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Short tandem repeats (STRs), or microsatellites, are genetic variants with repetitive 2­6 base pair motifs in many mammalian genomes. Using high-throughput sequencing and experimental validations, we systematically profiled STRs in five Holsteins. We identified a total of 60,106 microsatellites and generated the first high-resolution STR map, representing a substantial pool of polymorphism in dairy cattle. We observed significant STRs overlap with functional genes and quantitative trait loci (QTL). We performed evolutionary and population genetic analyses using over 20,000 common dinucleotide STRs. Besides corroborating the well-established positive correlation between allele size and variance in allele size, these analyses also identified dozens of outlier STRs based on two anomalous relationships that counter expected characteristics of neutral evolution. And one STR locus overlaps with a significant region of a summary statistic designed to detect STR-related selection. Additionally, our results showed that only 57.1% of STRs located within SNP-based linkage disequilibrium (LD) blocks whereas the other 42.9% were out of blocks. Therefore, a substantial number of STRs are not tagged by SNPs in the cattle genome, likely due to STR's distinct mutation mechanism and elevated polymorphism. This study provides the foundation for future STR-based studies of cattle genome evolution and selection.

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Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example F(ST), cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach.

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The genome-wide scan for selection is an important method for identifying loci involved in adaptive evolution. However, theory that underlies standard scans for selection assumes a simple mutation model. In particular, recurrent mutation of the selective target is not considered. Although this assumption is reasonable for single-nucleotide variants (SNVs), a microsatellite targeted by selection will reliably violate this assumption due to high mutation rate. Moreover, the mutation rate of microsatellites is generally high enough to ensure that recurrent mutation is pervasive rather than occasional. It is therefore unclear if positive selection targeting microsatellites can be detected using standard scanning statistics. Examples of functional variation at microsatellites underscore the significance of understanding the genomic effects of microsatellite selection. Here, we investigate the joint effects of selection and complex mutation on linked sequence diversity, comparing simulations of microsatellite selection and SNV-based selective sweeps. We find that selection on microsatellites is generally difficult to detect using popular summaries of the site frequency spectrum, and, under certain conditions, using popular methods such as the integrated haplotype statistic and SweepFinder. However, comparisons of the number of haplotypes (K) and segregating sites (S) often provide considerable power to detect selection on microsatellites. We apply this knowledge to a scan of autosomes in the human CEU population (CEPH population sampled from Utah). In addition to the most commonly reported targets of selection in European populations, we identify numerous novel genomic regions that bear highly anomalous haplotype configurations. Using one of these regions-intron 1 of MAGI2-as an example, we show that the anomalous configuration is coincident with a perfect CA repeat of length 22. We conclude that standard genome-wide scans will commonly fail to detect mutationally complex targets of selection but that comparisons of K and S will, in many cases, facilitate their identification.

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Long dinucleotide repeats found in exons present a substantial mutational hazard: mutations at these loci occur often and generate frameshifts. Here, we provide clear and compelling evidence that exonic dinucleotides experience strong selective constraint. In humans, only 18 exonic dinucleotides have repeat lengths greater than six, which contrasts sharply with the genome-wide distribution of dinucleotides. We genotyped each of these dinucleotides in 200 humans from eight 1000 Genomes Project populations and found a near-absence of polymorphism. More remarkably, divergence data demonstrate that repeat lengths have been conserved across the primate phylogeny in spite of what is likely considerable mutational pressure. Coalescent simulations show that even a very low mutation rate at these loci fails to explain the anomalous patterns of polymorphism and divergence. Our data support two related selective constraints on the evolution of exonic dinucleotides: a short-term intolerance for any change to repeat length and a long-term prevention of increases to repeat length. In general, our results implicate purifying selection as the force that eliminates new, deleterious mutants at exonic dinucleotides. We briefly discuss the evolution of the longest exonic dinucleotide in the human genome--a 10 x CA repeat in fibroblast growth factor receptor-like 1 (FGFRL1)--that should possess a considerably greater mutation rate than any other exonic dinucleotide and therefore generate a large number of deleterious variants.

