Diane Hu-Lince

GAB2 Alleles Modify Alzheimer's Risk in APOE ε4 Carriers

The apolipoprotein E (APOE) epsilon4 allele is the best established genetic risk factor for late-onset Alzheimers disease (LOAD). We conducted genome-wide surveys of 502,627 single-nucleotide polymorphisms (SNPs) to characterize and confirm other LOAD susceptibility genes. In epsilon4 carriers from neuropathologically verified discovery, neuropathologically verified replication, and clinically characterized replication cohorts of 1411 cases and controls, LOAD was associated with six SNPs from the GRB-associated binding protein 2 (GAB2) gene and a common haplotype encompassing the entire GAB2 gene. SNP rs2373115 (p = 9 x 10(-11)) was associated with an odds ratio of 4.06 (confidence interval 2.81-14.69), which interacts with APOE epsilon4 to further modify risk. GAB2 was overexpressed in pathologically vulnerable neurons; the Gab2 protein was detected in neurons, tangle-bearing neurons, and dystrophic neuritis; and interference with GAB2 gene expression increased tau phosphorylation. Our findings suggest that GAB2 modifies LOAD risk in APOE epsilon4 carriers and influences Alzheimers neuropathology.

A survey of genetic human cortical gene expression

It is widely assumed that genetic differences in gene expression underpin much of the difference among individuals and many of the quantitative traits of interest to geneticists. Despite this, there has been little work on genetic variability in human gene expression and almost none in the human brain, because tools for assessing this genetic variability have not been available. Now, with whole-genome SNP genotyping arrays and whole-transcriptome expression arrays, such experiments have become feasible. We have carried out whole-genome genotyping and expression analysis on a series of 193 neuropathologically normal human brain samples using the Affymetrix GeneChip Human Mapping 500K Array Set and Illumina HumanRefseq-8 Expression BeadChip platforms. Here we present data showing that 58% of the transcriptome is cortically expressed in at least 5% of our samples and that of these cortically expressed transcripts, 21% have expression profiles that correlate with their genotype. These genetic-expression effects should be useful in determining the underlying biology of associations with common diseases of the human brain and in guiding the analysis of the genomic regions involved in the control of normal gene expression.

Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Evidence for an association between KIBRA and late-onset Alzheimer's disease

We recently reported evidence for an association between the individual variation in normal human episodic memory and a common variant of the KIBRA gene, KIBRA rs17070145 (T-allele). Since memory impairment is a cardinal clinical feature of Alzheimers disease (AD), we investigated the possibility of an association between the KIBRA gene and AD using data from neuronal gene expression, brain imaging studies, and genetic association tests. KIBRA was significantly over-expressed and three of its four known binding partners under-expressed in AD-affected hippocampal, posterior cingulate and temporal cortex regions (P<0.010, corrected) in a study of laser-capture microdissected neurons. Using positron emission tomography in a cohort of cognitively normal, late-middle-aged persons genotyped for KIBRA rs17070145, KIBRA T non-carriers exhibited lower glucose metabolism than did carriers in posterior cingulate and precuneus brain regions (P<0.001, uncorrected). Lastly, non-carriers of the KIBRA rs17070145 T-allele had increased risk of late-onset AD in an association study of 702 neuropathologically verified expired subjects (P=0.034; OR=1.29) and in a combined analysis of 1026 additional living and expired subjects (P=0.039; OR=1.26). Our findings suggest that KIBRA is associated with both individual variation in normal episodic memory and predisposition to AD.

Sorl1 as an Alzheimer’s Disease Predisposition Gene?

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressively disabling impairments in memory, cognition, and non-cognitive behavioural symptoms. Sporadic AD is multifactorial and genetically complex. While several monogenic mutations cause early-onset AD and gene alleles have been suggested as AD susceptibility factors, the only extensively validated susceptibility gene for late-onset AD is the apolipoprotein E (APOE) Ε4 allele. Alleles of the APOE gene do not account for all of the genetic load calculated to be responsible for AD predisposition. Recently, polymorphisms across the neuronal sortilin-related receptor (SORL1) gene were shown to be significantly associated with AD in several cohorts. Here we present the results of our large case-control whole-genome scan at over 500,000 polymorphisms which presents weak evidence for association and potentially narrows the association interval.

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BackgroundPooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.ResultsWe report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.ConclusionOur results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.

