Heritability Of Alzheimer's Disease Research Articles

Heritability of Alzheimer's disease plasma biomarkers: A nuclear twin family design.

Alzheimer's disease (AD) is a highly heritable disease (60%-80%). Amyloid beta (Aβ) 42/40, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) are plasma biomarkers for AD. Clinical biomarker research would be served by an understanding of the sources of variance in these markers. Blood concentrations of Aβ42/40, NfL, and GFAP of twins and their families (monozygotic twins: 1574, dizygotic twins: 1266, other: 3657) were analyzed on the Simoa HD-X. Twin-family models were used to estimate proportional genetic contributions to the variance in biomarker levels. Heritability estimates were 16% for Aβ42/40, 42% for NfL, and 60% for GFAP. NfL and GFAP were significantly correlated with each other (0.37) but not with Aβ42/40. The heritability of Aβ42/40 (16%) is lower than the heritability of AD, suggesting strong environmental influences on this biomarker. The lack of correlation between NfL/GFAP and Aβ42/40 indicates these markers may be on different biological pathways. Heritability is found for glial fibrillary acidic protein (60%), neurofilament light chain (42%), and amyloid beta (Aβ) 42/40 (16%) plasma levels. Aβ42/40 plasma levels are sensitive to person-specific environmental influences.

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

Interpretation of 10years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity.

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

What does heritability of Alzheimer's disease represent?

Both late-onset Alzheimer's disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. SNP-based heritability of late onset Alzheimer's disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.

The role of structural variations in Alzheimer's disease and other neurodegenerative diseases.

Dozens of single nucleotide polymorphisms (SNPs) related to Alzheimer's disease (AD) have been discovered by large scale genome-wide association studies (GWASs). However, only a small portion of the genetic component of AD can be explained by SNPs observed from GWAS. Structural variation (SV) can be a major contributor to the missing heritability of AD; while SV in AD remains largely unexplored as the accurate detection of SVs from the widely used array-based and short-read technology are still far from perfect. Here, we briefly summarized the strengths and weaknesses of available SV detection methods. We reviewed the current landscape of SV analysis in AD and SVs that have been found associated with AD. Particularly, the importance of currently less explored SVs, including insertions, inversions, short tandem repeats, and transposable elements in neurodegenerative diseases were highlighted.

Polygenic risk scores for Alzheimer's disease predict MMSE decline in APOE4 carriers and noncarriers and the impact of sample overlap with GWAS summary statistics.

Heterogeneity in the progression of cognitive impairment, which is common in sporadic Alzheimer's disease (AD) trials, is especially challenging to predict in pre-symptomatic populations, and has a negative impact on clinical trial power. Heritability of AD extends beyond the APOE genotype, with multiple common genetic variants identified in large genome wide association studies (GWAS). Incorporating APOE and additional genetic variants into models has the potential to improve the prediction of disease progression. The limited volume of genetic cohorts in AD and the sample overlap in GWAS summary statistics poses some concern for polygenic risk score (PRS) overfitting. A PRS was calculated using genome-wide association study (GWAS) summary statistics for clinical AD diagnosis. PRS derived from this GWAS study was computed for participants drawn from two aging studies. Logistic regression models assessed the association between PRS and Mini Mental State Exam (MMSE) decline covarying for age, sex, education, APOE-ε4, and baseline MMSE score. Age and sex interactions with PRS were also assessed. Samples assumed to be used in the GWAS calculations were then removed from the test set and the impact to performance was analyzed. Participants in the training and test set showed similar baseline ages, years of education and baseline MMSE scores in the whole sample and after stratification by APOE4 carrier status. The PRS model was a significant predictor of MMSE decline, as well as in APOE4 carriers and noncarriers. Model performance was compared to the test set excluding the GWAS overlap samples and no significant difference was observed. The proposed model including PRS explains heterogeneity in cognitive decline above and beyond the APOE4 allele. Testing the model in a dataset excluding GWAS overlap samples did not result in a significant difference in performance and potential overfitting does not appear to be an impact. Utilization of demographic and genomic factors beyond APOE in PRS models could enhance clinical trial recruitment and stratification strategies for trial analyses, such that APOE4 carriers are selected for probable cognitive decline, in addition to APOE3 carriers that are high on polygenic risk.

