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Example: a SNP in the BDNF (brain-derived neurotrophic factor) gene has been found, which converts a valine (Val) to a methionine (Met) in the pro-region of the BDNF protein. BDNF is a key regulator for hippocampal long-term potentiation (LTP), a cellular model for learning and memory, in rodents. Human subjects with a Met allele exhibit lower neuronal activity and abnormal hippocampal activation, and Met/Met subjects exhibit impairment in episodic memory. From studies with rodent hippocampal neurons in cultures, it appeared that Met-BDNF cannot be correctly targeted to the synapses and cannot undergo activity-dependent secretion. Thus, synaptic targeting and activity-dependent secretion of BDNF may be critical for human memory and hippocampal function. Hence, a combination of functional analyses of the consequences of a human SNP with the rodent mechanistic study using gene manipulation techniques appears to be a powerful strategy to reveal genetic mechanisms underlying human cognitive dysfunctions. This strategy may also have general implications in studying functions of a specific gene in normal humans.

See also under "The Human Genome and HapMap projects".

Copy Number Variation (CNV)

Currently, structural variation of the human genome is commanding a great deal of attention. In the postgenomic era, the availability of human genome sequence for genome-wide analysis has revealed higher-order architectural features (i.e., beyond primary sequence information) that may cause genomic instability and susceptibility to genomic rearrangements. Nevertheless, it is perhaps less generally appreciated that any two humans contain more base-pair differences due to structural variation of the genome than resulting from SNPs. Copy-number variation (CNV; deletion/insertion/duplication) is the most prevalent type of structural variation in the human genome, and contributes significantly to genetic heterogeneity. CNVs are now amenable to genome-wide association studies so that their influence on human phenotypic diversity and disease susceptibility may soon be more readily determined. De novo genomic rearrangements have been shown to cause both chromosomal and Mendelian disease, as well as sporadic traits, but our understanding of the extent to which genomic rearrangements, gene CNV, and/or gene dosage alterations are responsible for common and complex neurological traits is limited. Interestingly, several neurodegenerative and neurodevelopmental disorders are now known to be caused by disparate recurrent and nonrecurrent genomic rearrangements that are mediated or stimulated by complex regional genomic architecture occurring throughout the human genome. These genomic disorders include peripheral (PNS) and central (CNS) nervous system neuropathies, well-recognized syndromes with characteristic behavioral or neurocognitive phenotypes, and also a growing number of psychiatric illnesses. Thus, there is indisputable evidence that CNVs can play a role in the pathogenesis of neurodevelopmental and neurodegenerative disorders.

Regarding the rearrangements resulting in CNV, the majority of rearrangement breakpoints are found to be in the vicinity of repeated sequences (see Figure below). Because of the surprisingly high level of CNV found in the normal population, genomic rearrangements mediated or stimulated by repeated sequences and the resulting CNV of a single gene or multiple genes important for nervous system function may potentially account for a higher proportion of heritable neurologic and psychiatric traits than is currently realized. The latest studies using microarray technology have demonstrated that as much as 12% of the human genome and thousands of genes are variable in copy number, and this diversity is likely to be responsible for a significant proportion of normal phenotypic variation. Current challenges involve developing methods not only for detecting and cataloging CNVs in human populations but also for determining the association of CNVs with biological function, recent human evolution, and common and complex human disease.

Duplications and deletions are generated when nonallelic homologous recombination is mediated by directly oriented low copy repeats (LCRs). In general, genomic features favorable for rearrangements to occur via nonallelic homologous recombination and cause gene CNVs consist of LCRs (1) >10 kb in size, (2) with >97% sequence identity, (3) directly oriented, (4) within 5 Mb of each other, and (5) located on the same chromosome (i.e., intrachromosomal).

Analysis of CNVs

Medium-sized genetic alterations, such as those seen for copy number variants, may involve single exons or entire genes, or a part of a chromosomal band. Standard cytogenetic and fluorescent in situ hybridization (FISH) techniques are useful for looking at large gross changes in the structure of the chromosomes, and even within smaller defined parts of chromosomes. However, the ability to identify smaller rearrangements, such as single exon deletions, is unlikely to be successful. These techniques are also difficult to perform as high throughput, when compared to other methods. In the past, Southern blotting has also been used to detect deletions and duplications, and provide a reliable semi-quantitative approach. However, this technique is severely limited by its low throughput, since only a limited number of samples can be analysed at any one time, and it will take several days to arrive at an answer. Newer techniques are now required to identify these copy number variants. Two main types of methods are employed. The first will identify variants and establish their location, such as whole genome-based analyses, including microarray-based comparative genomic hybridisation (CGH; measuring the fluorescence ratio along the length of each chromosome identifies regions of relative loss and gain in the test sample; see Figure below) and high-density SNP genotyping arrays. Whole-genome scanning methods enable us to interrogate the genome at a resolution intermediate between that of cytogenetic analysis using microscopy (>5–10 Mb) and that of DNA sequencing (1–700 bp). The second types of methods are those allowing validation of the outcome of these genome-based studies. In some situations, they may require repeated analysis with larger numbers of patient samples using a similar technology (array-CGH, or SNP arrays). However, it is also possible that when specific copy number variants are identified, variant-specific assays will need to be developed. At the moment, quantitative PCR methods are being employed to identify and screen gene-based deletions and duplications.