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Island populations provide natural laboratories for studying key contributors to evolutionary change, including natural selection, population size and the colonization of new environments. The demographic histories of island populations can be reconstructed from patterns of genetic diversity. House mice (Mus musculus) inhabit islands throughout the globe, making them an attractive system for studying island colonization from a genetic perspective. Gough Island, in the central South Atlantic Ocean, is one of the remotest islands in the world. House mice were introduced to Gough Island by sealers during the 19th century and display unusual phenotypes, including exceptionally large body size and carnivorous feeding behaviour. We describe genetic variation in Gough Island mice using mitochondrial sequences, nuclear sequences and microsatellites. Phylogenetic analysis of mitochondrial sequences suggested that Gough Island mice belong to Mus musculus domesticus, with the maternal lineage possibly originating in England or France. Cluster analyses of microsatellites revealed genetic membership for Gough Island mice in multiple coastal populations in Western Europe, suggesting admixed ancestry. Gough Island mice showed substantial reductions in mitochondrial and nuclear sequence variation and weak reductions in microsatellite diversity compared with Western European populations, consistent with a population bottleneck. Approximate Bayesian computation (ABC) estimated that mice recently colonized Gough Island (~100 years ago) and experienced a 98% reduction in population size followed by a rapid expansion. Our results indicate that the unusual phenotypes of Gough Island mice evolved rapidly, positioning these mice as useful models for understanding rapid phenotypic evolution.

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The ability to survey polymorphism on a genomic scale has enabled genome-wide scans for the targets of natural selection. Theory that connects patterns of genetic variation to evidence of natural selection most often assumes a diallelic locus and no recurrent mutation. Although these assumptions are suitable to selection that targets single nucleotide variants, fundamentally different types of mutation generate abundant polymorphism in genomes. Moreover, recent empirical results suggest that mutationally complex, multiallelic loci including microsatellites and copy number variants are sometimes targeted by natural selection. Given their abundance, the lack of inference methods tailored to the mutational peculiarities of these types of loci represents a notable gap in our ability to interrogate genomes for signatures of natural selection. Previous theoretical investigations of mutation-selection balance at multiallelic loci include assumptions that limit their application to inference from empirical data. Focusing on microsatellites, we assess the dynamics and population-level consequences of selection targeting mutationally complex variants. We develop general models of a multiallelic fitness surface, a realistic model of microsatellite mutation, and an efficient simulation algorithm. Using these tools, we explore mutation-selection-drift equilibrium at microsatellites and investigate the mutational history and selective regime of the microsatellite that causes Friedreich's ataxia. We characterize microsatellite selective events by their duration and cost, note similarities to sweeps from standing point variation, and conclude that it is premature to label microsatellites as ubiquitous agents of efficient adaptive change. Together, our models and simulation algorithm provide a powerful framework for statistical inference, which can be used to test the neutrality of microsatellites and other multiallelic variants.

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We use genotype data from the Marshfield Clinical Research Foundation Personalized Medicine Research Project to investigate genetic similarity and divergence between Europeans and the sampled population of European Americans in Central Wisconsin, USA. To infer recent genetic ancestry of the sampled Wisconsinites, we train support vector machines (SVMs) on the positions of Europeans along top principal components (PCs). Our SVM models partition continent-wide European genetic variance into eight regional classes, which is an improvement over the geographically broader categories of recent ancestry reported by personal genomics companies. After correcting for misclassification error associated with the SVMs (<10%, in all cases), we observe a >14% discrepancy between insular ancestries reported by Wisconsinites and those inferred by SVM. Values of FST as well as Mantel tests for correlation between genetic and European geographic distances indicate minimal divergence between Europe and the local Wisconsin population. However, we find that individuals from the Wisconsin sample show greater dispersion along higher-order PCs than individuals from Europe. Hypothesizing that this pattern is characteristic of nascent divergence, we run computer simulations that mimic the recent peopling of Wisconsin. Simulations corroborate the pattern in higher-order PCs, demonstrate its transient nature, and show that admixture accelerates the rate of divergence between the admixed population and its parental sources relative to drift alone. Together, empirical and simulation results suggest that genetic divergence between European source populations and European Americans in Central Wisconsin is subtle but already under way.

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Rapid advances in DNA sequencing and genotyping technologies are beginning to reveal the scope and pattern of human genomic variation. Although single nucleotide polymorphisms (SNPs) have been intensively studied, the extent and form of variation at other types of molecular variants remain poorly understood. Polymorphism at the most variable loci in the human genome, microsatellites, has rarely been examined on a genomic scale without the ascertainment biases that attend typical genotyping studies. We conducted a genomic survey of variation at microsatellites with at least three perfect repeats by comparing two complete genome sequences, the Human Genome Reference sequence and the sequence of J. Craig Venter. The genomic proportion of polymorphic loci was 2.7%, much higher than the rate of SNP variation, with marked heterogeneity among classes of loci. The proportion of variable loci increased substantially with repeat number. Repeat lengths differed in levels of variation, with longer repeat lengths generally showing higher polymorphism at the same repeat number. Microsatellite variation was weakly correlated with regional SNP number, indicating modest effects of shared genealogical history. Reductions in variation were detected at microsatellites located in introns, in untranslated regions, in coding exons, and just upstream of transcription start sites, suggesting the presence of selective constraints. Our results provide new insights into microsatellite mutational processes and yield a preview of patterns of variation that will be obtained in genomic surveys of larger numbers of individuals.

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Theoretical work focused on microsatellite variation has produced a number of important results, including the expected distribution of repeat sizes and the expected squared difference in repeat size between two randomly selected samples. However, closed-form expressions for the sampling distribution and frequency spectrum of microsatellite variation have not been identified. Here, we use coalescent simulations of the stepwise mutation model to develop gamma and exponential approximations of the microsatellite allele frequency spectrum, a distribution central to the description of microsatellite variation across the genome. For both approximations, the parameter of biological relevance is the number of alleles at a locus, which we express as a function of θ, the population-scaled mutation rate, based on simulated data. Discovered relationships between θ, the number of alleles, and the frequency spectrum support the development of three new estimators of microsatellite θ. The three estimators exhibit roughly similar mean squared errors (MSEs) and all are biased. However, across a broad range of sample sizes and θ values, the MSEs of these estimators are frequently lower than all other estimators tested. The new estimators are also reasonably robust to mutation that includes step sizes greater than one. Finally, our approximation to the microsatellite allele frequency spectrum provides a null distribution of microsatellite variation. In this context, a preliminary analysis of the effects of demographic change on the frequency spectrum is performed. We suggest that simulations of the microsatellite frequency spectrum under evolutionary scenarios of interest may guide investigators to the use of relevant and sometimes novel summary statistics.

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Mutational changes coupled with endocrine, paracrine, and/or autocrine signals regulate cell division during carcinogenesis. The hormone signals remain undefined, although the absolute requirement in vitro for fetal serum indicates the necessity for a fetal serum factor(s) in cell proliferation. Using prostatic cancer cell (PCC) lines as a model of cancer cell proliferation, we have identified the fetal serum component activin A and its signaling through the activin receptor type II (ActRII), as necessary, although not sufficient, for PCC proliferation. Activin A induced Smad2 phosphorylation and PCC proliferation, but only in the presence of fetal bovine serum (FBS). Conversely, activin A antibodies and inhibin A suppressed FBS-induced PCC proliferation confirming activin A as one of multiple serum components required for PCC proliferation. Basic fibroblast growth factor was subsequently shown to synergize activin A-induced PCC proliferation. Inhibition of ActRII signaling using a blocking antibody or antisense-P decreased mature ActRII expression, Smad2 phosphorylation, and the apparent viability of PCCs and neuroblastoma cells grown in FBS. Suppression of ActRII signaling in PCC and neuroblastoma cells did not induce apoptosis as indicated by the ratio of active/inactive caspase 3 but did correlate with increased cell detachment and ADAM-15 expression, a disintegrin whose expression is strongly correlated with prostatic metastasis. These findings indicate that ActRII signaling is required for PCC and neuroblastoma cell viability, with ActRII mediating cell fate via the regulation of cell adhesion. That ActRII signaling governs both cell viability and cell adhesion has important implications for developing therapeutic strategies to regulate cancer growth and metastasis.

