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The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types., Competing Interests: The authors have declared that no competing interests exist.

Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect., Competing Interests: The authors have declared that no competing interests exist.

Sequencing studies have been discovering a numerous number of rare variants, allowing the identification of the effects of rare variants on disease susceptibility. As a method to increase the statistical power of studies on rare variants, several groupwise association tests that group rare variants in genes and detect associations between genes and diseases have been proposed. One major challenge in these methods is to determine which variants are causal in a group, and to overcome this challenge, previous methods used prior information that specifies how likely each variant is causal. Another source of information that can be used to determine causal variants is the observed data because case individuals are likely to have more causal variants than control individuals. In this article, we introduce a likelihood ratio test (LRT) that uses both data and prior information to infer which variants are causal and uses this finding to determine whether a group of variants is involved in a disease. We demonstrate through simulations that LRT achieves higher power than previous methods. We also evaluate our method on mutation screening data of the susceptibility gene for ataxia telangiectasia, and show that LRT can detect an association in real data. To increase the computational speed of our method, we show how we can decompose the computation of LRT, and propose an efficient permutation test. With this optimization, we can efficiently compute an LRT statistic and its significance at a genome-wide level. The software for our method is publicly available at http://genetics.cs.ucla.edu/rarevariants .

We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

Motivation: The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearson's correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearson's correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable., Results: In this article, we present a statistical model for calculating gene coexpression called mixed model coexpression (MMC), which models coexpression within a mixed model framework. Confounding effects are expected to be encoded in the matrix representing the correlation between arrays, the inter-sample correlation matrix. By conditioning on the information in the inter-sample correlation matrix, MMC is able to produce gene coexpressions that are not influenced by global confounding effects and thus significantly reduce the number of spurious coexpressions observed. We applied MMC to both human and yeast datasets and show it is better able to effectively prioritize strong coexpressions when compared to a traditional Pearson's correlation and a Pearson's correlation applied to data corrected with surrogate variable analysis (SVA)., Availability: The method is implemented in the R programming language and may be found at http://genetics.cs.ucla.edu/mmc., Contact: nfurlott@cs.ucla.edu; eeskin@cs.ucla.edu.

Motivation: Structural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads., Results: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate.

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications., Competing Interests: The authors have declared that no competing interests exist.

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs., (Copyright © 2011 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

The advent of next generation sequencing technologies allows one to discover nearly all rare variants in a genomic region of interest. This technological development increases the need for an effective statistical method for testing the aggregated effect of rare variants in a gene on disease susceptibility. The idea behind this approach is that if a certain gene is involved in a disease, many rare variants within the gene will disrupt the function of the gene and are associated with the disease. In this article, we present the rare variant weighted aggregate statistic (RWAS), a method that groups rare variants and computes a weighted sum of differences between case and control mutation counts. We show that our method outperforms the groupwise association test of Madsen and Browning in the disease-risk model that assumes that each variant makes an equally small contribution to disease risk. In addition, we can incorporate prior information into our method of which variants are likely causal. By using simulated data and real mutation screening data of the susceptibility gene for ataxia telangiectasia, we demonstrate that prior information has a substantial influence on the statistical power of association studies. Our method is publicly available at http://genetics.cs.ucla.edu/rarevariants.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits., Competing Interests: The authors have declared that no competing interests exist.

The Plasmodium falciparum parasite's ability to adapt to environmental pressures, such as the human immune system and antimalarial drugs, makes malaria an enduring burden to public health. Understanding the genetic basis of these adaptations is critical to intervening successfully against malaria. To that end, we created a high-density genotyping array that assays over 17,000 single nucleotide polymorphisms (∼ 1 SNP/kb), and applied it to 57 culture-adapted parasites from three continents. We characterized genome-wide genetic diversity within and between populations and identified numerous loci with signals of natural selection, suggesting their role in recent adaptation. In addition, we performed a genome-wide association study (GWAS), searching for loci correlated with resistance to thirteen antimalarials; we detected both known and novel resistance loci, including a new halofantrine resistance locus, PF10_0355. Through functional testing we demonstrated that PF10_0355 overexpression decreases sensitivity to halofantrine, mefloquine, and lumefantrine, but not to structurally unrelated antimalarials, and that increased gene copy number mediates resistance. Our GWAS and follow-on functional validation demonstrate the potential of genome-wide studies to elucidate functionally important loci in the malaria parasite genome., Competing Interests: The authors have declared that no competing interests exist.

