Effects of the active amyloid beta immunotherapy CAD106 on PET measurements of amyloid plaque deposition in cognitively unimpaired APOE ε4 homozygotes.

INTRODUCTION: Alzheimer's Prevention Initiative Generation Study 1 evaluated amyloid beta (Aß) active immunotherapy (vaccine) CAD106 and BACE-1 inhibitor umibecestat in cognitively unimpaired 60- to 75-year-old participants at genetic risk for Alzheimer's disease (AD). The study was reduced in size and terminated early. Results from the CAD106 cohort are presented. METHODS: Sixty-five apolipoprotein E ε4 homozygotes with/without amyloid deposition received intramuscular CAD106 450 µg (n = 42) or placebo (n = 23) at baseline; Weeks 1, 7, 13; and quarterly; 51 of them had follow-up Aß positron emission tomography (PET) scans at 18 to 24 months. RESULTS: CAD106 induced measurable serum Aß immunoglobulin G titers in 41/42 participants, slower rates of Aß plaque accumulation (mean [standard deviation] annualized change from baseline in amyloid PET Centiloid: -0.91[5.65] for CAD106 versus 8.36 [6.68] for placebo; P < 0.001), and three amyloid-related imaging abnormality cases (one symptomatic). DISCUSSION: Despite early termination, these findings support the potential value of conducting larger prevention trials of Aß active immunotherapies in individuals at risk for AD. HIGHLIGHTS: This was the first amyloid-lowering prevention trial in persons at genetic risk of late-onset Alzheimer's disease (AD). Active immunotherapy targeting amyloid (CAD106) was tested in this prevention trial. CAD106 significantly slowed down amyloid plaque deposition in apolipoprotein E homozygotes. CAD106 was generally safe and well tolerated, with only three amyloid-related imaging abnormality cases (one symptomatic). Such an approach deserves further evaluation in larger AD prevention trials.

The sum of standardized residuals: Goodness-of-fit test for binary response models.

We propose a new goodness-of-fit statistic for evaluating generalized linear models with binary responses on the basis of the sum of standardized residuals. We derive the asymptotic distribution of the sum of standardized residuals statistic and argue that, despite its relative simplicity, it typically outperforms many of the more sophisticated currently used goodness-of-fit statistics.

A case-control design for testing and estimating epigenetic effects on complex diseases.

Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case-control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case-control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases.

Shape mapping: genetic mapping meets geometric morphometrics.

Knowledge about biological shape has important implications in biology and biomedicine, but the underlying genetic mechanisms for shape variation have not been well studied. Statistical models play a pivotal role in mapping specific quantitative trait loci (QTLs) that contribute to biological shape and its developmental trajectories. We describe and assess a statistical framework for shape gene identification that incorporates shape and image analysis into a mixture-model framework for QTL mapping. Statistical parameters that define genotype-specific differences in biological shape are estimated by implementing statistical and computational algorithms. A state-of-the-art procedure is described to examine the control patterns of specific QTLs on the origin, properties and functions of biological shape. The statistical framework described will help to address many integrative biological and genetic questions and challenges in shape variation faced by the life sciences community.

Structural mapping: how to study the genetic architecture of a phenotypic trait through its formation mechanism.

Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation.

A model for family-based case-control studies of genetic imprinting and epistasis.

Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases.

A quantitative genetic and epigenetic model of complex traits.

BACKGROUND: Despite our increasing recognition of the mechanisms that specify and propagate epigenetic states of gene expression, the pattern of how epigenetic modifications contribute to the overall genetic variation of a phenotypic trait remains largely elusive. RESULTS: We construct a quantitative model to explore the effect of epigenetic modifications that occur at specific rates on the genome. This model, derived from, but beyond, the traditional quantitative genetic theory that is founded on Mendel's laws, allows questions concerning the prevalence and importance of epigenetic variation to be incorporated and addressed. CONCLUSIONS: It provides a new avenue for bringing chromatin inheritance into the realm of complex traits, facilitating our understanding of the means by which phenotypic variation is generated.

Systems mapping of HIV-1 infection.

Mathematical models of viral dynamics in vivo provide incredible insights into the mechanisms for the nonlinear interaction between virus and host cell populations, the dynamics of viral drug resistance, and the way to eliminate virus infection from individual patients by drug treatment. The integration of these mathematical models with high-throughput genetic and genomic data within a statistical framework will raise a hope for effective treatment of infections with HIV virus through developing potent antiviral drugs based on individual patients' genetic makeup. In this opinion article, we will show a conceptual model for mapping and dictating a comprehensive picture of genetic control mechanisms for viral dynamics through incorporating a group of differential equations that quantify the emergent properties of a system.