Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Examining the Effect of Genes on Depression as Mediated by Smoking and Modified by Sex.

Depression is heritable, differs by sex, and has environmental risk factors such as cigarette smoking. However, the effect of single nucleotide polymorphisms (SNPs) on depression through cigarette smoking and the role of sex is unclear. In order to examine the association of SNPs with depression and smoking in the UK Biobank with replication in the COPDGene study, we used counterfactual-based mediation analysis to test the indirect or mediated effect of SNPs on broad depression through the log of pack-years of cigarette smoking, adjusting for age, sex, current smoking status, and genetic ancestry (via principal components). In secondary analyses, we adjusted for age, sex, current smoking status, genetic ancestry (via principal components), income, education, and living status (urban vs. rural). In addition, we examined sex-stratified mediation models and sex-moderated mediation models. For both analyses, we adjusted for age, current smoking status, and genetic ancestry (via principal components). In the UK Biobank, rs6424532 [LOC105378800] had a statistically significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 4.0 × 10-4) among all participants and a marginally significant indirect effect among females (p = 0.02) and males (p = 4.0 × 10-3). Moreover, rs10501696 [GRM5] had a marginally significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 0.01) among all participants and a significant indirect effect among females (p = 2.2 × 10-3). In the secondary analyses, the sex-moderated indirect effect was marginally significant for rs10501696 [GRM5] on broad depression through the log of pack-years of cigarette smoking (p = 0.01). In the COPDGene study, the effect of an SNP (rs10501696) in GRM5 on depressive symptoms and medication was mediated by log of pack-years (p = 0.02); however, no SNPs had a sex-moderated mediated effect on depressive symptoms. In the UK Biobank, we found SNPs in two genes [LOC105378800, GRM5] with an indirect effect on broad depression through the log of pack-years of cigarette smoking. In addition, the indirect effect for GRM5 on broad depression through smoking may be moderated by sex. These results suggest that genetic regions associated with broad depression may be mediated by cigarette smoking and this relationship may be moderated by sex.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries.

INTRODUCTION: Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS: We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS: We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION: We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Fast computation of the eigensystem of genomic similarity matrices.

The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning.

BACKGROUND: Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (ß-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. METHODS: We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants (n = 205 controls, n = 546 mild cognitive impairment, n = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. RESULTS: Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. CONCLUSIONS: These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.

A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data.

Background: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical powerResults: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content.Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.

Infiltrating CD8+ T cells exacerbate Alzheimer's disease pathology in a 3D human neuroimmune axis model.

Brain infiltration of peripheral immune cells and their interactions with brain-resident cells may contribute to Alzheimer's disease (AD) pathology. To examine these interactions, in the present study we developed a three-dimensional human neuroimmune axis model comprising stem cell-derived neurons, astrocytes and microglia, together with peripheral immune cells. We observed an increase in the number of T cells (but not B cells) and monocytes selectively infiltrating into AD relative to control cultures. Infiltration of CD8+ T cells into AD cultures led to increased microglial activation, neuroinflammation and neurodegeneration. Using single-cell RNA-sequencing, we identified that infiltration of T cells into AD cultures led to induction of interferon-γ and neuroinflammatory pathways in glial cells. We found key roles for the C-X-C motif chemokine ligand 10 (CXCL10) and its receptor, CXCR3, in regulating T cell infiltration and neuronal damage in AD cultures. This human neuroimmune axis model is a useful tool to study the effects of peripheral immune cells in brain disease.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Zinc finger protein 33B demonstrates sex interaction with atopy-related markers in childhood asthma.

BACKGROUND: Sex differences related to immune responses can influence atopic manifestations in childhood asthma. While genome-wide association studies have investigated a sex-specific genetic architecture of the immune response, gene-by-sex interactions have not been extensively analysed for atopy-related markers including allergy skin tests, IgE and eosinophils in asthmatic children. METHODS: We performed a genome-wide gene-by-sex interaction analysis for atopy-related markers using whole-genome sequencing data based on 889 trios from the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) and 284 trios from the Childhood Asthma Management Program (CAMP). We also tested the findings in UK Biobank participants with self-reported childhood asthma. Furthermore, downstream analyses in GACRS integrated gene expression to disentangle observed associations. RESULTS: Single nucleotide polymorphism (SNP) rs1255383 at 10q11.21 demonstrated a genome-wide significant gene-by-sex interaction (pinteraction=9.08×10-10) for atopy (positive skin test) with opposite direction of effects between females and males. In the UK Biobank participants with a history of childhood asthma, the signal was consistently observed with the same sex-specific effect directions for high eosinophil count (pinteraction=0.0058). Gene expression of ZNF33B (zinc finger protein 33B), located at 10q11.21, was moderately associated with atopy in girls, but not in boys. CONCLUSIONS: We report SNPs in/near a zinc finger gene as novel sex-differential loci for atopy-related markers with opposite effect directions in females and males. A potential role for ZNF33B should be studied further as an important driver of sex-divergent features of atopy in childhood asthma.