The potential benefits of using population isolates in genetic mapping, such as reduced genetic, phenotypic and environmental heterogeneity, are offset by the challenges posed by the large amounts of direct and cryptic relatedness in these populations confounding basic assumptions of independence. We have evaluated four representative specialized methods for association testing in the presence of relatedness; (i) within-family (ii) within- and between-family and (iii) mixed-models methods, using simulated traits for 2906 subjects with known genome-wide genotype data from an extremely isolated population, the Island of Kosrae, Federated States of Micronesia. We report that mixed models optimally extract association information from such samples, demonstrating 88% power to rank the true variant as among the top 10 genome-wide with 56% achieving genome-wide significance, a >80% improvement over the other methods, and demonstrate that population isolates have similar power to non-isolate populations for observing variants of known effects. We then used the mixed-model method to reanalyze data for 17 published phenotypes relating to metabolic traits and electrocardiographic measures, along with another 8 previously unreported. We replicate nine genome-wide significant associations with known loci of plasma cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, thyroid stimulating hormone, homocysteine, C-reactive protein and uric acid, with only one detected in the previous analysis of the same traits. Further, we leveraged shared identity-by-descent genetic segments in the region of the uric acid locus to fine-map the signal, refining the known locus by a factor of 4. Finally, we report a novel associations for height (rs17629022, P< 2.1 × 10⁻⁸).

Both iron deficiency and iron excess are detrimental in many organisms, and previous studies in both mice and humans suggest that genetic variation may influence iron status in mammals. However, these genetic factors are not well defined. To address this issue, we measured basal liver iron levels in 18 inbred strains of mice of both sexes on a defined iron diet and found ∼4-fold variation in liver iron in males (lowest 153 μg/g, highest 661 μg/g) and ∼3-fold variation in females (lowest 222 μg/g, highest 658 μg/g). We carried out a genome-wide association mapping to identify haplotypes underlying differences in liver iron and three other related traits (copper and zinc liver levels, and plasma diferric transferrin levels) in a subset of 14 inbred strains for which genotype information was available. We identified two putative quantitative trait loci (QTL) that contain genes with a known role in iron metabolism: Eif2ak1 and Igf2r. We also identified four putative QTL that reside in previously identified iron-related QTL and 22 novel putative QTL. The most promising putative QTL include a 0.22 Mb region on Chromosome 7 and a 0.32 Mb region on Chromosome 11 that both contain only one candidate gene, Adam12 and Gria1, respectively. Identified putative QTL are good candidates for further refinement and subsequent functional studies.

Background: Recent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach; as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants., Results: In this paper we provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming. The approach allows for the addition of external data, particularly imputation data, resulting in a flexible environment that is suitable for different applications., Conclusions: Particularly, we demonstrate that both low and high allele frequency SNPs can be accurately genotyped when the DNA pooling scheme is performed in conjunction with microarray genotyping and imputation. Additionally, we demonstrate the use of our framework for the detection of cancer fusion genes from RNA sequences.

