公开(公告)号：US20090192135A1

公开(公告)日：2009-07-30

申请号：US11887346

申请日：2006-03-28

申请人： Jason Samuel Simon ,  Maha Chabhar Karnoub ,  Michael E. Severino ,  David James Devlin ,  Andrew Stewart Plump ,  Eric E. Schadt

发明人： Jason Samuel Simon ,  Maha Chabhar Karnoub ,  Michael E. Severino ,  David James Devlin ,  Andrew Stewart Plump ,  Eric E. Schadt

IPC分类号： A61K31/397 ,  C12Q1/60 ,  C12Q1/68 ,  C40B30/04 ,  C07H21/04

CPC分类号： C12Q1/6883 ,  C12Q2600/106 ,  C12Q2600/156 ,  C12Q2600/172

摘要： The present invention relates to the identification and use of single nucleotide polymorphisms and haplotypes in the Niemann Pick C1-Like 1 (NPC1L1) gene. In particular, methods are provided for correlating NPC1L1 polymorphisms and haplo-types with the responsiveness of a pharmaceutically active compound administered to a human subject. The invention further relates to a method for estimating the responsiveness of a pharmaceutically active compound administered to a human subject which method comprises determining at least one polymorphism in the NPC1L1 gene. The methods are based on determining polymorphisms in the NPC1L1 gene and correlating the responsiveness of a pharmaceutically active compound in the human by reference to one or more polymorphism in NPC1L1. The invention further relates to isolated nucleic acids comprising within their sequence the polymorphisms as defined herein, to nucleic acid primers and oligonucleotide probes capable of hybridizing to such nucleic acids and to a diagnostic kit comprising one or more of such primers and probes for detecting a polymorphism in the NPC1L1 gene.

摘要翻译： 本发明涉及Niemann Pick C1-Like 1（NPC1L1）基因中单核苷酸多态性和单元型的鉴定和应用。 特别地，提供了将NPC1L1多态性和单体类型与施用于人类受试者的药学活性化合物的反应性相关联的方法。 本发明还涉及一种用于估计施用于人受试者的药学活性化合物的反应性的方法，所述方法包括确定NPC1L1基因中的至少一个多态性。 该方法基于确定NPC1L1基因中的多态性，并通过参照NPC1L1中的一个或多个多态性将药物活性化合物在人体中的反应性相关联。 本发明还涉及在其序列中包含本文所定义的多态性的分离的核酸与能够与此类核酸杂交的核酸引物和寡核苷酸探针以及包含一种或多种用于检测多态性的引物和探针的诊断试剂盒 在NPC1L1基因中。

公开(公告)号：US08185367B2

公开(公告)日：2012-05-22

申请号：US11587900

申请日：2005-05-02

申请人： Jun Zhu ,  Eric E. Schadt

发明人： Jun Zhu ,  Eric E. Schadt

IPC分类号： G06G7/58

CPC分类号： G06F19/12 ,  G06F19/24

摘要： The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human disease, but living systems more generally. The present invention provides novel gene network reconstruction algorithms that utilize naturally occurring genetic variations as a source of perturbations to elucidate the networks. The algorithms incorporate relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of these novel algorithms can be demonstrated via application to gene expression data from a segregating mouse population. The network derived from such data using the novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

摘要翻译： 哺乳动物系统中遗传网络的重建是生物研究的主要目标之一，特别是因为这些重建不仅涉及常见的多基因人类疾病，而且更广泛地阐明生物系统。 本发明提供了利用天然存在的遗传变异作为扰动源来阐明网络的新型基因网络重建算法。 该算法通过采用在贝叶斯网络重建问题中常用的最大似然度的广义评分函数，将相对转录丰度和来自分离群体的基因型数据结合起来。 这些新算法的实用性可以通过应用于分离的小鼠群体的基因表达数据来证明。 与使用相同数据导出的更经典的重建网络相比，使用新颖的网络重构算法从这种数据得到的网络能够捕获导致增加的预测能力的基因之间的因果关联。

公开(公告)号：US07729864B2

公开(公告)日：2010-06-01

申请号：US10558928

申请日：2004-05-28

申请人： Eric E. Schadt

发明人： Eric E. Schadt

IPC分类号： G06F19/00 ,  G06F15/00 ,  G11C17/00

CPC分类号： G01N33/5023 ,  G01N33/5082 ,  G01N33/6803 ,  G01N2500/00

摘要： Methods, computer program products and systems for identifying cellular constituents in a secondary tissue that serve as surrogate markers for a target gene expressed in a primary tissue of a species are provided. A classifier is constructed using cellular constituent abundances of cellular constituents in a first plurality of cellular constituents measured in the secondary tissue in a population. This population comprises a first and second subgroup. The classifier is based on a second plurality of cellular constituents that comprises all or a portion of the first plurality of cellular constituents. Abundance levels of each cellular constituent in the second plurality of cellular constituents varies between the first and second subgroup. All or portion of the population is classified into a plurality of subtypes using the classifier. Then, one or more cellular constituents that can discriminate members of the population between a first subtype and a second subtype in the plurality of subtypes are identified.