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The basic mechanism(s) by which altered Cu homeostasis is toxic to hepatocytes and neurons, the two major cell types affected in copper storage diseases such as Wilson's disease (WD), remain unclear. Using human M17 neuroblastoma cells as a model to examine Cu toxicity, we found that there was a time- and concentration-dependent induction of neuronal death, such that at 24 h there was a approximately 50 % reduction in viability with 25 muM Cu-glycine(2). Cu-glycine(2) (25:50 muM) treatment for 24 h significantly altered the expression of 296 genes, including 8 genes involved with apoptosis (BCL2-associated athanogene 3, BCL2/adenovirus E1B 19kDa interacting protein caspase 5, regulator of Fas-induced apoptosis, V-jun sarcoma virus 17 oncogene homolog, claudin 5, prostaglandin E receptor 3 and protein tyrosine phosphatase, non-receptor type 6). Surprisingly, changes in the expression of more 'traditional' apoptotic genes (Bcl-2, Bax, Bak and Bad) did not vary more than 20 %. To test whether the induction of apoptosis in neuroblastoma cells was via post-translational mechanisms, we measured the protein expression of these apoptotic markers in M17 neuroblastoma cells treated with Cu-glycine(2) (0-100 muM) for 24-48 h. Compared with glycine treated cells, Cu-glycine(2) reduced Bcl-2 expression by 50 %, but increased Bax and Bak expression by 130% and 400 %, respectively. To assess whether Cu also induced apoptotic cell death in a mouse model of WD, we measured the expression of these apoptotic markers in the liver and brain of mice expressing an ATP7b gene mutation (tx(J) mice) at 10 months of age (near the end of their lives when overt liver pathology is displayed). Changes in the liver expression of these apoptotic markers in tx(J) mice compared to background mice mirrored those of Cu treated neuroblastoma cells. In contrast, few changes in apoptotic protein expression were detected in the brain between tx(J) and background mice, indicating the tx(J) mouse is a good model of hepatic, but not brain, Cu toxicity. Our results indicate that Cu-induction of neuronal apoptosis does not require de novo synthesis or degradation of apoptotic genes, and that Cu accumulation in the aged tx(J) mouse brain is insufficient to induce apoptosis.

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Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.

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Genetic and biochemical studies support the apolipoprotein E (APOE) epsilon4 allele as a major risk factor for late-onset Alzheimer's disease (AD), though ~50% of AD patients do not carry the allele. APOE transports cholesterol for luteinizing hormone (LH)-regulated steroidogenesis, and both LH and neurosteroids have been implicated in the etiology of AD. Since polymorphisms of LH beta-subunit (LHB) and its receptor (LHCGR) have not been tested for their association with AD, we scored AD and age-matched control samples for APOE genotype and 14 polymorphisms of LHB and LHCGR. Thirteen gene-gene interactions between the loci of LHB, LHCGR, and APOE were associated with AD. The most strongly supported of these interactions was between an LHCGR intronic polymorphism (rs4073366; lhcgr2) and APOE in males, which was detected using all three interaction analyses: linkage disequilibrium, multi-dimensionality reduction, and logistic regression. While the APOE epsilon4 allele carried significant risk of AD in males [p = 0.007, odds ratio (OR) = 3.08(95%confidence interval: 1.37, 6.91)], epsilon4-positive males carrying 1 or 2 C-alleles at lhcgr2 exhibited significantly decreased risk of AD [OR = 0.06(0.01, 0.38); p = 0.003]. This suggests that the lhcgr2 C-allele or a closely linked locus greatly reduces the risk of AD in males carrying an APOE epsilon4 allele. The reversal of risk embodied in this interaction powerfully supports the importance of considering the role gene-gene interactions play in the etiology of complex biological diseases and demonstrates the importance of using multiple analytic methods to detect well-supported gene-gene interactions.

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BACKGROUND: The Caenorhabditis elegans genome is known to code for at least 1149 G protein-coupled receptors (GPCRs), but the GPCR(s) critical to the regulation of reproduction in this nematode are not yet known. This study examined whether GPCRs orthologous to human gonadotropin-releasing hormone receptor (GnRHR) exist in C. elegans. RESULTS: Our sequence analyses indicated the presence of two proteins in C. elegans, one of 401 amino acids [GenBank: NP_491453; WormBase: F54D7.3] and another of 379 amino acids [GenBank: NP_506566; WormBase: C15H11.2] with 46.9% and 44.7% nucleotide similarity to human GnRHR1 and GnRHR2, respectively. Like human GnRHR1, structural analysis of the C. elegans GnRHR1 orthologue (Ce-GnRHR) predicted a rhodopsin family member with 7 transmembrane domains, G protein coupling sites and phosphorylation sites for protein kinase C. Of the functionally important amino acids in human GnRHR1, 56% were conserved in the C. elegans orthologue. Ce-GnRHR was actively transcribed in adult worms and immunoanalyses using antibodies generated against both human and C. elegans GnRHR indicated the presence of a 46-kDa protein, the calculated molecular mass of the immature Ce-GnRHR. Ce-GnRHR staining was specifically localized to the germline, intestine and pharynx. In the germline and intestine, Ce-GnRHR was localized specifically to nuclei as revealed by colocalization with a DNA nuclear stain. However in the pharynx, Ce-GnRHR was localized to the myofilament lattice of the pharyngeal musculature, suggesting a functional role for Ce-GnRHR signaling in the coupling of food intake with reproduction. Phylogenetic analyses support an early evolutionary origin of GnRH-like receptors, as evidenced by the hypothesized grouping of Ce-GnRHR, vertebrate GnRHRs, a molluscan GnRHR, and the adipokinetic hormone receptors (AKHRs) and corazonin receptors of arthropods. CONCLUSION: This is the first report of a GnRHR orthologue in C. elegans, which shares significant similarity with insect AKHRs. In vertebrates, GnRHRs are central components of the reproductive endocrine system, and the identification of a GnRHR orthologue in C. elegans suggests the potential use of C. elegans as a model system to study reproductive endocrinology.

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Gonadotropin-releasing hormone receptor I (GnRHR I) has been localized to the limbic system of the rat brain, although the functional consequences of GnRH signaling through these receptors is unknown. In this paper, we characterize the expression of GnRHR I in the human hippocampus and cortex, and the functionality of GnRHR I in human neuroblastoma cells. Robust GnRHR I immunoreactivity was detected in the cell body as well as along the apical dendrites of pyramidal neurons in the CA2, CA1, and end plate, but was clearly lower in the subiculum of the hippocampus. Immunolabeling was also evident in cortical neurons, including those located in the entorhinal cortex and occipitotemporal gyrus but was not observed within the granular layer of the dentate gyrus. No differences in immunohistochemical staining were observed between control and Alzheimer's disease brain. GnRHR I mRNA and protein (mature, immature, and other variant) expression was detected in human neuroblastoma cells (M17, SH-SY5Y) and rat embryonic primary neurons and varied with differentiation and GnRH treatment. Since GnRHR I was expressed by extrapituitary cells, and hypothalamic GnRH I secretion markedly increases post-menopause/andropause, we treated human M17 neuroblastoma cells cultured in serum-free conditions with GnRH I for 6 h and measured LH expression. M17 neuroblastoma cells express LHbeta mRNA, while immunoblot analysis indicated the presence of three LH variants (approximately 30, 47, and 60 kDa) that were upregulated by low concentrations of GnRH I, but down-regulated at higher GnRH I concentrations. LH expression was also found to increase in differentiating embryonic rat primary cortical neurons. Our results demonstrate that neurons expressing GnRHR I are functional, responding to GnRH I by upregulating LH production. Post-reproductive surges in GnRH I secretion may explain the accumulation of LH in pyramidal neurons of the aged human and rat.

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BACKGROUND: The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge. RESULTS: A total of 3108 sequence signatures were found, each of which was shared by a set of guest proteins interacting with one of 944 host proteins in Saccharomyces cerevisiae genome. Approximately 94% of these sequence signatures matched entries in InterPro member databases. We identified 84 distinct sequence signatures from the remaining 172 unknown signatures. The signature sharing information was then applied in predicting sub-cellular localization of yeast proteins and the novel signatures were used in identifying possible interacting sites. CONCLUSION: We reported a method of PPI data mining that facilitated the discovery of novel sequence signatures using a large PPI dataset from S. cerevisiae genome as input. The fact that 94% of discovered signatures were known validated the ability of the approach to identify large numbers of signatures from PPI data. The significance of these discovered signatures was demonstrated by their application in predicting sub-cellular localizations and identifying potential interaction binding sites of yeast proteins.

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