In genetic association studies, quality control (QC) filters are applied to remove potentially problematic markers before the markers are tested for statistical associations. However, spurious associations can still occur after QC. We introduce Post-Association Cleaning (PAC) approach that can complement QC by capturing spurious associations using the information in the post-association results. Specifically, we propose a PAC filter based on the linkage disequilibrium (LD) information. The intuition is that if the association is caused by a true genetic effect, neighboring markers in LD should show comparably significant P-values. If not, it may be evidence of spurious association. Previous studies have applied the same idea but only manually without a formal statistical framework. Our proposed method LD-PAC provides a systematic framework to quantitatively measure the evidence of spurious associations based on the likelihood ratio. Simulations show that LD-PAC can detect spurious associations with high detection rate (84%). In addition to detecting spurious associations, our method can also be used to "rescue" candidate associations from the supposedly unclean data such as the markers excluded by QC. Although the additional associations must be treated with care, they can suggest interesting regions. The application of our method to the Wellcome Trust Case Control Consortium (WTCCC) data led to the discovery of an additional candidate association for type 1 diabetes among the QC-excluded markers. This locus turns out to be in a region recently identified as significant by a meta-analysis performed after the WTCCC study was published., (© 2010 Wiley-Liss, Inc.)

Recent studies in genomics have highlighted the significance of sequence insertions in determining individual variation. Efforts to discover the content of these sequence insertions have been limited to short insertions and long unique insertions. Much of the inserted sequence in the typical human genome, however, is a mixture of repeated and unique sequence. Current methods are designed to assemble only unique sequence insertions, using reads that do not map to the reference. These methods are not able to assemble repeated sequence insertions, as the reads will map to the reference in a different locus.In this paper, we present a computational method for discovering the content of sequence insertions that are unique, repeated, or a combination of the two. Our method analyzes the read mappings and depth of coverage of paired-end reads to identify reads that originated from inserted sequence. We demonstrate the process of assembling these reads to characterize the insertion content. Our method is based on the idea of segment extension, which progressively extends segments of known content using paired-end reads. We apply our method in simulation to discover the content of inserted sequences in a modified mouse chromosome and show that our method produces reliable results at 40x coverage.

Background: The characterization of structural variations (SV) such as insertions, deletions and copy number variations is a critical step in the process of understanding the full genetic architecture of organisms. Copy number variations (CNV) have attracted much recent attention due to their effects on gene expression and disease status., Results: In this paper, we present a method that utilizes next-generation sequencing technologies (NGS), in order to both detect and reconstruct CNVs. We focus on a special type of CNV, namely tandemly organized de novo CNVs, which have been shown to occur with high frequency in the mouse genome., Conclusions: We apply our method to CNV regions randomly inserted into the reference mouse genome and show that our method achieves good performance for both detection and reconstruction of tandemly organized de novo CNVs.

Objectives: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs)., Methods and Materials: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets., Results: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms., Conclusions: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Genome-wide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Likely, there exist even more associated variants, which remain to be found if even larger association studies can be assembled. Meta-analysis provides a straightforward means of increasing study sample sizes without collecting new samples by combining existing data sets. One obstacle to combining studies is that they are often performed on platforms with different marker sets. Current studies overcome this issue by imputing genotypes missing from each of the studies and then performing standard meta-analysis techniques. We show that this approach may result in a loss of power since errors in imputation are not accounted for. We present a new method for performing meta-analysis over imputed single nucleotide polymorphisms, show that it is optimal with respect to power, and discuss practical implementation issues. Through simulation experiments, we show that our imputation aware meta-analysis approach outperforms or matches standard meta-analysis approaches., ((c) 2010 Wiley-Liss, Inc.)

