Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

Influence of genetic variation on plasma protein levels in older adults using a multi-analyte panel

Proteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate corresponding protein levels and point to the role of these variants in disease pathophysiology. Effects of individual single nucleotide polymorphisms (SNPs) within a gene were analyzed for corresponding plasma protein levels using genome-wide association study (GWAS) genotype data and proteomic panel data with 132 quality-controlled analytes from 521 Caucasian participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Linear regression analysis detected 112 significant (Bonferroni threshold p = 2.44×10⁻⁵) associations between 27 analytes and 112 SNPs. 107 out of these 112 associations were tested in the Indiana Memory and Aging Study (IMAS) cohort for replication and 50 associations were replicated at uncorrected p<0.05 in the same direction of effect as those in the ADNI. We identified multiple novel associations including the association of rs7517126 with plasma complement factor H-related protein 1 (CFHR1) level at p<1.46×10⁻⁶⁰, accounting for 40 percent of total variation of the protein level. We serendipitously found the association of rs6677604 with the same protein at p<9.29×10⁻¹¹². Although these two SNPs were not in the strong linkage disequilibrium, 61 percent of total variation of CFHR1 was accounted for by rs6677604 without additional variation by rs7517126 when both SNPs were tested together. 78 other SNP-protein associations in the ADNI sample exceeded genome-wide significance (5×10⁻⁸). Our results confirmed previously identified gene-protein associations for interleukin-6 receptor, chemokine CC-4, angiotensin-converting enzyme, and angiotensinogen, although the direction of effect was reversed in some cases. This study is among the first analyses of gene-protein product relationships integrating multiplex-panel proteomics and targeted genes extracted from a GWAS array. With intensive searches taking place for proteomic biomarkers for many diseases, the role of genetic variation takes on new importance and should be considered in interpretation of proteomic results.

A Genome-Wide Association Study Suggests Novel Loci Associated with a Schizophrenia-Related Brain-Based Phenotype

Patients with schizophrenia and their siblings typically show subtle changes of brain structures, such as a reduction of hippocampal volume. Hippocampal volume is heritable, may explain a variety of cognitive symptoms of schizophrenia and is thus considered an intermediate phenotype for this mental illness. The aim of our analyses was to identify single-nucleotide polymorphisms (SNP) related to hippocampal volume without making prior assumptions about possible candidate genes. In this study, we combined genetics, imaging and neuropsychological data obtained from the Mind Clinical Imaging Consortium study of schizophrenia (n = 328). A total of 743,591 SNPs were tested for association with hippocampal volume in a genome-wide association study. Gene expression profiles of human hippocampal tissue were investigated for gene regions of significantly associated SNPs. None of the genetic markers reached genome-wide significance. However, six highly correlated SNPs (rs4808611, rs35686037, rs12982178, rs1042178, rs10406920, rs8170) on chromosome 19p13.11, located within or in close proximity to the genes NR2F6, USHBP1, and BABAM1, as well as four SNPs in three other genomic regions (chromosome 1, 2 and 10) had p-values between 6.75×10(-6) and 8.3×10(-7). Using existing data of a very recently published GWAS of hippocampal volume and additional data of a multicentre study in a large cohort of adolescents of European ancestry, we found supporting evidence for our results. Furthermore, allelic differences in rs4808611 and rs8170 were highly associated with differential mRNA expression in the cis-acting region. Associations with memory functioning indicate a possible functional importance of the identified risk variants. Our findings provide new insights into the genetic architecture of a brain structure closely linked to schizophrenia. In silico replication, mRNA expression and cognitive data provide additional support for the relevance of our findings. Identification of causal variants and their functional effects may unveil yet unknown players in the neurodevelopment and the pathogenesis of neuropsychiatric disorders.

The influence of APOE and TOMM40 polymorphisms on hippocampal volume and episodic memory in old age

Mitochondrial dysfunction is implicated in neurodegenerative disorders, such as Alzheimer's disease (AD). Translocase of outer mitochondrial membrane 40 (TOMM40) may be influential in this regard by influencing mitochondrial neurotoxicity. Little is known about the influence of the TOMM40 gene on hippocampal (HC) volume and episodic memory (EM), particularly in healthy older adults. Thus, we sought to discern the influence of TOMM40 single nucleotide polymorphisms (SNPs), which have previously been associated with medial temporal lobe integrity (rs11556505 and rs2075650), on HC volume and EM. The study sample consisted of individuals from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) who were free of dementia and known neurological disorders, and 60-87 years of age (n = 424). EM was measured by using a 16-item word list with a 2-min free recall period and delineation of the HC was performed manually. The influence of Apolipoprotein E (APOE) and TOMM40 was assessed by 2 × 2 ANOVAs and partial correlations. There was no effect of APOE and TOMM40 on EM performance and HC volume. However, partial correlations revealed that HC volume was positively associated with free recall performance (r = 0.21, p < 0.01, r (2) = 0.04). When further stratified for TOMM40, the observed association between HC volume and free recall in APOE ε4 carriers was present in combination with TOMM40 rs11556505 any T (r = 0.28, p < 0.01, R (2) = 0.08) and rs2075650 any G (r = 0.28, p < 0.01, R (2) = 0.08) "risk" alleles. This pattern might reflect higher reliance on HC volume for adequate EM performance among APOE ε4 carriers with additional TOMM40 "risk" alleles suggesting that the TOMM40 gene cannot merely be considered a marker of APOE genotype. Nevertheless, neither APOE nor TOMM40 influenced HC volume or EM in this population-based sample of cognitively intact individuals over the age of 60.

