Recombination rates of genes expressed in human tissues.

High-resolution recombination rates have recently been revealed in the human genome, and considerable variation in patterns of recombination rates has been found along the chromosomes. Although the associations between this variation and genomic sequence features, such as genic regions, provide information on haplotype diversity and natural selection in these regions, the associations are not well understood. Here, we performed microarray experiments to identify genes specifically expressed in human tissues and investigated tendencies of recombination rates within tissue-specific genes. We found that some types of tissue-specific genes (in the frontal lobe, fetal brain, testis, thymus and thyroid) tended to have extremely low recombination rates, whereas other types (in the brain cerebellum, brain whole, stomach, lung and bone marrow) tended to have relatively high recombination rates. Surprisingly, genes specifically expressed in the frontal lobe, which is a brain region involved in human cognitive abilities, had low recombination rates, whereas genes specifically expressed in the cerebellum, which is a brain region with primitive functions shared by all vertebrate species, had high rates. These findings suggest that natural selection forms the recombination rate tendencies according to the physiological functions exerted in the tissues. For example, the low recombination rates in frontal lobe-specific genes may indicate that a few haplotypes have been rapidly widespread across the population because higher cognitive abilities are advantageous. Frontal lobe-specific genes with extremely low recombination rates may be candidates for genes related to cognitive abilities that human species have recently obtained.

[1]  Yusuke Nakamura,et al.  Linkage disequilibrium of evolutionarily conserved regions in the human genome , 2006, BMC Genomics.

[2]  Pardis C Sabeti,et al.  Positive Natural Selection in the Human Lineage , 2006, Science.

[3]  J. Pritchard,et al.  A Map of Recent Positive Selection in the Human Genome , 2006, PLoS biology.

[4]  Gonçalo R Abecasis,et al.  Sequence features in regions of weak and strong linkage disequilibrium. , 2005, Genome research.

[5]  Ryan D. Hernandez,et al.  Natural selection on protein-coding genes in the human genome , 2005, Nature.

[6]  P. Donnelly,et al.  A Fine-Scale Map of Recombination Rates and Hotspots Across the Human Genome , 2005, Science.

[7]  P. Copeland.  Making sense of nonsense: the evolution of selenocysteine usage in proteins , 2005, Genome Biology.

[8]  Timothy B Sackton,et al.  A Scan for Positively Selected Genes in the Genomes of Humans and Chimpanzees , 2005, PLoS biology.

[9]  Thomas E. Nichols,et al.  Linkage Disequilibrium: Ancient History Drives the New Genetics , 2005, Human Heredity.

[10]  Geoffrey B. Nilsen,et al.  Whole-Genome Patterns of Common DNA Variation in Three Human Populations , 2005, Science.

[11]  D. Botstein,et al.  A DNA microarray survey of gene expression in normal human tissues , 2005, Genome Biology.

[12]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[13]  Yusuke Nakamura,et al.  Variation of gene-based SNPs and linkage disequilibrium patterns in the human genome. , 2004, Human molecular genetics.

[14]  Dana C Crawford,et al.  Evidence for substantial fine-scale variation in recombination rates across the human genome , 2004, Nature Genetics.

[15]  S. Keeney,et al.  Where the crossovers are: recombination distributions in mammals , 2004, Nature Reviews Genetics.

[16]  P. Donnelly,et al.  The Fine-Scale Structure of Recombination Rate Variation in the Human Genome , 2004, Science.

[17]  S. Batalov,et al.  A gene atlas of the mouse and human protein-encoding transcriptomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  A. Su,et al.  Applications of a rat multiple tissue gene expression data set. , 2004, Genome research.

[19]  Wen-Hsiung Li,et al.  Mammalian housekeeping genes evolve more slowly than tissue-specific genes. , 2004, Molecular biology and evolution.

[20]  Toshihiro Tanaka.  The International HapMap Project , 2003, Nature.

[21]  G. McVean,et al.  Estimating recombination rates from population-genetic data , 2003, Nature Reviews Genetics.

[22]  Sarah A Tishkoff,et al.  Patterns of human genetic diversity: implications for human evolutionary history and disease. , 2003, Annual review of genomics and human genetics.

[23]  M. Olivier.  A haplotype map of the human genome , 2003, Nature.

[24]  M. Olivier.  A haplotype map of the human genome. , 2003, Nature.

[25]  John Quackenbush.  Microarray data normalization and transformation , 2002, Nature Genetics.

[26]  Pardis C Sabeti,et al.  Detecting recent positive selection in the human genome from haplotype structure , 2002, Nature.

[27]  S. Knudsen,et al.  A new non-linear normalization method for reducing variability in DNA microarray experiments , 2002, Genome Biology.

[28]  Lon R. Cardon,et al.  A first-generation linkage disequilibrium map of human chromosome 22 , 2002, Nature.

[29]  D. Gudbjartsson,et al.  A high-resolution recombination map of the human genome , 2002, Nature Genetics.

[30]  L. Kruglyak,et al.  Patterns of linkage disequilibrium in the human genome , 2002, Nature Reviews Genetics.

[31]  Morris Freedman,et al.  Frontal lobe functions , 2001, Current neurology and neuroscience reports.

[32]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[33]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[34]  G. Churchill,et al.  Statistical design and the analysis of gene expression microarray data. , 2007, Genetical research.

[35]  Yusuke Nakamura,et al.  Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray. , 2002, DNA research : an international journal for rapid publication of reports on genes and genomes.

[36]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .