Ryan J. Haasl

Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites

Although growing numbers of single nucleotide polymorphisms (SNPs) and microsatellites (short tandem repeat polymorphisms or STRPs) are used to infer population structure, their relative properties in this context remain poorly understood. SNPs and STRPs mutate differently, suggesting multi-locus genotypes at these loci might differ in ability to detect population structure. Here, we use coalescent simulations to measure the power of sets of SNPs and STRPs to identify population structure. To maximize the applicability of our results to empirical studies, we focus on the popular STRUCTURE analysis and evaluate the role of several biological and practical factors in the detection of population structure. We find that: (1) fewer unlinked STRPs than SNPs are needed to detect structure at recent divergence times <0.3 Ne generations; (2) accurate estimation of the number of populations requires many fewer STRPs than SNPs; (3) for both marker types, declines in power due to modest gene flow (Nem=1.0) are largely negated by increasing marker number; (4) variation in the STRP mutational model affects power modestly; (5) SNP haplotypes (θ=1, no recombination) provide power comparable with STRP loci (θ=10); (6) ascertainment schemes that select highly variable STRP or SNP loci increase power to detect structure, though ascertained data may not be suitable to other inference; and (7) when samples are drawn from an admixed population and one of its parent populations, the reduction in power to detect two populations is greater for STRPs than SNPs. These results should assist the design of multi-locus studies to detect population structure in nature.

A Genomic Portrait of Human Microsatellite Variation

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.

Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication

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 FST, 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.

Identification of a gonadotropin-releasing hormone receptor orthologue in Caenorhabditis elegans

BackgroundThe 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.ResultsOur 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.ConclusionThis 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.

Demographic history of a recent invasion of house mice on the isolated Island of Gough

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.

Microsatellites as Targets of Natural Selection

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 Friedreichs 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.

Discover protein sequence signatures from protein-protein interaction data

BackgroundThe 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.ResultsA 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.ConclusionWe 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.

The number of alleles at a microsatellite defines the allele frequency spectrum and facilitates fast, accurate estimation of θ

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.

A luteinizing hormone receptor intronic variant is significantly associated with decreased risk of Alzheimer's disease in males carrying an apolipoprotein E ε4 allele

Genetic and biochemical studies support the apolipoprotein E (APOE) ε4 allele as a major risk factor for late-onset Alzheimers 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 ε4 allele carried significant risk of AD in males [p = 0.007, odds ratio (OR) = 3.08(95%confidence interval: 1.37, 6.91)], ε4-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 ε4 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.

Large-scale Protein-Protein Interaction prediction using novel kernel methods

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.