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs

MOTIVATION

Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.

RESULTS

Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.

AVAILABILITY

Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/

CONTACT

heng@uta.edu or shenli@inpui.edu.

A multi-platform draft de novo genome assembly and comparative analysis for the Scarlet Macaw (Ara macao)

Data deposition to NCBI Genomes: This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AMXX00000000 (SMACv1.0, unscaffolded genome assembly). The version described in this paper is the first version (AMXX01000000). The scaffolded assembly (SMACv1.1) has been deposited at DDBJ/EMBL/GenBank under the accession AOUJ00000000, and is also the first version (AOUJ01000000). Strong biological interest in traits such as the acquisition and utilization of speech, cognitive abilities, and longevity catalyzed the utilization of two next-generation sequencing platforms to provide the first-draft de novo genome assembly for the large, new world parrot Ara macao (Scarlet Macaw). Despite the challenges associated with genome assembly for an outbred avian species, including 951,507 high-quality putative single nucleotide polymorphisms, the final genome assembly (>1.035 Gb) includes more than 997 Mb of unambiguous sequence data (excluding N's). Cytogenetic analyses including ZooFISH revealed complex rearrangements associated with two scarlet macaw macrochromosomes (AMA6, AMA7), which supports the hypothesis that translocations, fusions, and intragenomic rearrangements are key factors associated with karyotype evolution among parrots. In silico annotation of the scarlet macaw genome provided robust evidence for 14,405 nuclear gene annotation models, their predicted transcripts and proteins, and a complete mitochondrial genome. Comparative analyses involving the scarlet macaw, chicken, and zebra finch genomes revealed high levels of nucleotide-based conservation as well as evidence for overall genome stability among the three highly divergent species. Application of a new whole-genome analysis of divergence involving all three species yielded prioritized candidate genes and noncoding regions for parrot traits of interest (i.e., speech, intelligence, longevity) which were independently supported by the results of previous human GWAS studies. We also observed evidence for genes and noncoding loci that displayed extreme conservation across the three avian lineages, thereby reflecting their likely biological and developmental importance among birds.

Increased CNV-region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample

We investigated the genome-wide distribution of CNVs in the Alzheimer's disease (AD) Neuroimaging Initiative (ADNI) sample (146 with AD, 313 with Mild Cognitive Impairment (MCI), and 181 controls). Comparison of single CNVs between cases (MCI and AD) and controls shows overrepresentation of large heterozygous deletions in cases (p-value<0.0001). The analysis of CNV-Regions identifies 44 copy number variable loci of heterozygous deletions, with more CNV-Regions among affected than controls (p=0.005). Seven of the 44 CNV-Regions are nominally significant for association with cognitive impairment. We validated and confirmed our main findings with genome re-sequencing of selected patients and controls. The functional pathway analysis of the genes putatively affected by deletions of CNV-Regions reveals enrichment of genes implicated in axonal guidance, cell-cell adhesion, neuronal morphogenesis and differentiation. Our findings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment.

A neurogenetics approach to understanding individual differences in brain, behavior, and risk for psychopathology

Neurogenetics research has begun to advance our understanding of how genetic variation gives rise to individual differences in brain function, which, in turn, shapes behavior and risk for psychopathology. Despite these advancements, neurogenetics research is currently confronted by three major challenges: (1) conducting research on individual variables with small effects, (2) absence of detailed mechanisms, and (3) a need to translate findings toward greater clinical relevance. In this review, we showcase techniques and developments that address these challenges and highlight the benefits of a neurogenetics approach to understanding brain, behavior and psychopathology. To address the challenge of small effects, we explore approaches including incorporating the environment, modeling epistatic relationships and using multilocus profiles. To address the challenge of mechanism, we explore how non-human animal research, epigenetics research and genome-wide association studies can inform our mechanistic understanding of behaviorally relevant brain function. Finally, to address the challenge of clinical relevance, we examine how neurogenetics research can identify novel therapeutic targets and for whom treatments work best. By addressing these challenges, neurogenetics research is poised to exponentially increase our understanding of how genetic variation interacts with the environment to shape the brain, behavior and risk for psychopathology.