The genetics of phenotypic variation in inbred mice has for nearly a century provided a primary weapon in the medical research arsenal. A catalog of the genetic variation among inbred mouse strains, however, is required to enable powerful positional cloning and association techniques. A recent whole-genome resequencing study of 15 inbred mouse strains captured a significant fraction of the genetic variation among a limited number of strains, yet the common use of hundreds of inbred strains in medical research motivates the need for a high-density variation map of a larger set of strains. Here we report a dense set of genotypes from 94 inbred mouse strains containing 10.77 million genotypes over 121,433 single nucleotide polymorphisms (SNPs), dispersed at 20-kb intervals on average across the genome, with an average concordance of 99.94% with previous SNP sets. Through pairwise comparisons of the strains, we identified an average of 4.70 distinct segments over 73 classical inbred strains in each region of the genome, suggesting limited genetic diversity between the strains. Combining these data with genotypes of 7570 gap-filling SNPs, we further imputed the untyped or missing genotypes of 94 strains over 8.27 million Perlegen SNPs. The imputation accuracy among classical inbred strains is estimated at 99.7% for the genotypes imputed with high confidence. We demonstrated the utility of these data in high-resolution linkage mapping through power simulations and statistical power analysis and provide guidelines for developing such studies. We also provide a resource of in silico association mapping between the complex traits deposited in the Mouse Phenome Database with our genotypes. We expect that these resources will facilitate effective designs of both human and mouse studies for dissecting the genetic basis of complex traits.

Motivation: Haplotype inference is an important step for many types of analyses of genetic variation in the human genome. Traditional approaches for obtaining haplotypes involve collecting genotype information from a population of individuals and then applying a haplotype inference algorithm. The development of high-throughput sequencing technologies allows for an alternative strategy to obtain haplotypes by combining sequence fragments. The problem of 'haplotype assembly' is the problem of assembling the two haplotypes for a chromosome given the collection of such fragments, or reads, and their locations in the haplotypes, which are pre-determined by mapping the reads to a reference genome. Errors in reads significantly increase the difficulty of the problem and it has been shown that the problem is NP-hard even for reads of length 2. Existing greedy and stochastic algorithms are not guaranteed to find the optimal solutions for the haplotype assembly problem., Results: In this article, we proposed a dynamic programming algorithm that is able to assemble the haplotypes optimally with time complexity O(m x 2(k) x n), where m is the number of reads, k is the length of the longest read and n is the total number of SNPs in the haplotypes. We also reduce the haplotype assembly problem into the maximum satisfiability problem that can often be solved optimally even when k is large. Taking advantage of the efficiency of our algorithm, we perform simulation experiments demonstrating that the assembly of haplotypes using reads of length typical of the current sequencing technologies is not practical. However, we demonstrate that the combination of this approach and the traditional haplotype phasing approaches allow us to practically construct haplotypes containing both common and rare variants.

In a genome-wide association study of structural brain degeneration, we mapped the 3D profile of temporal lobe volume differences in 742 brain MRI scans of Alzheimer's disease patients, mildly impaired, and healthy elderly subjects. After searching 546,314 genomic markers, 2 single nucleotide polymorphisms (SNPs) were associated with bilateral temporal lobe volume (P<5 x 10(-7)). One SNP, rs10845840, is located in the GRIN2B gene which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit. This protein - involved in learning and memory, and excitotoxic cell death - has age-dependent prevalence in the synapse and is already a therapeutic target in Alzheimer's disease. Risk alleles for lower temporal lobe volume at this SNP were significantly over-represented in AD and MCI subjects vs. controls (odds ratio=1.273; P=0.039) and were associated with mini-mental state exam scores (MMSE; t=-2.114; P=0.035) demonstrating a negative effect on global cognitive function. Voxelwise maps of genetic association of this SNP with regional brain volumes, revealed intense temporal lobe effects (FDR correction at q=0.05; critical P=0.0257). This study uses large-scale brain mapping for gene discovery with implications for Alzheimer's disease., (Copyright 2010 Elsevier Inc. All rights reserved.)

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

Inference of biological networks from high-throughput data is a central problem in bioinformatics. Particularly powerful for network reconstruction is data collected by recent studies that contain both genetic variation information and gene expression profiles from genetically distinct strains of an organism. Various statistical approaches have been applied to these data to tease out the underlying biological networks that govern how individual genetic variation mediates gene expression and how genes regulate and interact with each other. Extracting meaningful causal relationships from these networks remains a challenging but important problem. In this article, we use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. We evaluate our method using a well studied dataset consisting of both genetic variations and gene expressions collected over randomly segregated yeast strains. Our predictions of causal regulators, genes that control the expression of a large number of target genes, are consistent with previously known experimental evidence. In addition, our method can detect the absence of causal relationships and can distinguish between direct and indirect effects of variation on a gene expression level.

