRA. Seven subjects reported a high level of aerobic activity (64 +/- 5 years of age; 5 men, 2 women) and 7, a low activity level (68 +/- 6 years of age; 5 women, 2 men). Following vessel segmentation from MRA by an individual blinded to subject activity level, quantitative measures of vessel number, radius, and tortuosity were calculated and histogram analysis of vessel number and radius was performed.

RESULTS

Aerobically active subjects exhibited statistically significant reductions in vessel tortuosity and an increased number of small vessels compared with less active subjects.

CONCLUSIONS

Aerobic activity in elderly subjects is associated with lower vessel tortuosity values and an increase in the number of small-caliber vessels. It is possible that an aerobic exercise program may contribute to healthy brain aging. MRA offers a noninvasive approach to visualizing the cerebral vasculature and may prove useful in future longitudinal investigations.

Proper analysis of secondary phenotype data in case-control association studies

Case-control association studies often collect extensive information on secondary phenotypes, which are quantitative or qualitative traits other than the case-control status. Exploring secondary phenotypes can yield valuable insights into biological pathways and identify genetic variants influencing phenotypes of direct interest. All publications on secondary phenotypes have used standard statistical methods, such as least-squares regression for quantitative traits. Because of unequal selection probabilities between cases and controls, the case-control sample is not a random sample from the general population. As a result, standard statistical analysis of secondary phenotype data can be extremely misleading. Although one may avoid the sampling bias by analyzing cases and controls separately or by including the case-control status as a covariate in the model, the associations between a secondary phenotype and a genetic variant in the case and control groups can be quite different from the association in the general population. In this article, we present novel statistical methods that properly reflect the case-control sampling in the analysis of secondary phenotype data. The new methods provide unbiased estimation of genetic effects and accurate control of false-positive rates while maximizing statistical power. We demonstrate the pitfalls of the standard methods and the advantages of the new methods both analytically and numerically. The relevant software is available at our website.

Cumulative life course and adult socioeconomic status and markers of inflammation in adulthood

OBJECTIVE

To examine the association between cumulative life course and adult socioeconomic status (SES) and adult levels of inflammatory risk markers (fibrinogen, white blood cell count (WBC), C-reactive protein (CRP), von Willebrand factor (vWF) and an overall inflammatory score).

DESIGN

Retrospective cohort study.

SETTING

12,681 white and African-American participants in the Atherosclerosis Risk in Communities (ARIC) study and two ancillary studies.

METHODS

Participants provided information on SES and place of residence in childhood and young (30-40 years) and mature (45+) adulthood. Residences were linked to census data for neighbourhood SES information. Multiple imputation (MI) was used for missing data. Linear regression and adjusted geometric means were used to estimate the effects of SES on inflammatory risk marker levels.

RESULTS

Graded, statistically significant associations were observed between greater cumulative life-course exposure to low education and social class and elevated levels of fibrinogen and WBC among white participants. Stronger graded, statistically significant associations were observed between low adult education, social class and neighbourhood SES and elevated inflammatory levels. Associations were weaker and less consistent in African-Americans. Covariate adjustment attenuated results but many associations remained strong.

CONCLUSIONS

Our results suggest that cumulative exposure to adverse SES conditions across the life course and low adult SES are associated with an elevated systemic inflammatory burden in adulthood. Chronic systemic inflammation may be one pathway linking low life-course SES and elevated cardiovascular disease risk.