A genome-wide association study of brain lesion distribution in multiple sclerosis

Brain magnetic resonance imaging is widely used as a diagnostic and monitoring tool in multiple sclerosis and provides a non-invasive, sensitive and reproducible way to track the disease. Topological characteristics relating to the distribution and shape of lesions are recognized as important neuroradiological markers in the diagnosis of multiple sclerosis, although these have been much less well characterized quantitatively than have traditional measures such as T2 hyperintense or T1 hypointense lesion volumes. Here, we used voxel-level 3 T magnetic resonance imaging T1-weighted scans to reconstruct the 3D topology of lesions in 284 subjects with multiple sclerosis and tested whether this is a heritable phenotype. To this end, we extracted the genotypes from a published genome-wide association study on these same individuals and searched for genetic associations with lesion load, shape and topological distribution. Lesion probability maps were created to identify frequently affected areas and to assess the overall distribution of T1 lesions in the subject population as a whole. We then developed an original algorithm to cluster adjacent lesional voxels (cluxels) in each subject and tested whether cluxel topology was significantly associated with any single-nucleotide polymorphism in our data set. To focus on patterns of lesion distribution, we computed the first 10 principal components. Although principal component 1 correlated with lesion load, none of the remaining orthogonal components correlated with any other known variable. We then conducted genome-wide association studies on each of these and found 31 significant associations (false discovery rate <0.01) with principal component 8, which represents a mode of variation of lesion topology in the population. The majority of the loci can be linked to genes related to immune cell function and to myelin and neural growth; some (SYK, MYT1L, TRAPPC9, SLITKR6 and RIC3) have been previously associated with the distribution of white matter lesions in multiple sclerosis. Finally, we used a bioinformatics approach to identify a network of 48 interacting proteins showing genetic associations (P < 0.01) with cluxel topology in multiple sclerosis. This network also contains proteins expressed in immune cells and is enriched in molecules expressed in the central nervous system that contribute to neural development and regeneration. Our results show how quantitative traits derived from brain magnetic resonance images of patients with multiple sclerosis can be used as dependent variables in a genome-wide association study. With the widespread availability of powerful computing and the availability of genotyped populations, integration of imaging and genetic data sets is likely to become a mainstream tool for understanding the complex biological processes of multiple sclerosis and other brain disorders.

Genetic insights in Alzheimer's disease

In the search for new genes in Alzheimer's disease, classic linkage-based and candidate-gene-based association studies have been supplanted by exome sequencing, genome-wide sequencing (for mendelian forms of Alzheimer's disease), and genome-wide association studies (for non-mendelian forms). The identification of new susceptibility genes has opened new avenues for exploration of the underlying disease mechanisms. In addition to detecting novel risk factors in large samples, next-generation sequencing approaches can deliver novel insights with even small numbers of patients. The shift in focus towards translational studies and sequencing of individual patients places each patient's biomaterials as the central unit of genetic studies. The notional shift needed to make the patient central to genetic studies will necessitate strong collaboration and input from clinical neurologists.

Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning

Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.

Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.

Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/

Contact:heng@uta.edu; shenli@iupui.edu

Neuroimaging and other biomarkers for Alzheimer's disease: the changing landscape of early detection

The goal of this review is to provide an overview of biomarkers for Alzheimer's disease (AD), with emphasis on neuroimaging and cerebrospinal fluid (CSF) biomarkers. We first review biomarker changes in patients with late-onset AD, including findings from studies using structural and functional magnetic resonance imaging (MRI), advanced MRI techniques (diffusion tensor imaging, magnetic resonance spectroscopy, perfusion), positron emission tomography with fluorodeoxyglucose, amyloid tracers, and other neurochemical tracers, and CSF protein levels. Next, we evaluate findings from these biomarkers in preclinical and prodromal stages of AD including mild cognitive impairment (MCI) and pre-MCI conditions conferring elevated risk. We then discuss related findings in patients with dominantly inherited AD. We conclude with a discussion of the current theoretical framework for the role of biomarkers in AD and emergent directions for AD biomarker research.

PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease

Increasing evidence suggests that inflammation is one pathophysio-logical mechanism in Alzheimer's disease (AD). Recent studies have identifiedan association between the poly (ADP-ribose) polymerase 1 (PARP1) gene and AD. This gene encodes a protein that is involved in many biological functions, including DNA repair and chromatin remodeling, and is a mediator of inflammation. Therefore, we performed a targeted genetic association analysis to investigate the relationship between the PARP1 polymorphisms and brain micro-glial activity as indexed by [¹¹C]PBR28 positron emission tomography (PET). Participants were 26 non-Hispanic Caucasians in the Indiana Memory and Aging Study (IMAS). PET data were intensity-normalized by injected dose/total body weight. Average PBR standardized uptake values (SUV) from 6 bilateral regions of interest (thalamus, frontal, parietal, temporal, and cingulate cortices, and whole brain gray matter) were used as endophenotypes. Single nucleotide polymorphisms (SNPs) with 20% minor allele frequency that were within +/- 20 kb of the PARP1 gene were included in the analyses. Gene-level association analyses were performed using a dominant genetic model with translocator protein (18-kDa) (TSPO) genotype, age at PET scan, and gender as covariates. Analyses were performed with and without APOE ε4 status as a covariate. Associations with PBR SUVs from thalamus and cingulate were significant at corrected p<0.014 and <0.065, respectively. Subsequent multi-marker analysis with cingulate PBR SUV showed that individuals with the "C" allele at rs6677172 and "A" allele at rs61835377 had higher PBR SUV than individuals without these alleles (corrected P<0.03), and individuals with the "G" allele at rs6677172 and "G" allele at rs61835377 displayed the opposite trend (corrected P<0.065). A previous study with the same cohort showed an inverse relationship between PBR SUV and brain atrophy at a follow-up visit, suggesting possible protective effect of microglial activity against cortical atrophy. Interestingly, all 6 AD and 2 of 3 LMCI participants in the current analysis had one or more copies of the "GG" allele combination, associated with lower cingulate PBR SUV, suggesting that this gene variant warrants further investigation.