Genome-wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely on the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected in the study provided that they are present in a reference data set which contains both SNPs collected in the study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to previous methods, our method is up to ten times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods, and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

Innate immunity is the first line of defense against microbial infections. Although polymorphisms in toll-like receptors (TLRs) and downstream signaling molecules (CD14, TLR2, TLR4, TLR5, and IRAK4) affect the innate immune response, these variants account for only a portion of the ability of the host to respond to bacteria, fungi, and viruses. To identify other genes involved in the innate immune response, we challenged 16 inbred murine strains with lipopolysaccharide (LPS) systemically and measured serum concentrations of pro-inflammatory cytokines IL-1beta, IL-6, and TNFalpha, and the chemokine KC 6 hr post-treatment. Loci that segregate with strain phenotypes were identified by whole genome association (WGA) mapping of cytokine concentrations. Published gene expression profiles and quantitative trait loci (QTL) were then utilized to prioritize loci and genes that potentially regulate the host response to LPS. Sixteen loci were selected for further investigation by combining WGA analysis with previously published QTL for murine response to LPS or gram negative bacteria. Thirty-eight genes within these loci were then selected for further investigation on the basis of the significance of the identified locus, transcriptional response to LPS, and biological plausibility. RNA interference-mediated inhibition of 4 of 38 candidate genes was shown to block the production of IL-6 in J774A.1 macrophages. In summary, our analysis identified 4 genes that have not previously been implicated in innate immunity, namely, 1110058L19Rik, 4933415F23Rik, Fbxo9, and Ipo7. These genes could represent potential sepsis biomarkers or therapeutic targets that should be further investigated in human populations.

Nicotinic acetylcholine receptors (nAChRs) are combinations of subunits arranged as pentamers encircling a central cation channel. At least nine alpha and four beta subunits are expressed in the central and peripheral nervous systems; their presence in autonomic ganglia, the adrenal medulla, and central nervous system, with accompanying responses elicited by nicotinic agonists, point to their involvement in cardiovascular homeostasis. nAChRs formed by alpha3, alpha5, and beta4 subunits may regulate blood pressure (BP) by mediating release of catestatin, the endogenous nicotinic antagonist fragment of chromogranin A (CHGA) and potent inhibitor of catecholamine secretion. Genes encoding these subunits (CHRNA3, CHRNA5, and CHRNB4) are clustered on human chromosome 15q24. Because variation in this cluster may alter autonomic regulation of BP, we sequenced approximately 15 kilobase pairs in 15q24 containing their coding and 5'- and 3'-untranslated regions in 80 individuals. We identified 63 variants: 25 in coding regions of CHRNA3, CHRNA5, and CHRNB4 and 48 noncoding single-nucleotide polymorphisms (SNPs). Haplotype frequencies varied across ethnic populations. We assessed the contribution of six SNPs in the putative catestatin binding region of CHRNA3 and CHRNB4 to autonomic traits. In twins, catestatin and BP were heritable. CHRNA3 SNPs and haplotypes containing K95K (G285A) associated with circulating plasma catestatin, epinephrine levels, as well as systolic BP, suggesting altered coupling of the nAChRs to BP. Studies of chromaffin cells in vitro reveal that nicotinic agonist stimulation releases catecholamines and CHGA, a process augmented by overexpression of CHRNA3 and blocked by catestatin. These cellular events suggest a homeostatic mechanism underlying the pleiotropic actions of CHRNA3 genetic variation on autonomic function observed in twins.

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies--SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu., Competing Interests: The authors have declared that no competing interests exist.

Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

The HapMap provides a valuable resource to help uncover genetic variants of important complex phenotypes such as disease risk and outcome. Using the HapMap we can infer the patterns of LD within different human populations. This is a critical step for determining which SNPs to genotype as part of a study, estimating study power, designing a follow-up study to identify the causal variants, 'imputing' untyped SNPs, and estimating recombination rates along the genome. Despite its tremendous importance, the HapMap suffers from the fundamental limitation that at most 60 unrelated individuals are available per population. We present an analytical framework for analyzing the implications of a finite sample HapMap. We present and justify simple approximations for deriving analytical estimates of important statistics such as the square of the correlation coefficient r(2) between two SNPs. Finally, we use this framework to show that current HapMap based estimates of r(2) and power have significant errors, and that tag sets highly overestimate their coverage. We show that a reasonable increase in the number of individuals, such as that proposed by the 1000 genomes project, greatly reduces the errors due to finite sample size for a large proportion of SNPs.

In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called "regulatory hotspots," resulting in "trans-regulatory bands" in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects.

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL "hotspots"), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock., Competing Interests: The authors have declared that no competing interests exist.

The availability of various types of genomic data provides an opportunity to incorporate this data as prior information in genetic association studies. This information includes knowledge of linkage disequilibrium structure as well as which regions are likely to be involved in disease. In this paper, we present an approach for incorporating this information by revisiting how we perform multiple-hypothesis correction. In a traditional association study, in order to correct for multiple-hypothesis testing, the significance threshold at each marker, t, is set to control the total false-positive rate. In our framework, we vary the threshold at each marker t(i) and use these thresholds to incorporate prior information. We present a numerical procedure for solving for thresholds that maximizes association study power using prior information. We also present the results of benchmark simulation experiments using the HapMap data, which demonstrate a significant increase in association study power under this framework. We provide a Web server for performing association studies using our method and provide thresholds optimized for the Affymetrix 500 k and Illumina HumanHap 550 chips and demonstrate the application of our framework to the analysis of the Wellcome Trust Case Control Consortium data.

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

Background: Genetic isolates such as the Ashkenazi Jews (AJ) potentially offer advantages in mapping novel loci in whole genome disease association studies. To analyze patterns of genetic variation in AJ, genotypes of 101 healthy individuals were determined using the Affymetrix EAv3 500 K SNP array and compared to 60 CEPH-derived HapMap (CEU) individuals. 435,632 SNPs overlapped and met annotation criteria in the two groups., Results: A small but significant global difference in allele frequencies between AJ and CEU was demonstrated by a mean FST of 0.009 (P < 0.001); large regions that differed were found on chromosomes 2 and 6. Haplotype blocks inferred from pairwise linkage disequilibrium (LD) statistics (Haploview) as well as by expectation-maximization haplotype phase inference (HAP) showed a greater number of haplotype blocks in AJ compared to CEU by Haploview (50,397 vs. 44,169) or by HAP (59,269 vs. 54,457). Average haplotype blocks were smaller in AJ compared to CEU (e.g., 36.8 kb vs. 40.5 kb HAP). Analysis of global patterns of local LD decay for closely-spaced SNPs in CEU demonstrated more LD, while for SNPs further apart, LD was slightly greater in the AJ. A likelihood ratio approach showed that runs of homozygous SNPs were approximately 20% longer in AJ. A principal components analysis was sufficient to completely resolve the CEU from the AJ., Conclusion: LD in the AJ versus was lower than expected by some measures and higher by others. Any putative advantage in whole genome association mapping using the AJ population will be highly dependent on regional LD structure.