Copy number variation (CNV) identification and association study on 3,928 Alzheimer's disease whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP).

Estimation of heritability of Alzheimer's Disease (AD) ranges from 49-79%, but the conventional single nucleotide variants (SNVs) identified to date account for <50% of the AD heritability. The involvement of genetic variants, such as SNVs and copy number variations (CNVs), has been proposed to explain part of the missing heritability. To accelerate AD genetic discovery, the Alzheimer's Disease Sequencing Project (ADSP) sequenced cases and cognitively normal elder controls from multiple ethnic groups. Leveraging this large-scale Whole Genome Sequence (WGS) collection, we could detect and conduct AD genetic analyses in full-spectrum of CNVs (small or large, common or rare, and coding or non-coding in genomic regions). We developed a bioinformatics pipeline consisting of four steps. First, we applied three calling algorithms, CNVnator, JAX-CNV, and Smoove, on each sample. Results of each tool were then merged by Svimmer. Second, we employed GraphTyper for genotyping. GraphTyper is a graph-pangenome-based method that may mitigate bias of human references. Finally, we resolved issues of multiple CNVs overlapping in a region by bedtools. Once the CNV callset was obtained, we conducted an association study to identify CNV regions that contribute to AD. We consider conventional association methods as well as a new developed CNV-curve-centric strategy which models CNVs as piecewise constant curves to assess the CNV effect on AD. For each sample, we identified an average of 7,959 CNVs by three algorithms. After merging CNVs of all samples, we obtained 56,316 deletions and 16,390 duplications for GraphTyper joint genotyping. The preliminary results of PLINK permutation-based test on the chromosome 19 indicated significant CNVs (p-value < 0.05 after adjusting for multiple tests) on genes UNC13A, IGSF23, KMT2B, LIN37, PSENEN, IGFLR1, and U2AF1L4. UNC13A, KMT2B, and PSENEN have been discovered to be associated in frontotemporal dementia, dystonia, and AD. Further validations of those CNVs are necessary. We developed a scalable CNV detection pipeline and applied it to 3,928 ADSP WGS samples. The preliminary results of the association study validate some known AD, dementia, and dystonia genes. To the best of our knowledge, this is the first large-scale CNV investigation of AD using WGS data.

Meta-GWAS of amyloid burden endophenotype combining PET and CSF results.

It is well known that Alzheimer's disease (AD) has a strong genetic component. Even though it is a highly heritable disease, a big fraction of AD heritability remains to be elucidated. Genetic analysis of endophenotypes tightly linked to the disease might help to identify or confirm AD genetic risk factors. GWAS results Amyloid β (Aβ42) in cerebrospinal fluid (CSF) or positron emission tomography (PET) measures were combined in a single meta-analysis. Six independent GWAS were run using a generalized linear model with four principal components and status as a covariate (PLINK software). The datasets had data for two different amyloid endophenotypes: CSF-Aβ42 levels from ADNI, Marqués de Valdecilla Hospital (Santander) and Fundació ACE (N= 1051); or PET-SUVR (amyloid global standardized uptake ratio) from ADNI and Fundació ACE (N= 607). GWAS results were meta-analysed using the sample size weighted method (METAL software). To reduce false positive findings, only genetic signals with opposite effect direction in PET and CSF endophenotypes were considered. Finally, we tried to replicate previously known genes for AD (de Rojas et al., MedRxiv 2019). After selecting SNPs with opposite effect on CSF and PET associations (N= 325051), we only observed genome wide significance in APOE locus (rs429358, P-value= 2.202×10-48) in the meta-GWAS. We also observed several suggestive signals in chromosomes 3, 5 and 6 (rs72977014, rs13164192 and rs4960198); further studies are needed to add evidence of AD association. Finally, relative to known AD genes we were able to replicate CR1 (rs4844610, P-value= 0.045) signal. By combining data from both β-amyloid endophenotypes (CSF and PET) we were able to replicate some well-known AD genes indicating that this approach could be a useful strategy to identify novel variants increasing the knowledge of AD genetic architecture.

Whole-Exome Sequencing Analysis of Alzheimer's Disease in Non-APOE*4 Carriers.

The genetics of late-onset Alzheimer's disease (AD) is complex due to the heterogeneous nature of the disorder. APOE*4 is the strongest genetic risk factor for AD. Genome-wide association studies have identified more than 30 additional loci, each having relatively small effect size. Known AD loci explain only about 30% of the genetic variance, and thus much of the genetic variance remains unexplained. To identify some of the missing heritability of AD, we analyzed whole-exome sequencing (WES) data focusing on non-APOE*4 carriers from two WES datasets: 720 cases and controls from the University of Pittsburgh and 7,252 cases and controls from the Alzheimer's Disease Sequencing Project. Following separate WES analyses in each dataset, we performed meta-analysis for overlapping markers present in both datasets. Among the four variants reaching the exome-wide significance threshold, three were from known AD loci: APOE/rs7412 (odds ratio (OR) = 0.40; p = 5.46E-24), TOMM40/rs157581 (OR = 1.49; p = 4.04E-07), and TREM2/rs75932628 (OR = 4.00; p = 1.15E-07). The fourth significant variant, rs199533, was from a novel locus on chromosome 17 in the NSF gene (OR = 0.78; p = 2.88E-07). NSF was also significant in the gene-based analysis (p = 1.20E-05). In the GTEx data, NSF/rs199533 is a cis-eQTL for multiple genes in the brain and blood, including NSF that is highly expressed across all brain tissues, including regions that typically show amyloid-β accumulation. Further characterization of genes that are affected by NSF/rs199533 may help to shed light on the roles of these genes in AD etiology.

The multiplex model of the genetics of Alzheimer's disease.

Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

Faster progression from MCI to probable AD for carriers of a single-nucleotide polymorphism associated with type 2 diabetes

Sporadic Alzheimer's disease (AD), as opposed to its autosomal dominant form, is likely caused by a complex interaction of genetic, environmental, and health lifestyle factors. Twin studies indicate that sporadic AD heritability could be between 58% and 79%, around half of which is explained by the ε4 allele of the apolipoprotein E (APOE4). We hypothesized that genes associated with known risk factors for AD, namely hypertension, hypercholesterolemia, obesity, diabetes, and cardiovascular disease, would contribute significantly to the remaining heritability. We analyzed 22 AD-associated single-nucleotide polymorphisms (SNPs), associated with these risk factors, that were included in the sequencing data of the Alzheimer's Disease Neuroimaging Initiative 1 data set, which included 355 participants with mild cognitive impairment (MCI). We built survival models with the selected SNPs to predict progression of MCI to probable AD over the 10-year follow-up of the study. The rs391300 SNP, located on the serine racemase (SRR) gene and linked to increased susceptibility to type 2 diabetes, was associated with progression from MCI to probable AD.

A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease.

A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

Higher prevalence of cerebral white matter hyperintensities in homozygous APOE-ɛ4 allele carriers aged 45–75: Results from the ALFA study