An association and haplotype analysis of porcine maternal infanticide: a model for human puerperal psychosis?

An association analysis using the Illumina porcine SNP60 beadchip was performed to identify SNPs significantly associated with porcine maternal infanticide. We previously hypothesised that this was a good animal model for human puerperal psychosis, an extreme form of postnatal mood disorder. Animals were selected from carefully phenotyped unrelated infanticide and control groups (representing extremes of the phenotypic spectrum), from four different lines. Permutation and sliding window analyses and an analysis to see which haplotypes were in linkage disequilibrium (LD) were compared to identify concordant regions. Across all analyses, intervals on SSCs 1, 3, 4, 10, and 13 were constant, contained genes associated with psychiatric or neurological disorders and were significant in multiple lines. The strongest (near GWS) consistent candidate region across all analyses and all breeds was the one located on SSC3 with one peak at 23.4 Mb, syntenic to a candidate region for bipolar disorder and another at 31.9 Mb, syntenic to a candidate region for human puerperal psychosis (16p13). From the haplotype/LD analysis, two regions reached genome wide significance (GWS): the first on SSC4 (KHDRBS3 to FAM135B), which was significant (-logP 5.57) in one Duroc based breed and is syntenic to a region in humans associated with cognition and neurotism; the second on SSC15, which was significant (-log10P 5.68) in two breeds and contained PAX3, which is expressed in the brain.

Genome-wide association analysis of age-at-onset in Alzheimer's disease

The risk of Alzheimer's disease (AD) is strongly determined by genetic factors and recent genome-wide association studies (GWAS) have identified several genes for the disease risk. In addition to the disease risk, age-at-onset (AAO) of AD has also strong genetic component with an estimated heritability of 42%. Identification of AAO genes may help to understand the biological mechanisms that regulate the onset of the disease. Here we report the first GWAS focused on identifying genes for the AAO of AD. We performed a genome-wide meta-analysis on three samples comprising a total of 2222 AD cases. A total of ~2.5 million directly genotyped or imputed single-nucleotide polymorphisms (SNPs) were analyzed in relation to AAO of AD. As expected, the most significant associations were observed in the apolipoprotein E (APOE) region on chromosome 19 where several SNPs surpassed the conservative genome-wide significant threshold (P<5E-08). The most significant SNP outside the APOE region was located in the DCHS2 gene on chromosome 4q31.3 (rs1466662; P=4.95E-07). There were 19 additional significant SNPs in this region at P<1E-04 and the DCHS2 gene is expressed in the cerebral cortex and thus is a potential candidate for affecting AAO in AD. These findings need to be confirmed in additional well-powered samples.

Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3,PIK3CG,PRKCAandPRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.

Increasing power for voxel-wise genome-wide association studies: the random field theory, least square kernel machines and fast permutation procedures

Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's disease neuroimaging initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.

Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative

Missing heritability in late onset Alzheimer disease can be attributed, at least in part, to heterogeneity in disease status and to the lack of statistical analyses exploring genetic interactions. In the current study, we use quantitative intermediate phenotypes derived from magnetic resonance imaging data available from the Alzheimer's Disease Neuroimaging Initiative, and we test for association with gene-gene interactions within biological pathways. Regional brain volumes from the hippocampus (HIP) and entorhinal cortex (EC) were estimated from baseline and 12-month magnetic resonance imaging scans. Approximately 560,000 single nucleotide polymorphisms (SNPs) were available genome-wide. We tested all pairwise SNP-SNP interactions (approximately 151 million) within 212 Kyoto Encyclopedia of Genes and Genomes pathways for association with 12-month regional atrophy rates using linear regression, with sex, APOE ε4 carrier status, age, education, and clinical status as covariates. A total of 109 SNP-SNP interactions were associated with right HIP atrophy, and 125 were associated with right EC atrophy. Enrichment analysis indicated significant SNP-SNP interactions were overrepresented in the calcium signaling and axon guidance pathways for both HIP and EC atrophy and in the ErbB signaling pathway for HIP atrophy.

