Bayesian method to predict individual SNP genotypes from gene expression data

RNA profiling can be used to capture the expression patterns of many genes that are associated with expression quantitative trait loci (eQTLs). Employing published putative cis eQTLs, we developed a Bayesian approach to predict SNP genotypes that is based only on RNA expression data. We show that predicted genotypes can accurately and uniquely identify individuals in large populations. When inferring genotypes from an expression data set using eQTLs of the same tissue type (but from an independent cohort), we were able to resolve 99% of the identities of individuals in the cohort at P(adjusted) ≤ 1 × 10(-5). When eQTLs derived from one tissue were used to predict genotypes using expression data from a different tissue, the identities of 90% of the study subjects could be resolved at P(adjusted) ≤ 1 × 10(-5). We discuss the implications of deriving genotypic information from RNA data deposited in the public domain.     

Duplicate genes increase gene expression diversity within and between species

Using microarray gene expression data from several Drosophila species and strains, we show that duplicated genes, compared with single-copy genes, significantly increase gene expression diversity during development. We show further that duplicate genes tend to cause expression divergences between Drosophila species (or strains) to evolve faster than do single-copy genes. This conclusion is also supported by data from different yeast strains.     

Merlin--rapid analysis of dense genetic maps using sparse gene flow trees.

Efforts to find disease genes using high-density single-nucleotide polymorphism (SNP) maps will produce data sets that exceed the limitations of current computational tools. Here we describe a new, efficient method for the analysis of dense genetic maps in pedigree data that provides extremely fast solutions to common problems such as allele-sharing analyses and haplotyping. We show that sparse binary trees represent patterns of gene flow in general pedigrees in a parsimonious manner, and derive a family of related algorithms for pedigree traversal. With these trees, exact likelihood calculations can be carried out efficiently for single markers or for multiple linked markers. Using an approximate multipoint calculation that ignores the unlikely possibility of a large number of recombinants further improves speed and provides accurate solutions in dense maps with thousands of markers. Our multipoint engine for rapid likelihood inference (Merlin) is a computer program that uses sparse inheritance trees for pedigree analysis; it performs rapid haplotyping, genotype error detection and affected pair linkage analyses and can handle more markers than other pedigree analysis packages.     

Evidence for substantial fine-scale variation in recombination rates across the human genome

Characterizing fine-scale variation in human recombination rates is important, both to deepen understanding of the recombination process and to aid the design of disease association studies. Current genetic maps show that rates vary on a megabase scale, but studying finer-scale variation using pedigrees is difficult. Sperm-typing experiments have characterized regions where crossovers cluster into 1-2-kb hot spots, but technical difficulties limit the number of studies. An alternative is to use population variation to infer fine-scale characteristics of the recombination process. Several surveys reported 'block-like' patterns of diversity, which may reflect fine-scale recombination rate variation, but limitations of available methods made this impossible to assess. Here, we applied a new statistical method, which overcomes these limitations, to infer patterns of fine-scale recombination rate variation in 74 genes. We found extensive rate variation both within and among genes. In particular, recombination hot spots are a common feature of the human genome: 47% (35 of 74) of genes showed substantive evidence for a hot spot, and many more showed evidence for some rate variation. No primary sequence characteristics are consistently associated with precise hot-spot location, although G+C content and nucleotide diversity are correlated with local recombination rate.     

MicroRNA-responsive 'sensor' transgenes uncover Hox-like and other developmentally regulated patterns of vertebrate microRNA expression

MicroRNAs (miRNAs) are a class of short ( approximately 22-nt) noncoding RNA molecules that downregulate expression of their mRNA targets. Since their discovery as regulators of developmental timing in Caenorhabditis elegans, hundreds of miRNAs have been identified in both animals and plants. Here, we report a technique for visualizing detailed miRNA expression patterns in mouse embryos. We elucidate the tissue-specific expression of several miRNAs during embryogenesis, including two encoded by genes embedded in homeobox (Hox) clusters, miR-10a and miR-196a. These two miRNAs are expressed in patterns that are markedly reminiscent of those of Hox genes. Furthermore, miR-196a negatively regulates Hoxb8, indicating that its restricted expression pattern probably reflects a role in the patterning function of the Hox complex.     

Natural variation in cardiac metabolism and gene expression in Fundulus heteroclitus

Individual variation in gene expression is important for evolutionary adaptation and susceptibility to diseases and pathologies. In this study, we address the functional importance of this variation by comparing cardiac metabolism to patterns of mRNA expression using microarrays. There is extensive variation in both cardiac metabolism and the expression of metabolic genes among individuals of the teleost fish Fundulus heteroclitus from natural outbred populations raised in a common environment: metabolism differed among individuals by a factor of more than 2, and expression levels of 94% of genes were significantly different (P < 0.01) between individuals in a population. This unexpectedly high variation in metabolic gene expression explains much of the variation in metabolism, suggesting that it is biologically relevant. The patterns of gene expression that are most important in explaining cardiac metabolism differ between groups of individuals. Apparently, the variation in metabolism seems to be related to different patterns of gene expression in the different groups of individuals. The magnitude of differences in gene expression in these groups is not important; large changes in expression have no greater predictive value than small changes. These data suggest that variation in physiological performance is related to the subtle variation in gene expression and that this relationship differs among individuals.     

MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia.

Acute lymphoblastic leukemias carrying a chromosomal translocation involving the mixed-lineage leukemia gene (MLL, ALL1, HRX) have a particularly poor prognosis. Here we show that they have a characteristic, highly distinct gene expression profile that is consistent with an early hematopoietic progenitor expressing select multilineage markers and individual HOX genes. Clustering algorithms reveal that lymphoblastic leukemias with MLL translocations can clearly be separated from conventional acute lymphoblastic and acute myelogenous leukemias. We propose that they constitute a distinct disease, denoted here as MLL, and show that the differences in gene expression are robust enough to classify leukemias correctly as MLL, acute lymphoblastic leukemia or acute myelogenous leukemia. Establishing that MLL is a unique entity is critical, as it mandates the examination of selectively expressed genes for urgently needed molecular targets.     

Regulatory evolution across the protein interaction network

Protein-protein interactions may impose constraints on both structural and regulatory evolution. Here we show that protein-protein interactions are negatively associated with evolutionary variation in gene expression. Moreover, interacting proteins have similar levels of variation in expression, and their expression levels are positively correlated across strains. Our results suggest that interacting proteins undergo similar evolutionary dynamics, and that their expression levels are evolutionarily coupled. These patterns hold for organisms as diverse as budding yeast and fruit flies.     

Evidence for co-evolution of gene order and recombination rate

There is increasing evidence in eukaryotic genomes that gene order is not random, even allowing for tandem duplication. Notably, in numerous genomes, genes of similar expression tend to be clustered. Are there other reasons for clustering of functionally similar genes? If genes are linked to enable genetic, rather than physical clustering, then we also expect that clusters of certain genes might be associated with blocks of reduced recombination rates. Here we show that, in yeast, essential genes are highly clustered and this clustering is independent of clustering of co-expressed genes and of tandem duplications. Adjacent pairs of essential genes are preferentially conserved through evolution. Notably, we also find that clusters of essential genes are in regions of low recombination and that larger clusters have lower recombination rates. These results suggest that selection acts to modify both the fine-scale intragenomic variation in the recombination rate and the distribution of genes and provide evidence for co-evolution of gene order and recombination rate.