Phenotypic definition of chiari type I malformation coupled with high-density SNP genome screen shows significant evidence for linkage to regions on chromosomes 9 and 15

Chiari type I malformation (CMI; OMIM 118420) is narrowly defined when the tonsils of the cerebellum extend below the foramen magnum, leading to a variety of neurological symptoms. It is widely thought that a small posterior fossa (PF) volume, relative to the total cranial volume leads to a cramped cerebellum and herniation of the tonsils into the top of the spinal column. In a collection of magnetic resonance imagings (MRIs) from affected individuals and their family members, we measured correlations between ten cranial morphologies and estimated their heritability in these families. Correlations between bones delineating the PF and significant heritability of PF volume (0.955, P = 0.003) support the cramped PF theory and a genetic basis for this condition. In a collection of 23 families with 71 affected individuals, we performed a genome wide linkage screen of over 10,000 SNPs across the genome to identify regions of linkage to CMI. Two‐point LOD scores on chromosome 15 reached 3.3 and multipoint scores in this region identified a 13 cM region with LOD scores over 1 (15q21.1‐22.3). This region contains a biologically plausible gene for CMI, fibrillin‐1, which is a major gene in Marfan syndrome and has been linked to Shprintzen–Goldberg syndrome, of which CMI is a distinguishing characteristic. Multipoint LOD scores on chromosome 9 maximized at 3.05, identifying a 40 cM region with LOD scores over 1 (9q21.33‐33.1) and a tighter region with multipoint LOD scores over 2 that was only 8.5 cM. This linkage evidence supports a genetic role in Chiari malformation and justifies further exploration with fine mapping and investigation of candidate genes in these regions.

The Autism Genome Project: goals and strategies.

Autism is a complex neurodevelopmental disorder with a broad spectrum of symptoms and varying severity. Currently, no biological diagnosis exists. Although there has been a significant increase in autism genetics research recently, validated susceptibility genes for the most common, sporadic forms of autistic disorder, as well as familial autism, have yet to be identified. The identification of autism-susceptibility genes will not only assist in the identification and/or development of better medications that can help improve the health and neurodevelopment of children with autism, but will also allow for better perinatal diagnosis. The Autism Genome Project (AGP) is a large-scale, collaborative genetics research project initiated by the National Alliance for Autism Research and the National Institutes of Health, and is aimed at sifting through the human genome in search of autism-susceptibility genes. Phase I of the AGP will consist of genome-wide scans utilizing both SNP array and microsatellite technologies. Linkage analysis will subsequently be performed on approximately 1500 pedigrees as will downstream fine-mapping and sequencing of the critical linkage intervals. Ultimately, the vision will be to identify the exact nucleotide variants within genes which give rise to predisposition. The AGP intends to move the field of autism clinical management forward by answering questions about the causal mechanisms underlying the pathophysiology of autism. From this knowledge, therapeutic targets for drug treatments, and ultimately, a newborn screening diagnostic that would allow for early intervention, can begin to be developed.

SNiPer: Improved SNP genotype calling for Affymetrix 10K GeneChip microarray data

BackgroundHigh throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip® Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.ResultsA database of 948 individuals genotyped on the GeneChip® Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data.ConclusionThe correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip® without sacrificing accuracy, thereby increasing the amount of valid data generated.

Genome‐wide SNP arrays as a diagnostic tool: Clinical description, genetic mapping, and molecular characterization of Salla disease in an Old Order Mennonite population

An Old Order Mennonite child was evaluated for gross motor delay, truncal ataxia, and slow linear growth. The diagnostic evaluation, which included sub‐specialty consultations, neuroimaging, and metabolic testing, was long, costly, and did not yield a diagnosis. Recognition of a similarly affected second cousin prompted a genome‐wide homozygosity mapping study using high‐density single nucleotide polymorphism (SNP) arrays. SNP genotypes from two affected individuals and their parents were used to localize the disease locus to a 14.9 Mb region on chromosome 6. This region contained 55 genes, including SLC17A5, the gene encoding the lysosomal N‐acetylneuraminic acid transport protein. Direct sequencing of SLC17A5 in the proband revealed homozygosity for the 115C → T (R39C) sequence variant, the common cause of Salla disease in Finland. Three additional affected Mennonite individuals, ages 8 months to 50 years, were subsequently identified by directed molecular genetic testing. This small‐scale mapping study was rapid, inexpensive, and analytically simple. In families with shared genetic heritage, genome‐wide SNP arrays with relatively high marker density allow disease gene mapping studies to be incorporated into routine diagnostic evaluations.