The genetics and neuropathology of Alzheimer's disease

Here we review the genetic causes and risks for Alzheimer's disease (AD). Early work identified mutations in three genes that cause AD: APP, PSEN1 and PSEN2. Although mutations in these genes are rare causes of AD, their discovery had a major impact on our understanding of molecular mechanisms of AD. Early work also revealed the ε4 allele of the APOE as a strong risk factor for AD. Subsequently, SORL1 also was identified as an AD risk gene. More recently, advances in our knowledge of the human genome, made possible by technological advances and methods to analyze genomic data, permit systematic identification of genes that contribute to AD risk. This work, so far accomplished through single nucleotide polymorphism arrays, has revealed nine new genes implicated in AD risk (ABCA7, BIN1, CD33, CD2AP, CLU, CR1, EPHA1, MS4A4E/MS4A6A, and PICALM). We review the relationship between these mutations and genetic variants and the neuropathologic features of AD and related disorders. Together, these discoveries point toward a new era in neurodegenerative disease research that impacts not only AD but also related illnesses that produce cognitive and behavioral deficits.

A Polygenic Approach to the Study of Polygenic Diseases

Polygenic diseases are caused by the joint contribution of a number of independently acting or interacting polymorphic genes; the individual contribution of each gene may be small or even unnoticeable. The carriage of certain combinations of genes can determine the occurrence of clinically heterogeneous forms of the disease and treatment efficacy. This review describes the approaches used in a polygenic analysis of data in medical genomics, in particular, pharmacogenomics, aimed at identifying the cumulative effect of genes. This effect may result from the summation of gains of different genes or be caused by the epistatic interaction between the genes. Both cases are undoubtedly of great interest in investigating the nature of polygenic diseases. The means that allow one to discriminate between these two possibilities are discussed. The methods for searching for combinations of alleles of different genes associated with the polygenic phenotypic traits of the disease, as well as the methods for presenting and validating the results, are described and compared. An attempt is made to evaluate the applicability of the existing methods to an epistasis analysis. The results obtained by the authors using the APSampler software are described and summarized.

Multiple loci influencing hippocampal degeneration identified by genome scan

OBJECTIVE

Large genome-wide association studies (GWASs) have identified many novel genes influencing Alzheimer disease (AD) risk, but most of the genetic variance remains unexplained. We conducted a 2-stage GWAS for AD-related quantitative measures of hippocampal volume (HV), total cerebral volume (TCV), and white matter hyperintensities (WMH).

METHODS

Brain magnetic resonance imaging measures of HV, TCV, and WMH were obtained from 981 Caucasian and 419 African American AD cases and their cognitively normal siblings in the MIRAGE (Multi Institutional Research in Alzheimer's Genetic Epidemiology) Study, and from 168 AD cases, 336 individuals with mild cognitive impairment, and 188 controls in the Alzheimer's Disease Neuroimaging Initiative Study. A GWAS for each trait was conducted in the 2 Caucasian data sets in stage 1. Results from the 2 data sets were combined by meta-analysis. In stage 2, 1 single nucleotide polymorphism (SNP) from each region that was nominally significant in each data set (p < 0.05) and strongly associated in both data sets (p < 1.0 × 10(-5)) was evaluated in the African American data set.

RESULTS

Twenty-two markers (14 for HV, 3 for TCV, and 5 for WMH) from distinct regions met criteria for evaluation in stage 2. Novel genome-wide significant associations (p < 5.0 × 10(-8)) were attained for HV with SNPs in the APOE, F5/SELP, LHFP, and GCFC2 gene regions. All of these associations were supported by evidence in each data set. Associations with different SNPs in the same gene (p < 1 × 10(-5) in Caucasians and p < 2.2 × 10(-4) in African Americans) were also observed for PICALM with HV, SYNPR with TCV, and TTC27 with WMH.

INTERPRETATION

Our study demonstrates the efficacy of endophenotypes for broadening our understanding of the genetic basis of AD.

Amyloid pathway-based candidate gene analysis of [(11)C]PiB-PET in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort

Amyloid imaging with [(11)C]Pittsburgh Compound-B (PiB) provides in vivo data on plaque deposition in those with, or at risk for, Alzheimer's disease (AD). We performed a gene-based association analysis of 15 quality-controlled amyloid-pathway associated candidate genes in 103 Alzheimer's Disease Neuroimaging Initiative participants. The mean normalized PiB uptake value across four brain regions known to have amyloid deposition in AD was used as a quantitative phenotype. The minor allele of an intronic SNP within DHCR24 was identified and associated with a lower average PiB uptake. Further investigation at whole-brain voxel-wise level indicated that non-carriers of the minor allele had higher PiB uptake in frontal regions compared to carriers. DHCR24 has been previously shown to confer resistance against beta-amyloid and oxidative stress-induced apoptosis, thus our findings support a neuroprotective role. Pathway-based genetic analysis of targeted molecular imaging phenotypes appears promising to help elucidate disease pathophysiology and identify potential therapeutic targets.