Objectives: Two genome-wide linkage surveys suggest chromosome 22q12 may contain a susceptibility locus for bipolar disorder (BPD) in the immediate region of the gene G protein receptor kinase-3 (GRK3). We previously published evidence that a single nucleotide polymorphism (SNP) in the promoter region of GRK3, designated P5, was associated with BPD. This SNP, however, was too rare (allele frequency 0.007) to explain the evidence for linkage., Methods: To identify other SNPs or haplotypes associated with illness, we have now sequenced an additional 28-kb genomic segment of GRK3 and tested an additional 35 SNPs for association with BPD in 181 Caucasian nuclear families., Results: Transmission disequilibrium test analyses identified two closely related disease-associated haplotypes defined by four SNPs located upstream of the promoter region: transmission to nontransmission ratios=54:22 and 20:9, odds ratios=2.50 and 2.36, and P values=0.0009 and 0.05. The best P value remained significant after correction for multiple testing. These two haplotypes were found on an entirely different set of chromosomes from the previously identified SNP P5. They had a combined frequency of approximately 0.10 and, therefore, a much greater population attributable risk for disease than the previously identified P5 haplotype., Conclusions: These data provide evidence that at least two distinct haplotypes, and possibly two or more different underlying mutations, in GRK3 might be associated with BPD. These new findings add support for the hypothesis that a dysregulation in GRK3 expression alters signaling desensitization and thereby predisposes to the development of BPD.

A dense map of genetic variation in the laboratory mouse genome will provide insights into the evolutionary history of the species and lead to an improved understanding of the relationship between inter-strain genotypic and phenotypic differences. Here we resequence the genomes of four wild-derived and eleven classical strains. We identify 8.27 million high-quality single nucleotide polymorphisms (SNPs) densely distributed across the genome, and determine the locations of the high (divergent subspecies ancestry) and low (common subspecies ancestry) SNP-rate intervals for every pairwise combination of classical strains. Using these data, we generate a genome-wide haplotype map containing 40,898 segments, each with an average of three distinct ancestral haplotypes. For the haplotypes in the classical strains that are unequivocally assigned ancestry, the genetic contributions of the Mus musculus subspecies--M. m. domesticus, M. m. musculus, M. m. castaneus and the hybrid M. m. molossinus--are 68%, 6%, 3% and 10%, respectively; the remaining 13% of haplotypes are of unknown ancestral origin. The considerable regional redundancy of the SNP data will facilitate imputation of the majority of these genotypes in less-densely typed classical inbred strains to provide a complete view of variation in additional strains.

Statistical and learning techniques are becoming increasingly popular for different tasks in bioinformatics. Many of the most powerful statistical and learning techniques are applicable to points in a Euclidean space but not directly applicable to discrete sequences such as protein sequences. One way to apply these techniques to protein sequences is to embed the sequences into a Euclidean space and then apply these techniques to the embedded points. In this work we introduce a biologically motivated sequence embedding, the homology kernel, which takes into account intuitions from local alignment, sequence homology, and predicted secondary structure. This embedding allows us to directly apply learning techniques to protein sequences. We apply the homology kernel in several ways. We demonstrate how the homology kernel can be used for protein family classification and outperforms state-of-the-art methods for remote homology detection. We show that the homology kernel can be used for secondary structure prediction and is competitive with popular secondary structure prediction methods. Finally, we show how the homology kernel can be used to incorporate information from homologous sequences in local sequence alignment.

Background: Chromogranin A, coreleased with catecholamines by exocytosis, is cleaved to the catecholamine release-inhibitory fragment catestatin. We identified a natural nonsynonymous variant of catestatin, Gly364Ser, that alters human autonomic function and blood pressure., Methods and Results: Gly364Ser heterozygotes and controls underwent physiological and biochemical phenotyping, including catecholamine production, chromogranin A precursor, and its catestatin product. Case-control studies replicated effects of the gene on blood pressure in the population. Gly364Ser displayed diminished inhibition of catecholamine secretion from cultured neurons. Gly/Ser heterozygotes displayed increased baroreceptor slope during upward deflections (by approximately 47%) and downward deflections (by approximately 44%), increased cardiac parasympathetic index (by approximately 2.4-fold), and decreased cardiac sympathetic index (by approximately 26%). Renal norepinephrine excretion was diminished by approximately 26% and epinephrine excretion by approximately 34% in Gly/Ser heterozygotes. The coalescent dated emergence of the variant to approximately 70,000 years ago. Gly364Ser was in linkage disequilibrium with 1 major Chromogranin A promoter haplotype, although promoter haplotypes did not predict autonomic phenotypes. The 364Ser variant was associated with lower diastolic blood pressure in 2 independent/confirmatory groups of patients with hypertension; genotype groups differed by approximately 5 to 6 mm Hg, and the polymorphism accounted for approximately 1.8% of population diastolic blood pressure variance, although a significant gene-by-sex interaction existed, with an enhanced effect in men., Conclusions: The catestatin Gly364Ser variant causes profound changes in human autonomic activity, both parasympathetic and sympathetic, and seems to reduce risk of developing hypertension, especially in men. A model for catestatin action in the baroreceptor center of the nucleus of the tractus solitarius accounts for these actions.

