Longitudinal data analysis for genetic studies in the whole-genome sequencing era

Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model

In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits.

Multiplexed Simian Immunodeficiency Virus-Specific Paired RNA-Guided Cas9 Nickases Inactivate Proviral DNA

Human and simian immunodeficiency virus (HIV and SIV) infections establish lifelong reservoirs of cells harboring an integrated proviral genome. Genome editing CRISPR-associated Cas9 nucleases, combined with SIV-specific guiding RNA (gRNA) molecules, inactivate integrated provirus DNA in vitro and in animal models. We generated RNA-guided Cas9 nucleases (RGNu) and nickases (RGNi) targeting conserved SIV regions with no homology in the human or rhesus macaque genome. Assays in cells cotransfected with SIV provirus and plasmids coding for RGNus identified SIV long terminal repeat (LTR), trans-activation response (TAR) element, and ribosome slip site (RSS) regions as the most effective at virus suppression; RGNi targeting these regions inhibited virus production significantly. Multiplex plasmids that coexpressed these three RGNu (Nu3), or six (three pairs) RGNi (Ni6), were more efficient at virus suppression than any combination of individual RGNu and RGNi plasmids. Both Nu3 and Ni6 plasmids were tested in lymphoid cells chronically infected with SIVmac239, and whole-genome sequencing was used to determine on- and off-target mutations. Treatment with these all-in-one plasmids resulted in similar levels of mutations of viral sequences from the cellular genome; Nu3 induced indels at the 3 SIV-specific sites, whereas for Ni6 indels were present at the LTR and TAR sites. Levels of off-target effects detected by two different algorithms were indistinguishable from background mutations. In summary, we demonstrate that Cas9 nickase in association with gRNA pairs can specifically eliminate parts of the integrated provirus DNA; also, we show that careful design of an all-in-one plasmid coding for 3 gRNAs and Cas9 nuclease inhibits SIV production with undetectable off-target mutations, making these tools a desirable prospect for moving into animal studies. IMPORTANCE Our approach to HIV cure, utilizing the translatable SIV/rhesus macaque model system, aims at provirus inactivation and its removal with the least possible off-target side effects. We developed single molecules that delivered either three truncated SIV-specific gRNAs along with Cas9 nuclease or three pairs of SIV-specific gRNAs (six individual gRNAs) along with Cas9 nickase to enhance efficacy of on-target mutagenesis. Whole-genome sequencing demonstrated effective SIV sequence mutation and inactivation and the absence of demonstrable off-target mutations. These results open the possibility to employ Cas9 variants that introduce single-strand DNA breaks to eliminate integrated proviral DNA.

Modeling gene-environment interactions in longitudinal family studies: a comparison of methods and their application to the association between the IGF pathway and childhood obesity

BACKGROUND

The interactive effect of the IGF pathway genes with the environment may contribute to childhood obesity. Such gene-environment interactions can take on complex forms. Detecting those relationships using longitudinal family studies requires simultaneously accounting for correlations within individuals and families.

METHODS

We studied three methods for detecting interaction effects in longitudinal family studies. The twin model and the nonparametric partition-based score test utilized individual outcome averages, whereas the linear mixed model used all available longitudinal data points. Simulation experiments were performed to evaluate the methods' power to detect different gene-environment interaction relationships. These methods were applied to the Quebec Newborn Twin Study data to test for interaction effects between the IGF pathway genes (IGF-1, IGFALS) and environmental factors (physical activity, daycare attendance and sleep duration) on body mass index outcomes.

RESULTS

For the simulated data, the twin model with the mean time summary statistic yielded good performance overall. Modelling an interaction as linear when the true model had a different relationship influenced power; for certain non-linear interactions, none of the three methods were effective. Our analysis of the IGF pathway genes showed suggestive association for the joint effect of IGF-1 variant at position 102,791,894 of chromosome 12 and physical activity. However, this association was not statistically significant after multiple testing correction.

CONCLUSIONS

The analytical approaches considered in this study were not robust to different gene-environment interactions. Methodological innovations are needed to improve the current methods' performances for detecting non-linear interactions. More studies are needed in order to better understand the IGF pathway's role in childhood obesity development.

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

Longitudinal SNP-set association analysis of quantitative phenotypes

Many genetic epidemiological studies collect repeated measurements over time. This design not only provides a more accurate assessment of disease condition, but allows us to explore the genetic influence on disease development and progression. Thus, it is of great interest to study the longitudinal contribution of genes to disease susceptibility. Most association testing methods for longitudinal phenotypes are developed for single variant, and may have limited power to detect association, especially for variants with low minor allele frequency. We propose Longitudinal SNP-set/sequence kernel association test (LSKAT), a robust, mixed-effects method for association testing of rare and common variants with longitudinal quantitative phenotypes. LSKAT uses several random effects to account for the within-subject correlation in longitudinal data, and allows for adjustment for both static and time-varying covariates. We also present a longitudinal trait burden test (LBT), where we test association between the trait and the burden score in linear mixed models. In simulation studies, we demonstrate that LBT achieves high power when variants are almost all deleterious or all protective, while LSKAT performs well in a wide range of genetic models. By making full use of trait values from repeated measures, LSKAT is more powerful than several tests applied to a single measurement or average over all time points. Moreover, LSKAT is robust to misspecification of the covariance structure. We apply the LSKAT and LBT methods to detect association with longitudinally measured body mass index in the Framingham Heart Study, where we are able to replicate association with a circadian gene NR1D2.