Neurogenetics of depression: a focus on reward processing and stress sensitivity

Major depressive disorder (MDD) is etiologically complex and has a heterogeneous presentation. This heterogeneity hinders the ability of molecular genetic research to reliably detect the small effects conferred by common genetic variation. As a result, significant research efforts have been directed at investigating more homogenous intermediate phenotypes believed to be more proximal to gene function and lie between genes and/or environmental effects and disease processes. In the current review we survey and integrate research on two promising intermediate phenotypes linked to depression: reward processing and stress sensitivity. A synthesis of this burgeoning literature indicates that a molecular genetic approach focused on intermediate phenotypes holds significant promise to fundamentally improve our understanding of the pathophysiology and etiology of depression, which will be required for improved diagnostic definitions and the development of novel and more efficacious treatment and prevention strategies. We conclude by highlighting challenges facing intermediate phenotype research and future development that will be required to propel this pivotal research into new directions.

Promising Genetic Biomarkers of Preclinical Alzheimer's Disease: The Influence of APOE and TOMM40 on Brain Integrity

Finding biomarkers constitutes a crucial step for early detection of Alzheimer's disease (AD). Brain imaging techniques have revealed structural alterations in the brain that may be phenotypic in preclinical AD. The most prominent polymorphism that has been associated with AD and related neural changes is the Apolipoprotein E (APOE) ε4. The translocase of outer mitochondrial membrane 40 (TOMM40), which is in linkage disequilibrium with APOE, has received increasing attention as a promising gene in AD. TOMM40 also impacts brain areas vulnerable in AD, by downstream apoptotic processes that forego extracellular amyloid beta aggregation. The present paper aims to extend on the mitochondrial influence in AD pathogenesis and we propose a TOMM40-induced disconnection of the medial temporal lobe. Finally, we discuss the possibility of mitochondrial dysfunction being the earliest pathophysiological event in AD, which indeed is supported by recent findings.

A genome-wide search for genetic influences and biological pathways related to the brain's white matter integrity

A genome-wide search for genetic variants influencing the brain's white matter integrity in old age was conducted in the Lothian Birth Cohort 1936 (LBC1936). At ∼73 years of age, members of the LBC1936 underwent diffusion MRI, from which 12 white matter tracts were segmented using quantitative tractography, and tract-averaged water diffusion parameters were determined (n = 668). A global measure of white matter tract integrity, g(FA), derived from principal components analysis of tract-averaged fractional anisotropy measurements, accounted for 38.6% of the individual differences across the 12 white matter tracts. A genome-wide search was performed with g(FA) on 535 individuals with 542,050 single nucleotide polymorphisms (SNPs). No single SNP association was genome-wide significant (all p > 5 × 10(-8)). There was genome-wide suggestive evidence for two SNPs, one in ADAMTS18 (p = 1.65 × 10(-6)), which is related to tumor suppression and hemostasis, and another in LOC388630 (p = 5.08 × 10(-6)), which is of unknown function. Although no gene passed correction for multiple comparisons in single gene-based testing, biological pathways analysis suggested evidence for an over-representation of neuronal transmission and cell adhesion pathways relating to g(FA).

The challenges and promise of neuroimaging in psychiatry

Neuroimaging is central to the quest for a biological foundation of psychiatric diagnosis but so far has not yielded clinically relevant biomarkers for mental disorders. This review addresses potential reasons for this limitation and discusses refinements of paradigms and analytic techniques that may yield improved diagnostic and prognostic accuracy. Neuroimaging can also be used to probe genetically defined biological pathways underlying mental disorders, for example through the genetic imaging of variants discovered in genome-wide association studies. These approaches may ultimately reveal mechanisms through which genes contribute to psychiatric symptoms and how pharmacological and psychological interventions exert their effects.

Identification of cancer cell-line origins using fluorescence image-based phenomic screening

Universal phenotyping techniques that can discriminate among various states of biological systems have great potential. We applied 557 fluorescent library compounds to NCI's 60 human cancer cell-lines (NCI-60) to generate a systematic fluorescence phenotypic profiling data. By the kinetic fluorescence intensity analysis, we successfully discriminated the organ origin of all the 60 cell-lines.

Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort

MOTIVATION

Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation.

RESULTS

Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs.

AVAILABILITY

Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.

A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

The effect of TOMM40 poly-T length on gray matter volume and cognition in middle-aged persons with APOE ε3/ε3 genotype

OBJECTIVE

Apolipoprotein E (APOE) genotypes are associated with variable risk of developing late-onset Alzheimer's disease (LOAD), with APOE epsilon 4 (APOE ε4) having higher risk. A variable poly-T length polymorphism at rs10524523, within intron 6 of the translocase of the outer mitochondrial membrane (TOMM40) gene, has been shown to influence age of onset in LOAD, with very long (VL) poly-T length associated with earlier disease onset, and short poly-T length associated with later onset. In this study, we tested the hypothesis that brain and cognitive changes suggestive of presymptomatic LOAD may be associated with this TOMM40 polymorphism.

METHODS

Among healthy APOE ε3 homozygous adults (N = 117; mean age, 55 years), we compared those who were homozygous for VL/VL (n = 35) TOMM40 poly-T lengths (who were presumably at higher risk) with those homozygous for short (S/S; n = 38) poly-T lengths, as well as those with heterozygous (S/VL; n = 44) poly-T length polymorphisms, on measures of learning and memory and on structural brain imaging.