Detection and validation of a spontaneous deletion in a patient with Asperger syndrome. Parents were ascertained and determined to have no change in copy number. Probe ratio data are shown for the patient (blue) and the mother (red) and father (green) for 85K ROMA (A) and 244K Agilent CGH (B) platforms. (C) The map of annotated known genes; the genomic region estimated to be deleted is boxed in red.

The genome	Functional Genomics	Genome-wide association studies (GWAS)
Genomics research	Pharmacogenomics	Molecular networks
The Human Genome and HapMap Projects	Genetic variations: SNPs and CNVs

As we saw in the previous sections, a major challenge in Neuroscience is to understand the genetic basis of higher brain functions in human. One strategy is to take advantage of the ever-growing knowledge about the nucleotide sequences of whole genomes and to search for a link between genetic variations in these genomes and differences in brain functions. Single-Nucleotide Polymorphisms (SNPs) are single-nucleotide differences (polymorphisms) or one-letter variations in the DNA sequence that occur when a single nucleotide (A, T, C, or G) in the genome sequence is altered. Recently, it became clear that also genomic rearrangements resulting in copy number variations (CNVs) represent important genetic variations among individuals. Single-Nucleotide Polymorphism (SNP)

SNPs are caused when nucleotides replicate less than perfectly or mutate. This replication process is enormously complex, and happens perfectly most of the time, but occasional imperfections or mutations do occur. In humans, the mutations occur at about 200 mutations per generation. Thus, each child receives about 200 such mutations from his father, and 200 from his mother, for a total of 400 mutations, spread more-or-less randomly across the genome. Because each child will only pass on about half of the father's mutations to the next generation, virtually all of the father's 200 mutations will be gone after 20 generations or 400 years. Occasionally, however, one persists and becomes established in the population. There are millions of SNPs in the human genome. In fact, it is estimated that if any two genomes are compared, there will be on average one SNP every 1,200 base pairs. SNPs thus contribute to differences among individuals that are present in humans with a frequency of about once in every 1,200 bases. Most of the mutations have no effect on the way an individual looks or functions. A small fraction of mutations are beneficial, allowing the species to evolve and adapt to new environments, while others are quite catastrophic. Yet other mutations produce only subtle effects, not noticeable by themselves, but sufficient to produce a larger effect when combined with other mutations. The causes of common disease are very complex. We know that both the environment and genetics play important roles. Research in human genetics has revealed that the majority of common human diseases, as well as most human responses to medicines, involve the interaction of many genes i.e. they are polygenic. This makes understanding the underlying causes extremely difficult, as they are likely to be influenced by a large number of SNPs found in a variety of genes and intergenic regions that act together in specific combinations. By itself, each individual SNP may have only a small effect on disease susceptibility or drug response. This complexity highlights the futility of attempting to identify disease causality (or drug response) through analysis of individual genes, small regions of chromosomes, or even whole chromosomes. Instead, detailed analysis of entire genomes is required to identify these polygenic effects. The challenge is to be able to identify which of the millions of SNPs throughout the entire genome contribute to a particular disease or drug response. A high-density oligonucleotide array approach offers the ability to analyze large numbers of SNPs in large numbers of individuals using automated methods. The arrays consist of DNA probes (short segments of DNA) that are synthesized in pre-determined positions on glass surfaces. Sample DNA hybridized to the arrays will bind in such a way that the genotype of each of the SNPs can be determined. The probes are designed with multiple redundancy, thus increasing the accuracy with which each SNP is assayed. Methods have been developed to use such high-density oligonucleotide arrays to genotype more than 1.5 million SNPs in either pooled or individual DNA samples in an accurate, rapid, and cost-effective manner.
		Finding sequence variation: examine multiple sequences from the same genomic region.
Microarray-based SNP-detection	High-throughput SNP detection using microarrays. Eight features (four for the forward strand and four for the reverse complement strand) are associated with every queried site. Each feature consists of a 25-base oligonucleotide. The 13th base is the query base and all possible genotypes are tested.