摘要翻译： 提供了用于鉴定作为在物种的初级组织中表达的靶基因的替代标记物的次级组织中的细胞成分的计算机程序产品和系统。 在群体中的次级组织中测量的第一多个细胞成分中，使用细胞组分的细胞组成丰度构建分类器。 该群体包括第一和第二子群。 分类器基于包含第一多个细胞成分的全部或一部分的第二多个细胞成分。 第二多个细胞组分中每个细胞成分的丰度水平在第一和第二亚组之间变化。 使用分类器将全部或部分群体分类为多个亚型。 然后，识别可以区分多个亚型中的第一亚型和第二亚型之间的群体成员的一个或多个细胞成分。

公开(公告)号：US07035739B2

公开(公告)日：2006-04-25

申请号：US10356857

申请日：2003-02-03

申请人： Eric E. Schadt ,  Stephanie A. Monks

发明人： Eric E. Schadt ,  Stephanie A. Monks

IPC分类号： G06F19/00 ,  G11C17/00 ,  G05B15/00

CPC分类号： G06F19/12 ,  G06F19/18 ,  G06F19/20 ,  G06F19/24

摘要： A method for associating a gene with a trait exhibited by one or more organisms in a plurality of organisms from a species. A genetic marker map is constructed from a set of genetic markers associated with the plurality of organisms. For each gene in a plurality of genes, a quantitative trait locus analysis is performed using the genetic marker map and a quantitative trait. The quantitative trait locus analysis produces quantitative trait locus data. A quantitative trait comprises an expression statistic for a gene. The expression statistic for a gene is derived from a cellular constituent level that corresponds to the gene in each organism in the plurality of organisms. The quantitative trait locus data are clustered from each quantitative trait locus analysis to form a quantitative trait locus interaction map. Clusters of genes in the map are identified as a candidate pathway group. An expression cluster map is used to refine the candidate pathway group. Multivariate analysis is used to validate the candidate pathway group as a set of genes that are genetically interacting.

公开(公告)号：US08600718B1

公开(公告)日：2013-12-03

申请号：US11985841

申请日：2007-11-16

申请人： Serguei Stepaniants ,  Pek Y. Lum ,  Roy Kuraisa ,  Eric E. Schadt

发明人： Serguei Stepaniants ,  Pek Y. Lum ,  Roy Kuraisa ,  Eric E. Schadt

IPC分类号： G06G7/58

CPC分类号： G06F19/18 ,  G06F19/20

摘要： Systems and methods for determining a functional relationship between pairs of cellular constituents are provided. A plurality of datasets is received. Each dataset represents an experimental condition and comprises measurement data for a plurality of cellular constituents from each of a plurality of organisms. Each respective dataset is represented by correlation coefficients. Each correlation coefficient for a respective dataset in the plurality of datasets represents a correlation between abundance measurement data for a pair of cellular constituents across the dataset. The plurality of correlation coefficients that represents a first dataset in the plurality of datasets is clustered, thereby determining their order. This order is applied to each remaining dataset thereby forming a plurality of correlation matrices. When a conserved area in the plurality of matrices is identified, the functional relationship between the first cellular constituent and the second constituent is determined to be present.