Constrained multivariate association with longitudinal phenotypes

BACKGROUND

The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (>0.001) for SBP variability and a known gene-centric kernel (MAP4)-based test under the GAW19 simulation model across 200 replicates.

RESULTS

When compared to results using two time points, the constrained method utilizing all 3 time points increased power to detect association. Averaging SBP was equally effective when the variant has a large effect on the phenotype, but less powerful for variants with lower effect sizes. However, averaging SBP was far more effective than either the constrained or unconstrained approaches when using a gene-centric kernel-based test.

CONCLUSION

We determined that this constrained multivariate approach improves genetic signal over the bivariate method. However, this method is still only effective in those variants that explain a moderate to large proportion of the phenotypic variance but is not as effective for gene-centric tests.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

Longitudinal analytical approaches to genetic data

BACKGROUND

Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure-related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.

RESULTS

Two analytical methods were applied to the GAW19: GWA using real phenotypic data, and one approach to WGS using 200 simulated replicates. The first GWA approach applied a GEE-based model to identify gene-based associations with 4 derived hypertension phenotypes. This GEE model identified 1 significant locus, GRM7, which passed multiple test corrections for 2 hypertension-derived traits. The second GWA approach employed the LME to estimate genetic associations with systolic blood pressure (SBP) change trajectories identified using LCGM. This LCGM method identified 5 SBP trajectories and association analyses identified a genome-wide significant locus, near ATOX1 (p = 1.0E(-8)). Finally, a third VC-based model using WGS and simulated SBP phenotypes that constrained the β coefficient for a genetic variant across each time point was calculated and compared to an unconstrained approach. This constrained VC approach demonstrated increased power for WGS variants of moderate effect, but when larger genetic effects were present, averaging across time points was as effective.

CONCLUSION

In this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed techniques under alternative conditions for longitudinal genetic association. We conclude that these approaches when appropriately applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and standard error;

Generalization of Rare Variant Association Tests for Longitudinal Family Studies

Given the functional relevance of many rare variants, their identification is frequently critical for dissecting disease etiology. Functional variants are likely to be aggregated in family studies enriched with affected members, and this aggregation increases the statistical power to detect rare variants associated with a trait of interest. Longitudinal family studies provide additional information for identifying genetic and environmental factors associated with disease over time. However, methods to analyze rare variants in longitudinal family data remain fairly limited. These methods should be capable of accounting for different sources of correlations and handling large amounts of sequencing data efficiently. To identify rare variants associated with a phenotype in longitudinal family studies, we extended pedigree-based burden (BT) and kernel (KS) association tests to genetic longitudinal studies. Generalized estimating equation (GEE) approaches were used to generalize the pedigree-based BT and KS to multiple correlated phenotypes under the generalized linear model framework, adjusting for fixed effects of confounding factors. These tests accounted for complex correlations between repeated measures of the same phenotype (serial correlations) and between individuals in the same family (familial correlations). We conducted comprehensive simulation studies to compare the proposed tests with mixed-effects models and marginal models, using GEEs under various configurations. When the proposed tests were applied to data from the Diabetes Heart Study, we found exome variants of POMGNT1 and JAK1 genes were associated with type 2 diabetes.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

Apolipoprotein E4 association with metabolic syndrome depends on body fatness

BACKGROUND AND AIMS

The human Apolipoprotein E (APOE) gene is polymorphic. The APOE*4 allele is a risk factor for cardiovascular disease and could contribute to the development of the metabolic syndrome (MetS) as it may affect all MetS components. We hypothesize that the common APOE4 polymorphism differentially regulates MetS risk and that this association might be modulated by body fatness.

METHODS & RESULTS

We used body mass index (BMI) as surrogate of fatness and cross-sectionally studied the prevalence of MetS in 4408 middle-aged men of the Aragon Workers Health Study (AWHS). Our analysis revealed i) a gene dose-dependent association between APOE*4 allele and increased risk for MetS, ii) this association primarily derived from the overweight subjects. For these individuals, the MetS risk was higher in APOE*4 carriers than in non-carriers (Odds Ratio = 1.31; 95% CI, 1.03-1.67). Additionally, we examined 3908 healthy young individuals from the Coronary Artery Risk Development in Young Adults (CARDIA) cohort, followed-up for 25 years. Compared with APOE*4 non-carriers, APOE*4 presence significantly increased the risk of developing MetS (Hazard Ratio, 1.12; 95% CI, 1.00-1.26). Again, an interplay between APOE*4 and the longitudinal development of fatness towards the onset of MetS occurred throughout the study. For individuals with BMI gain below the median, the cumulative onset rate of MetS was significantly higher in APOE*4 carriers than in the non-carriers (HR, 1.29; 95% CI, 1.07-1.55).

CONCLUSIONS

Carrying APOE*4 alleles increases MetS in a dose-dependent manner, characterizing individual's APOE genotype might help identify at-risk subjects for preventive intervention.