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Schizophrenia associated genes, proteins and biallelic markers

Schizophrenia associated genes, proteins and biallelic markers description/claims

This application is a divisional of U.S. patent application Ser. No. 11/145,703, filed Jun. 6, 2005, which is a divisional of U.S. patent application Ser. No. 10/147,603, filed May 16, 2002, now U.S. Pat. No. 7,067,627, which is a divisional of U.S. patent application Ser. No. 09/539,333, filed Mar. 30, 2000, now U.S. Pat. No. 6,476,208, which is a continuation-in-part of U.S. patent application Ser. No. 09/416,384, filed Oct. 12, 1999, now abandoned, which claims the benefit of U.S. Provisional Application Nos. 60/103,955, filed Oct. 13, 1998; 60/106,457, filed Oct. 30, 1998; and 60/132,277, filed May 3, 1999. U.S. patent application Ser. No. 09/539,333 claims the benefit of U.S. Provisional Patent Application Nos. 60/126,903, filed Mar. 30, 1999; 60/131,971, filed Apr. 30, 1999; 60/132,065, filed Apr. 30, 1999; 60/143,928, filed Jul. 14, 1999; 60/145,915, filed Jul. 27, 1999; 60/146,453, filed Jul. 29, 1999; 60/146,452, filed Jul. 29, 1999; and 60/162,288, filed Oct. 28, 1999. All of the above applications are hereby incorporated by reference in their entireties.

The Sequence Listing for this application is labeled “Seq-List.txt” which was created on Jan. 9, 2005, and is 1095 KB. The entire content is incorporated herein by reference in its entirety.

The invention concerns the human sbg1, g34665, sbg2, g35017 and g35018 genes, polynucleotides, polypeptides biallelic markers, and human chromosome 13q31-q33 biallelic markers. The invention also concerns the association established between schizophrenia and bipolar disorder and the biallelic markers and the sbg1, g34665, sbg2, g35017 and g35018 genes and nucleotide sequences. The invention provides means to identify compounds useful in the treatment of schizophrenia, bipolar disorder and related diseases, means to determine the predisposition of individuals to said disease as well as means for the disease diagnosis and prognosis.

Advances in the technological armamentarium available to basic and clinical investigators have enabled increasingly sophisticated studies of brain and nervous system function in health and disease. Numerous hypotheses both neurobiological and pharmacological have been advanced with respect to the neurochemical and genetic mechanisms involved in central nervous system (CNS) disorders, including psychiatric disorders and neurodegenerative diseases. However, CNS disorders have complex and poorly understood etiologies, as well as symptoms that are overlapping, poorly characterized, and difficult to measure. As a result future treatment regimes and drug development efforts will be required to be more sophisticated and focused on multigenic causes, and will need new assays to segment disease populations, and provide more accurate diagnostic and prognostic information on patients suffering from CNS disorders.

Neurotransmitters serve as signal transmitters throughout the body. Diseases that affect neurotransmission can therefore have serious consequences. For example, for over 30 years the leading theory to explain the biological basis of many psychiatric disorders such as depression has been the monoamine hypothesis. This theory proposes that depression is partially due to a deficiency in one of the three main biogenic monoamines, namely dopamine, norepinephrine and/or serotonin.

In addition to the monoamine hypothesis, numerous arguments tend to show the value of taking into account the overall function of the brain and no longer only considering a single neuronal system. In this context, the value of dual specific actions on the central aminergic systems including second and third messenger systems has now emerged.

It is furthermore apparent that the main monoamine systems, namely dopamine, norepinephrine and serotonin, do not completely explain the pathophysiology of many CNS disorders. In particular, it is clear that CNS disorders may have an endocrine component; the hypothalamic-pituitary-adrenal (HPA) axis, including the effects of corticotrophin-releasing factor and glucocorticoids, plays an important role in the pathophysiology of CNS disorders.

In the hypothalamus-pituitary-adrenal (HPA) axis, the hypothalamus lies at the top of the hierarchy regulating hormone secretion. It manufactures and releases peptides (small chains of amino acids) that act on the pituitary, at the base of the brain, stimulating or inhibiting the pituitary's release of various hormones into the blood. These hormones, among them growth hormone, thyroid-stimulating hormone and adrenocorticotrophic hormone (ACTH), control the release of other hormones from target glands. In addition to functioning outside the nervous system, the hormones released in response to pituitary hormones also feed back to the pituitary and hypothalamus. There they deliver inhibitory signals that serve to limit excess hormone biosynthesis.