摘要翻译： 提供了用于确定细胞成分对之间功能关系的系统和方法。 接收多个数据集。 每个数据集表示实验条件，并且包括来自多个生物体中的每一个的多个细胞成分的测量数据。 每个相应的数据集由相关系数表示。 多个数据集中的相应数据集的每个相关系数表示跨数据集的一对细胞成分的丰度测量数据之间的相关性。 表示多个数据集中的第一数据集的多个相关系数被聚类，从而确定它们的顺序。 该顺序被应用于每个剩余的数据集，从而形成多个相关矩阵。 当识别多个矩阵中的保守区域时，确定出现第一细胞成分与第二成分之间的功能关系。

公开(公告)号：US08843356B2

公开(公告)日：2014-09-23

申请号：US10540405

申请日：2003-12-24

申请人： Eric E. Schadt ,  John Lamb

发明人： Eric E. Schadt ,  John Lamb

IPC分类号： G06G7/58 ,  G01N33/48 ,  G01N33/50 ,  G01N31/00

CPC分类号： C12Q1/6883 ,  C12Q1/6827 ,  C12Q2600/158 ,  G06F19/12 ,  G06F19/18 ,  G06F19/20 ,  G06F19/24

摘要： A method for confirming the association of a query QTL or a query gene in the genome of a second species with a clinical trait T exhibited by the second species. A first QTL or a first gene in a first species that is linked to a trait T′ is found. The trait T′ is indicative of trait T. A region of the genome of the first species that comprises the first QTL or the first gene is mapped to a particular region of the genome of the second species. A query QTL or a query gene in the second species that is potentially associated with the trait T is found. The potential association of the query QTL or the query gene with the clinical trait T is confirmed when the query QTL or the query gene is in the particular region of the genome of the second species.

摘要翻译： 一种用于确认第二物种的基因组中的查询QTL或查询基因与由第二物种展示的临床特征T相关联的方法。 发现与性状T'相关联的第一种物种中的第一个QTL或第一个基因。 性状T'表示性状T.包含第一个QTL或第一个基因的第一个物种的基因组区域被映射到第二个物种的基因组的特定区域。 发现可能与性状T相关的第二种物种中的查询QTL或查询基因。 当查询QTL或查询基因位于第二物种的基因组的特定区域时，确认查询QTL或查询基因与临床特征T的潜在关联。

公开(公告)号：US07653491B2

公开(公告)日：2010-01-26

申请号：US10515804

申请日：2003-05-20

申请人： Eric E. Schadt ,  Stephanie A. Monks

发明人： Eric E. Schadt ,  Stephanie A. Monks

IPC分类号： G06F19/00 ,  G11C17/00 ,  G06F15/00

CPC分类号： G06F19/18 ,  G06F19/20 ,  G06F19/24

摘要： A method for identifying a quantitative trait loci for a complex trait that is exhibited by a plurality of organisms in a population. The population is divided into a plurality of sub-populations using a classification scheme. Depending on what is known about the population, either a supervised or unsupervised classification is used. The classification scheme is derived from a plurality of cellular constituent measurements obtained from each organism in the population. For each sub-population in the plurality of sub-populations, a quantitative genetic analysis is performed on the sub-population in order to identify one or more quantitative trait loci for the complex trait.

摘要翻译： 用于鉴定由群体中的多种生物体展现的复杂性状的数量性状位点的方法。 使用分类方案将群体分成多个子群体。 根据已知的人口，使用监督或无监督分类。 分类方案来源于从群体中的每个生物获得的多个细胞成分测量。 对于多个子群体中的每个子群体，对子群体进行定量遗传分析，以便鉴定复杂性状的一个或多个数量性状位点。

公开(公告)号：US20080294403A1

公开(公告)日：2008-11-27

申请号：US11587900

申请日：2005-05-02

申请人： Jun Zhu ,  Eric E. Schadt

发明人： Jun Zhu ,  Eric E. Schadt

IPC分类号： G06G7/58

CPC分类号： G06F19/12 ,  G06F19/24

摘要： The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human disease, but living systems more generally. The present invention provides novel gene network reconstruction algorithms that utilize naturally occurring genetic variations as a source of perturbations to elucidate the networks. The algorithms incorporate relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of these novel algorithms can be demonstrated via application to gene expression data from a segregating mouse population. The network derived from such data using the novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

摘要翻译： 哺乳动物系统中遗传网络的重建是生物研究的主要目标之一，特别是因为这些重建不仅涉及常见的多基因人类疾病，而且更广泛地阐明生物系统。 本发明提供了利用天然存在的遗传变异作为扰动源来阐明网络的新型基因网络重建算法。 该算法通过采用在贝叶斯网络重建问题中常用的最大似然度的广义评分函数，将相对转录丰度和来自分离群体的基因型数据结合起来。 这些新算法的实用性可以通过应用于分离的小鼠群体的基因表达数据来证明。 与使用相同数据导出的更经典的重建网络相比，使用新颖的网络重构算法从这种数据得到的网络能够捕获导致增加的预测能力的基因之间的因果关联。