RESULTS

The VL/VL group showed lower performance than the S/S TOMM40 group on primacy retrieval from a verbal list learning task, a finding which is also seen in early Alzheimer's disease. A dose-dependent increase in the VL TOMM40 polymorphism (from no VL alleles, to S/VL heterozygous, to VL/VL homozygous) was associated with decreasing gray matter volume in the ventral posterior cingulate and medial ventral precuneus, a region of the brain affected early in LOAD.

CONCLUSIONS

These findings among APOE ε3/ε3 late middle-aged adults suggest that a subgroup with VL TOMM40 poly-T lengths may be experiencing incipient LOAD-related cognitive and brain changes.

Functional analysis of APOE locus genetic variation implicates regional enhancers in the regulation of both TOMM40 and APOE

Genetic variation within the apolipoprotein E gene (APOE) locus is associated with late-onset Alzheimer's disease risk and quantitative traits as well as apoE expression in multiple tissues. The aim of this investigation was to explore the influence of APOE locus cis-regulatory element enhancer region genetic variation on regional gene promoter activity. Luciferase reporter constructs containing haplotypes of APOE locus gene promoters; APOE, APOC1, and TOMM40, and regional putative enhancers; TOMM40 IVS2-4, TOMM40 IVS6 poly-T, as well as previously described enhancers; ME1, or BCR, were evaluated for their effects on luciferase activity in 3 human cell lines. Results of this investigation demonstrate that in SHSY5Y cells, the APOE promoter is significantly influenced by the TOMM40 IVS2-4 and ME1 and the TOMM40 promoter is significantly influenced by the TOMM40 IVS6 poly-T, ME1 and BCR. In HepG2 cells, theTOMM40 promoter is significantly influenced by all four enhancers, whereas the APOE promoter is not influenced by any of the enhancers. The main novel finding of this investigation was that multiple APOE locus cis-elements influence both APOE and TOMM40 promoter activity according to haplotype and cell type suggesting that a complex transcriptional regulatory structure modulates regional expression.

Hippocampal surface mapping of genetic risk factors in AD via sparse learning models

Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with considerable evidence suggesting an initiation of disease in the entorhinal cortex and hippocampus and spreading thereafter to the rest of the brain. In this study, we combine genetics and imaging data obtained from the Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed study. To identify genetic susceptibility loci for AD, we conducted a genome-wide study of atrophy in regions associated with neurodegeneration in this condition. We identified one single-nucleotide polymorphism (SNP) with a disease-specific effect associated with entorhinal cortical volume in an intron of the ZNF292 gene (rs1925690; P-value=2.6 × 10(-8); corrected P-value for equivalent number of independent quantitative traits=7.7 × 10(-8)) and an intergenic SNP, flanking the ARPP-21 gene, with an overall effect on entorhinal cortical thickness (rs11129640; P-value=5.6 × 10(-8); corrected P-value=1.7 × 10(-7)). Gene-wide scoring also highlighted PICALM as the most significant gene associated with entorhinal cortical thickness (P-value=6.7 × 10(-6)).

Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, which makes regression analysis a suitable model to study whether neuroimaging measures can help predict memory performance and track the progression of AD. Existing memory performance prediction methods via regression, however, do not take into account either the interconnected structures within imaging data or those among memory scores, which inevitably restricts their predictive capabilities. To bridge this gap, we propose a novel Sparse Multi-tAsk Regression and feaTure selection (SMART) method to jointly analyze all the imaging and clinical data under a single regression framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as facilitate multi-task learning. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performances in all empirical test cases and a compact set of selected RAVLT-relevant MRI predictors that accord with prior studies.

The effect of reference panels and software tools on genotype imputation

Genotype imputation is increasingly employed in genome-wide association studies, particularly for integrative and cross-platform analysis. Several imputation algorithms use reference panels with a larger set of genotyped markers to infer genotypes at ungenotyped marker locations. Our objective was to assess which method and reference panel was more accurate when carrying out imputation. We investigated the influence of choice of two most popular imputation methods, IMPUTE and MACH, on two reference panels from the HapMap and the 1000 Genomes Project. Our results indicated that for the HapMap, MACH consistently yielded more accurate imputation results than IMPUTE, while for the 1000 Genomes Project, IMPUTE performed slightly better. The best imputation results were achieved by IMPUTE with the combined reference panel (HapMap + 1000 Genomes Project). IMPUTE with the combined reference panel is a promising strategy for genotype imputation, which should facilitate fine-mapping for discovery as well as known disease-associated candidate regions.

Multilocus genetic analysis of brain images

The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes.

Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

By studying the loci that contribute to human longevity, we aim to identify mechanisms that contribute to healthy aging. To identify such loci, we performed a genome-wide association study (GWAS) comparing 403 unrelated nonagenarians from long-living families included in the Leiden Longevity Study (LLS) and 1670 younger population controls. The strongest candidate SNPs from this GWAS have been analyzed in a meta-analysis of nonagenarian cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort. Only one of the 62 prioritized SNPs from the GWAS analysis (P < 1 × 10⁻⁴) showed genome-wide significance with survival into old age in the meta-analysis of 4149 nonagenarian cases and 7582 younger controls [OR = 0.71 (95% CI 0.65–0.77), P = 3.39 × 10⁻¹⁷]. This SNP, rs2075650, is located in TOMM40 at chromosome 19q13.32 close to the apolipoprotein E (APOE) gene. Although there was only moderate linkage disequilibrium between rs2075650 and the ApoE ε4 defining SNP rs429358, we could not find an APOE-independent effect of rs2075650 on longevity, either in cross-sectional or in longitudinal analyses. As expected, rs429358 associated with metabolic phenotypes in the offspring of the nonagenarian cases from the LLS and their partners. In addition, we observed a novel association between this locus and serum levels of IGF-1 in women (P = 0.005). In conclusion, the major locus determining familial longevity up to high age as detected by GWAS was marked by rs2075650, which tags the deleterious effects of the ApoE ε4 allele. No other major longevity locus was found.

Alzheimer's disease genetics: lessons to improve disease modelling

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

A network of genes, genetic disorders, and brain areas

The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

Genomic Copy Number Analysis in Alzheimer's Disease and Mild Cognitive Impairment: An ADNI Study

Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap genes and may play important roles in disease. Only one published study has examined CNVs in late-onset Alzheimer's disease (AD), and none have examined mild cognitive impairment (MCI). CNV calls were generated in 288 AD, 183 MCI, and 184 healthy control (HC) non-Hispanic Caucasian Alzheimer's Disease Neuroimaging Initiative participants. After quality control, 222 AD, 136 MCI, and 143 HC participants were entered into case/control association analyses, including candidate gene and whole genome approaches. Although no excess CNV burden was observed in cases (AD and/or MCI) relative to controls (HC), gene-based analyses revealed CNVs overlapping the candidate gene CHRFAM7A, as well as CSMD1, SLC35F2, HNRNPCL1, NRXN1, and ERBB4 regions, only in cases. Replication in larger samples is important, after which regions detected here may be promising targets for resequencing.

Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.

Neuroimaging measures as endophenotypes in Alzheimer's disease

Late onset Alzheimer's disease (AD) is moderately to highly heritable. Apolipoprotein E allele ε4 (APOE4) has been replicated consistently as an AD risk factor over many studies, and recently confirmed variants in other genes such as CLU, CR1, and PICALM each increase the lifetime risk of AD. However, much of the heritability of AD remains unexplained. AD is a complex disease that is diagnosed largely through neuropsychological testing, though neuroimaging measures may be more sensitive for detecting the incipient disease stages. Difficulties in early diagnosis and variable environmental contributions to the disease can obscure genetic relationships in traditional case-control genetic studies. Neuroimaging measures may be used as endophenotypes for AD, offering a reliable, objective tool to search for possible genetic risk factors. Imaging measures might also clarify the specific mechanisms by which proposed risk factors influence the brain.

Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression

Traditional neuroimaging studies in Alzheimer's disease (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.

Genome-wide association study of CSF biomarkers Abeta1-42, t-tau, and p-tau181p in the ADNI cohort

OBJECTIVES

CSF levels of Aβ1-42, t-tau, and p-tau181p are potential early diagnostic markers for probable Alzheimer disease (AD). The influence of genetic variation on these markers has been investigated for candidate genes but not on a genome-wide basis. We report a genome-wide association study (GWAS) of CSF biomarkers (Aβ1-42, t-tau, p-tau181p, p-tau181p/Aβ1-42, and t-tau/Aβ1-42).

METHODS

A total of 374 non-Hispanic Caucasian participants in the Alzheimer's Disease Neuroimaging Initiative cohort with quality-controlled CSF and genotype data were included in this analysis. The main effect of single nucleotide polymorphisms (SNPs) under an additive genetic model was assessed on each of 5 CSF biomarkers. The p values of all SNPs for each CSF biomarker were adjusted for multiple comparisons by the Bonferroni method. We focused on SNPs with corrected p<0.01 (uncorrected p<3.10×10(-8)) and secondarily examined SNPs with uncorrected p values less than 10(-5) to identify potential candidates.

RESULTS

Four SNPs in the regions of the APOE, LOC100129500, TOMM40, and EPC2 genes reached genome-wide significance for associations with one or more CSF biomarkers. SNPs in CCDC134, ABCG2, SREBF2, and NFATC4, although not reaching genome-wide significance, were identified as potential candidates.

CONCLUSIONS

In addition to known candidate genes, APOE, TOMM40, and one hypothetical gene LOC100129500 partially overlapping APOE; one novel gene, EPC2, and several other interesting genes were associated with CSF biomarkers that are related to AD. These findings, especially the new EPC2 results, require replication in independent cohorts.

Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging

Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5% cross-validation accuracy for classifying AD and HC, and 72.3% accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.