Neurotransmitter and hormonal abnormalities are implicated in disorders of movement (e.g. Parkinson's disease, Huntington's disease, motor neuron disease, etc.), disorders of mood (e.g. unipolar depression, bipolar disorder, anxiety, etc.) and diseases involving the intellect (e.g. Alzheimers disease, Lewy body dementia, schizophrenia, etc.). In addition, these systems have been implicated in many other disorders, such as coma, head injury, cerebral infarction, epilepsy, alcoholism and the mental retardation states of metabolic origin seen particularly in childhood.

Until recently, the identification of genes linked with detectable traits has relied mainly on a statistical approach called linkage analysis. Linkage analysis is based upon establishing a correlation between the transmission of genetic markers and that of a specific trait throughout generations within a family. Linkage analysis involves the study of families with multiple affected individuals and is useful in the detection of inherited traits, which are caused by a single gene, or possibly a very small number of genes. But linkage studies have proven difficult when applied to complex genetic traits. Most traits of medical relevance do not follow simple Mendelian monogenic inheritance. However, complex diseases often aggregate in families, which suggests that there is a genetic component to be found. Such complex traits are often due to the combined action of multiple genes as well as environmental factors. Such complex trait, include susceptibilities to heart disease, hypertension, diabetes, cancer and inflammatory diseases. Drug efficacy, response and tolerance/toxicity can also be considered as multifactoral traits involving a genetic component in the same way as complex diseases. Linkage analysis cannot be applied to the study of traits for which no large informative families are available. Moreover, because of their low penetrance, such complex traits do not segregate in a clear-cut Mendelian manner as they are passed from one generation to the next. Attempts to map such diseases have been plagued by inconclusive results, demonstrating the need for more sophisticated genetic tools.

Knowledge of genetic variation in the neuronal and endocrine systems is important for understanding why some people are more susceptible to disease or respond differently to treatments. Ways to identify genetic polymorphism and to analyze how they impact and predict disease susceptibility and response to treatment are needed.

Although the genes involved in the neuronal and endocrine systems represent major drug targets and are of high relevance to pharmaceutical research, we still have scant knowledge concerning the extent and nature of sequence variation in these genes and their regulatory elements. In the case where polymorphisms have been identified the relevance of the variation is rarely understood. While polymorphisms hold promise for use as genetic markers in determining which genes contribute to multigenic or quantitative traits, suitable markers and suitable methods for exploiting those markers have not been found and brought to bear on the genes related to disorders of the brain and nervous system.

The basis for accomplishing these goals is to use genetic association analysis to detect markers that predict susceptibility for these traits. Recently, advances in the fields of genetics and molecular biology have allowed identification of forms, or alleles, of human genes that lead to diseases. Most of the genetic variations responsible for human diseases identified so far, belong to the class of single gene disorders. As this name implies, the development of single gene disorders is determined, or largely influenced, by the alleles of a single gene. The alleles that cause these disorders are, in general, highly deleterious (and highly penetrant) to individuals who carry them. Therefore, these alleles and their associated diseases, with some exceptions, tend to be very rare in the human population. In contrast, most common diseases and non-disease traits, such as a physiological response to a pharmaceutical agent, can be viewed as the result of many complex factors. These can include environmental exposures (toxins, allergens, infectious agents, climate, and trauma) as well as multiple genetic factors.

Association studies seek to analyze the distributions of chromosomes that have occurred in populations of unrelated (at least not directly related) individuals. An assumption in this type of study is that genetic alleles that result in susceptibility for a common trait arose by ancient mutational events on chromosomes that have been passed down through many generations in the population. These alleles can become common throughout the population in part because the trait they influence, if deleterious, is only expressed in a fraction of those individuals who carry them. Identification of these “ancestral” chromosomes is made difficult by the fact that genetic markers are likely to have become separated from the trait susceptibility allele through the process of recombination, except in regions of DNA which immediately surround the allele. The identities of genetic markers contained within the fragments of DNA surrounding a susceptibility allele will be the same as those from the ancestral chromosome on which the allele arose. Therefore, individuals from the population who express a complex trait might be expected to carry the same set of genetic markers in the vicinity of a susceptibility allele more often than those who do not express the trait; that is these markers will show an association with the trait.

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