Cerebral white matter hyperintensities are believed the consequence of small vessel disease and are associated with risk and progression of Alzheimer's disease. The ɛ4 allele of the APOE gene is the major factor accountable for Alzheimer's disease heritability. However, the relationship between white matter hyperintensities and APOE genotype in healthy subjects remains controversial. We investigated the association between APOE-ɛ4 and vascular risk factors with white matter hyperintensities, and explored their interactions, in a cohort of cognitively healthy adults (45–75 years). White matter hyperintensities were assessed with the Fazekas Scale from magnetic resonance images (575 participants: 74 APOE-ɛ4 homozygotes, 220 heterozygotes and 281 noncarriers) and classified into normal (Fazekas < 2) and pathological (≥2). Stepwise logistic regression was used to study the association between pathological Fazekas and APOE genotype after correcting for cardiovascular and sociodemographic factors. APOE-ɛ4 homozygotes, but not heterozygotes, bear a significantly higher risk (OR 3.432; 95% CI [1.297–9.082]; p = 0.013) of displaying pathological white matter hyperintensities. As expected, aging, hypertension and cardiovascular and dementia risk scales were also positively associated to pathological white matter hyperintensities, but these did not modulate the effect of APOE-ɛ4/ɛ4. In subjects at genetic risk of developing Alzheimer's disease, the control of modifiable risk factors of white matter hyperintensities is of particular relevance to reduce or delay dementia’s onset.

New Genome-Wide Methods for Elucidation of Candidate Copy Number Variations (CNVs) Contributing to Alzheimer's Disease Heritability.

The complexity of human genetic variation has been extended by the observation of abundant and widespread variation in the copy number of submicroscopic DNA segments. The discovery of this novel level of genome organization opened new possibilities concerning the genetic variation that may confer susceptibility to or cause disease. Copy number variants (CNVs) influence gene expression, phenotypic variation and adaptation by altering gene dosage and genome organization. Concordant with the common disease common variant hypothesis these structural variants are now subject to interrogation for disease association. Alzheimer's disease (AD) is a progressive neurodegenerative disease with an estimated heritability of 60-80 %. Large scale genome-wide association studies (GWAS) using high frequency single nucleotide polymorphism (SNP) variants identified ten loci which do not account for the measured heritability. To find the missing heritability systematic assessment of all mutational mechanisms needs to be performed. Between the powerful SNP-GWAS studies and the planned Whole Genome Sequencing projects the contribution of copy number variation (CNV) to the genetic architecture of AD needs to be studied fully.

Linking Genetics of Brain Changes to Alzheimer's Disease: Sparse Whole Genome Association Scan of Regional MRI Volumes in the ADNI and AddNeuroMed Cohorts.

Alzheimer's disease (AD) is a highly heritable disease, but until recently few replicated genetic markers have been identified. Markers identified so far are likely to account for only a tiny fraction of the heritability of AD and many more genetic risk alleles are thought to be undiscovered. Identifying genetic markers for AD using combined analysis of genetics and brain imaging data. Imaging quantitative trait loci (iQTLs) has recently emerged as an interesting research area for linking genetics of brain changes to AD. We consider a genome-wide association scan of 109 brain-wide regional imaging phenotypes to identify genetic susceptibility loci for AD from a combined set of 1,045 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the AddNeuroMed studies. We use one-SNP-at-a-time as well as multi-SNP Hyperlasso based iQTL methods for the analysis. We identified several novel markers associated with AD, namely HOMER2 (rs1256429; intronic, p = 8.7 × 10⁻¹⁰), EOMES (rs2724509; flanking), JAM2 (rs2829841; intronic), and WEE1 (rs10770042; coding). The SNP rs1256429 (HOMER2) was one of the top hits in Hyperlasso as well as in the single-SNP analysis showing an association with the volume of the right thalamus and AD, a brain region reported to be linked with AD in several studies. We believe that the markers identified in this study are novel additions to the existing list of genetic variants associated with AD which can be validated in future replicated studies.