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest.

As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

PILRA polymorphism modifies the effect of APOE4 and GM17 on Alzheimer's disease risk.

PILRA (rs1859788 A > G) has been suggested to be a protective variant for Alzheimer's disease (AD) and is an entry co-receptor for herpes simplex virus-1. We conducted a nested case-control study of 360 1:1-matched AD subjects. Interactions between the PILRA-A allele, APOE risk variants (ε3/ε4 or ε4/ε4) and GM17 for AD risk were modelled. The associations were cross-validated using two independent whole-genome sequencing datasets. We found negative interactions between PILRA-A and GM17 (OR 0.72, 95% CI 0.52-1.00) and between PILRA-A and APOE risk variants (OR 0.56, 95% CI 0.32-0.98) in the discovery dataset. In the replication cohort, a joint effect of PILRA and PILRA × GM 17/17 was observed for the risk of developing AD (p .02). Here, we report a negative effect modification by PILRA on APOE and GM17 high-risk variants for future AD risk in two independent datasets. This highlights the complex genetics of AD.

Most Pathways Can Be Related to the Pathogenesis of Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder. The relative contribution of the numerous underlying functional mechanisms is poorly understood. To comprehensively understand the context and distribution of pathways that contribute to AD, we performed text-mining to generate an exhaustive, systematic assessment of the breadth and diversity of biological pathways within a corpus of 206,324 dementia publication abstracts. A total of 91% (325/335) of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways have publications containing an association via at least 5 studies, while 63% of pathway terms have at least 50 studies providing a clear association with AD. Despite major technological advances, the same set of top-ranked pathways have been consistently related to AD for 30 years, including AD, immune system, metabolic pathways, cholinergic synapse, long-term depression, proteasome, diabetes, cancer, and chemokine signaling. AD pathways studied appear biased: animal model and human subject studies prioritize different AD pathways. Surprisingly, human genetic discoveries and drug targeting are not enriched in the most frequently studied pathways. Our findings suggest that not only is this disorder incredibly complex, but that its functional reach is also nearly global. As a consequence of our study, research results can now be assessed in the context of the wider AD literature, supporting the design of drug therapies that target a broader range of mechanisms. The results of this study can be explored at www.adpathways.org.

Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2.

Alzheimer's disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency <0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values <10-6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (pmeta = 4.74 × 10-8). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (pmeta = 1 × 10-6). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants.

A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data.

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the "Lassosum" penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer's disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels.

INTRODUCTION: Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. METHODS: We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. RESULTS: We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1. DISCUSSION: Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development.

INTRODUCTION: Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permits genome-wide analyses to identify rare variants contributing to AD risk. METHODS: We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family-based WGS-based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals. RESULTS: We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2. DISCUSSION: Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of the exome.

An APP ectodomain mutation outside of the Aß domain promotes Aß production in vitro and deposition in vivo.

Familial Alzheimer's disease (FAD)-linked mutations in the APP gene occur either within the Aß-coding region or immediately proximal and are located in exons 16 and 17, which encode Aß peptides. We have identified an extremely rare, partially penetrant, single nucleotide variant (SNV), rs145081708, in APP that corresponds to a Ser198Pro substitution in exon 5. We now report that in stably transfected cells, expression of APP harboring the S198P mutation (APPS198P) leads to elevated production of Aß peptides by an unconventional mechanism in which the folding and exit of APPS198P from the endoplasmic reticulum is accelerated. More importantly, coexpression of APP S198P and the FAD-linked PS1ΔE9 variant in the brains of male and female transgenic mice leads to elevated steady-state Aß peptide levels and acceleration of Aß deposition compared with age- and gender-matched mice expressing APP and PS1ΔE9. This is the first AD-linked mutation in APP present outside of exons 16 and 17 that enhances Aß production and deposition.

Estimating the effective sample size in association studies of quantitative traits.

The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.