Machine learning approaches for high-dimensional genome-wide association studies

Formålet med Genome-wide association studies (GWAS) er å finne statistiske sammenhenger mellom genetiske varianter og egenskaper av interesser. De genetiske variantene som forklarer mye av variasjonene i genomfattende genekspresjoner kan medføre konfunderende analyser av kvantitative egenskaper ved ekspresjonsplasseringer (eQTL). For å betrakte konfunderende faktorene, presenterte vi LVREML-metoden i artikkel I, en metode som er konseptuelt analogt med å estimere faste og tilfeldige effekter i Lineære Blandede modeller (LMM). Vi viste at de latente variablene med “Maximum likelihood” alltid kan velges ortogonalt til de kjente faktorene (som genetiske variasjoner). Dette indikerer at “Maximum likelihood” variablene forklarer utvalgsvariansene som ikke allerede er forklart av de genetiske variantene i modellen. For å kartlegge hvilke egenskaper som påvirkes av de identifiserte genetiske variantene, må vi reversere den funksjonelle relasjonen mellom genotyper og egenskaper. I denne sammenhengen er en “multi-trait” metode mer fordelaktige enn å studere egenskapene individuelt. “Multi-trait”-metoden drar nytte av økt kapasitet som følge av å vurdere kovarianser på tvers av egenskaper, og redusert multiple tester, fordi det trengs en enkelt test for å teste for sammenhenger til et sett med egenskaper. I artikkel II analyserte vi ulike maskinlæringsmetoder (Naive Bayes/independent univariate correlation, random forests og support vector machines) for omvendt regresjon i multi-trekk GWAS, ved bruk av genotyper, genuttrykksdata og “groundtruth” transcriptional regulatory networks fra DREAM5 SysGen Challenge og fra en krysning mellom to gjærstammer for å evaluere metoder. I artikkel III utvidet vi metoden ovenfor til å behandle menneskelig data. En viktig forskjell mellom data fra artikkel II og artikkel III er at vi ikke har “Groundtruth” data tilgjengelig for sistnevnte. Vi brukte genotypen og Magnetresonanstomografi (MRI) data hentet fra ADNI databasen. Resultatene fra både artikkel II og artikkel III viste at resultat av genotypeprediksjon varierte på tvers av genetiske varianter. Dette hjulpet med å identifisere genomiske regioner som er assosiert med stort antall egenskaper i høydimensjonale fenotypiske data. Vi observerte også at koeffisientene til maskinlæringsmodeller korrelerte med styrken til assosiasjonene mellom varianter og egenskaper. Resultatene våre viste også at ikke-lineære maskin-læringsmetoder som “random forests” identifiserte genetiske varianter tydeligere enn de lineære metodene. Spesielt observerte vi i artikkel III at “random forests” var i stand til å identifisere enkeltnukleotidpolymorfismer (SNP-er) som var forskjellige fra de som ble identifisert “ridge” og“lasso” regresjonsmetodene. Ytterligere analyse viste at de identifiserte SNP-ene tilhørte gener som tidligere var assosiert med hjernerelaterte lidelser.Genome-wide association studies (GWAS) aim to find statistical associations between genetic variants and traits of interests. The genetic variants that explain a lot of variation in genome-wide gene expression may lead to confounding in expression quantitative trait loci (eQTL) analyses. To account for these confounding factors, in Article I we proposed LVREML, a method conceptually analogous to estimating fixed and random effects in linear mixed models (LMM). We showed that the maximum-likelihood latent variables can always be chosen orthogonal to the known factors (such genetic variants). This indicates that the maximum-likelihood variables explain the sample covariances that is not already explained by the genetic variants in the model. For identifying which traits are effected by the identified genetic variants, we need to reverse the functional relation between genotypes and traits. In this regard, multitrait approaches are more advantageous than studying the traits individually. The multi-trait approaches benefit from increased power from considering cross-trait covariances and reduced multiple testing burden because a single test is needed to test for associations to a set of traits. In Article II, we analyzed various machine learning methods (ridge regression, Naive Bayes/independent univariate correlation, random forests and support vector machines) for reverse regression in multi-trait GWAS, using genotypes, gene expression data and ground-truth transcriptional regulatory networks from the DREAM5 SysGen Challenge and from a cross between two yeast strains to evaluate methods. In Article III, we extended the above approach to human dataset. An important difference between data from Article II and Article III is that we do not have groundtruth data available for the latter. We used the genotype and brain-imaging features extracted from the MRIs obtained from the ADNI database. The results from both Article II and Article III showed that the genotype prediction performance varied across genetic variants. This helped in identifying genomic regions that are associated with high number of traits in high-dimensional phenotypic data. We also observed that the feature coefficients of fitted machine learning models correlated with the strength of association between variants and traits. Our results also showed that non-linear machine learning methods like random forests identified genetic variants distinct from the linear methods. In particular, we observed in Article III that random forest was able to identify single-nueclotide-polymorphisms (SNPs) that were distinct from the ones identified by ridge and lasso regression. Further analysis showed that the identified SNPs belonged to genes previously associated with brain-related disorders.Doktorgradsavhandlin

A multivariate regression approach to association analysis of a quantitative trait network

Motivation: Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses

A Likelihood Based Framework for Data Integration with Application to eQTL Mapping

We develop a new way of thinking about and integrating gene expression data (continuous) and genomic information data (binary) by jointly compressing the two data sets and embedding their signals in low dimensional feature spaces with an information sharing mechanism, which connects the continuous data to the binary data, under the penalized log-likelihood framework. In particular, the continuous data are modeled by a Gaussian likelihood and the binary data are modeled by a Bernoulli likelihood which is formed by transforming the feature space of the genomic information with a logit link. The smoothly clipped absolute deviation (SCAD) penalty, is added on the basis vectors of the low dimensional feature spaces for both data sets, which is based on the assumption that only a small set of genetic variants are associated with a small fraction of gene expression and the fact that those basis vectors can be interpreted as weights assigned on the genetic variants and gene expression similar to the way the loading vectors of principal component analysis (PCA) or canonical correlation analysis (CCA) are interpreted. Algorithmically, a Majorization-Minimization (MM) algorithm with local linear approximation (LLA) to SCAD penalty is developed to effectively and efficiently solve the optimization problem involved, which produces closed-form updating rules. The effectiveness of our method is demonstrated by simulations in various setups with comparisons to some popular competing methods and an application to eQTL mapping with real data

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

A Review of Integrative Imputation for Multi-Omics Datasets

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets

Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach

Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations

Integrated Analysis of Multiple Data Sets With Biomedical Applications

It is increasingly common to have measurements from multiple platforms on the same set of samples in modern biomedical sciences. In this dissertation, we develop novel methodologies for integrated analysis of multiple data sets. In particular, we devise a supervised principal component analysis framework that achieves dimension reduction of the primary data with guidance from an auxiliary data set. It extracts accurate and interpretable low-rank structures that are potentially driven by the auxiliary information. We further extend the method to accommodate special features of data such as functionality and high dimensionality through regularization. Numerical examples demonstrate that the proposed methodologies have clear advantages over existing methods. In addition, we develop a Bayesian hierarchical model for multi-tissue eQTL analysis. It exploits shared information in multiple tissues to increase the power of eQTL discovery and improve tissue specicity assessment. The method has been adopted by the Genotype-Tissue Expression (GTEx) consortium and successfully applied to the nine-tissue pilot data.Doctor of Philosoph