Integrating Genome-Wide Association Study and Brain Expression Data Highlights Cell Adhesion Molecules and Purine Metabolism in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. However, a large proportion of AD heritability has yet to be explained. We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in two AD GWAS. However, it is unclear whether CAM is present in the Genetic and Environmental Risk for Alzheimer's Disease Consortium (GERAD) GWAS and brain expression GWAS. Meanwhile, we think integrating AD GWAS and AD brain expression datasets may provide complementary information to identify important pathways involved in AD. Here, we conducted a systems analysis using (1) KEGG pathways, (2) large-scale AD GWAS from GERAD (n = 11,789), (3) two brain expression GWAS datasets (n = 399) from the AD cerebellum and temporal cortex, and (4) previous results from pathway analysis of AD GWAS. Our results indicate that (1) CAM is a consistent signal in five AD GWAS; (2) CAM is the most significant signal in AD; (3) we confirmed previous AD risk pathways related to immune system and diseases, and cardiovascular disease, etc.; and (4) we highlighted the purine metabolism pathway in AD for the first time. We believe that our results may advance our understanding of AD mechanisms and will be very informative for future genetic studies in AD.

Genome-Wide Scan for Copy Number Variation Association with Age at Onset of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease with high prevalence, which imposes a substantial public health problem. The heritability of AD is estimated at 60-80% forecasting the potential use of genetic biomarkers for risk stratification in the future. Several large scale genome-wide association studies using high frequency variants identified 10 loci accountable for only a fraction of the estimated heritability. To find the missing heritability, systematic assessment of various mutational mechanisms needs to be performed. This copy number variation (CNV) genome-wide association study with age at onset (AAO) of AD identified 5 CNV regions that may contribute to the heritability of AAO of AD. Two CNV events are intragenic causing a deletion in CPNE4. In addition, to further study the mutational load at the 10 known susceptibility loci, CNVs overlapping with these loci were also catalogued. We identified rare small events overlapping CR1 and BIN1 in AD and normal controls with opposite CNV dosage. The CR1 events are consistent with previous reports. Larger scale studies with deeper genotyping specifically addressing CNV are needed to evaluate the significance of these findings.

Autosomal Recessive Causes Likely in Early-Onset Alzheimer Disease

To determine the genetic contribution to non-autosomal dominant early-onset Alzheimer disease (EOAD) (onset age ≤60 years) cases and identify the likely mechanism of inheritance in those cases. A liability threshold model of disease was used to estimate heritability of EOAD and late-onset Alzheimer disease (AD) using concordance for AD among parent-offspring pairs. The Uniform Data Set, whose participants were collected from 32 US Alzheimer's Disease Centers, maintained by the National Alzheimer's Coordinating Center. Individuals with probable AD and detailed parental history (n=5370). The concordance among relatives and heritability of EOAD and late-onset AD. For late-onset AD (n=4302), we found sex-specific parent-offspring concordance that ranged from approximately 10% to 30%, resulting in a heritability of 69.8% (95% confidence interval, 64.6%-75.0%), and equal heritability for both sexes regardless of parental sex. For EOAD (n=702), we found that the parent-offspring concordance was 10% or less and concordance among siblings was 21.6%. Early-onset AD heritability was 92% to 100% for all likely values of EOAD prevalence. We confirm late-onset AD is a highly polygenic disease. By contrast, the data for EOAD suggest it is an almost entirely genetically based disease, and the patterns of observed concordance for parent-offspring pairs and among siblings lead us to reject the hypotheses that EOAD is a purely dominant, mitochondrial, X-linked, or polygenic disorder. The most likely explanation of the data is that approximately 90% of EOAD cases are due to autosomal recessive causes.

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.

Genetic and Genomic Aspects of Alzheimer's Disease

Alzheimer's disease (AD) is the most common chronic neurodegenerative disease today, afflicting 35 million people worldwide. Age is the major risk factor. The heritability of AD is estimated to be around 60%. Less than 5% of AD cases are familial with early-onset of disease caused by specific gene mutations. Genetic studies over the past 2 decades have provided invaluable insights into this complex disease. Here we review AD from the latest genome-wide association studies (GWAS), and a brief review of the transcriptomics, proteomics, metabolomics and epigenetics. Ultimately, a system-wide approach is essential to integrating the diverse and complicated findings into a meaningful understanding of AD.