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  • 1
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 25, No. 7 ( 2015-07), p. 927-936
    Abstract: Genomic imprinting is an important regulatory mechanism that silences one of the parental copies of a gene. To systematically characterize this phenomenon, we analyze tissue specificity of imprinting from allelic expression data in 1582 primary tissue samples from 178 individuals from the Genotype-Tissue Expression (GTEx) project. We characterize imprinting in 42 genes, including both novel and previously identified genes. Tissue specificity of imprinting is widespread, and gender-specific effects are revealed in a small number of genes in muscle with stronger imprinting in males. IGF2 shows maternal expression in the brain instead of the canonical paternal expression elsewhere. Imprinting appears to have only a subtle impact on tissue-specific expression levels, with genes lacking a systematic expression difference between tissues with imprinted and biallelic expression. In summary, our systematic characterization of imprinting in adult tissues highlights variation in imprinting between genes, individuals, and tissues.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2015
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Cold Spring Harbor Laboratory ; 2016
    In:  Genome Research Vol. 26, No. 7 ( 2016-07), p. 863-873
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 26, No. 7 ( 2016-07), p. 863-873
    Abstract: The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2016
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    Cold Spring Harbor Laboratory ; 2005
    In:  Genome Research Vol. 15, No. 11 ( 2005-11), p. 1594-1600
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 15, No. 11 ( 2005-11), p. 1594-1600
    Abstract: In the attempt to understand human variation and the genetic basis of complex disease, a tremendous number of single nucleotide polymorphisms (SNPs) have been discovered and deposited into NCBI's dbSNP public database. More than 2.7 million SNPs in the database have genotype information. This data provides an invaluable resource for understanding the structure of human variation and the design of genetic association studies. The genotypes deposited to dbSNP are unphased, and thus, the haplotype information is unknown. We applied the phasing method HAP to obtain the haplotype information, block partitions, and tag SNPs for all publicly available genotype data and deposited this information into the dbSNP database. We also deposited the orthologous chimpanzee reference sequence for each predicted haplotype block computed using the UCSC BLASTZ alignments of human and chimpanzee. Using dbSNP, researchers can now easily perform analyses using multiple genotype data sets from the same genomic regions. Dense and sparse genotype data sets from the same region were combined to show that the number of common haplotypes is significantly underestimated in whole genome data sets, while the predicted haplotypes over the common SNPs are consistent between studies. To validate the accuracy of the predictions, we benchmarked HAP's running time and phasing accuracy against PHASE. Although HAP is slightly less accurate than PHASE, HAP is over 1000 times faster than PHASE, making it suitable for application to the entire set of genotypes in dbSNP.
    Type of Medium: Online Resource
    ISSN: 1088-9051
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2005
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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