Recent high-throughput genotyping technologies, such as the Affymetrix 500k array and the Illumina HumanHap 550 beadchip, have driven down the costs of association studies and have enabled the measurement of single-nucleotide polymorphism (SNP) allele frequency differences between case and control populations on a genomewide scale. A key aspect in the efficiency of association studies is the notion of "indirect association," where only a subset of SNPs are collected to serve as proxies for the uncollected SNPs, taking advantage of the correlation structure between SNPs. Recently, a new class of methods for indirect association, multimarker methods, has been proposed. Although the multimarker methods are a considerable advancement, current methods do not fully take advantage of the correlation structure between SNPs and their multimarker proxies. In this article, we propose a novel multimarker indirect-association method, WHAP, that is based on a weighted sum of the haplotype frequency differences. In contrast to traditional indirect-association methods, we show analytically that there is a considerable gain in power achieved by our method compared with both single-marker and multimarker tests, as well as traditional haplotype-based tests. Our results are supported by empirical evaluation across the HapMap reference panel data sets, and a software implementation for the Affymetrix 500k and Illumina HumanHap 550 chips is available for download.

Motivation: Recently, a new type of expression data is being collected which aims to measure the effect of genetic variation on gene expression in pathways. In these datasets, expression profiles are constructed for multiple strains of the same model organism under the same condition. The goal of analyses of these data is to find differences in regulatory patterns due to genetic variation between strains, often without a phenotype of interest in mind. We present a new method based on notions of tight regulation and differential expression to look for sets of genes which appear to be significantly affected by genetic variation., Results: When we use categorical phenotype information, as in the Alzheimer's and diabetes datasets, our method finds many of the same gene sets as gene set enrichment analysis. In addition, our notion of correlated gene sets allows us to focus our efforts on biological processes subjected to tight regulation. In murine hematopoietic stem cells, we are able to discover significant gene sets independent of a phenotype of interest. Some of these gene sets are associated with several blood-related phenotypes., Availability: The programs are available by request from the authors.

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at http://research.calit2.net/hap/.

Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are an order of magnitude slower than sequence comparisons, making profiles impractical for large datasets. Also, because they are such a rich representation, profiles are difficult to visualize. To address these problems, we describe a method to map probabilistic profiles to a discrete alphabet while preserving most of the information in the profiles. We find an informationally optimal discretization using the Information Bottleneck approach (IB). We observe that an 80-character IB alphabet captures nearly 90% of the amino acid occurrence information found in profiles, compared to the consensus sequence's 78%. Distant homolog search with IB sequences is 88% as sensitive as with profiles compared to 61% with consensus sequences (AUC scores 0.73, 0.83, and 0.51, respectively), but like simple sequence comparison, is 30 times faster. Discrete IB encoding can therefore expand the range of sequence problems to which profile information can be applied to include batch queries over large databases like SwissProt, which were previously computationally infeasible.

Knowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million-SNP HapMap data set. Finally, we evaluated methods of estimating the value of r(2) between a pair of SNPs and concluded that all methods estimated r(2) well when the estimated value was >or=0.8.

Regulator of G protein signaling (RGS) proteins stimulate the GTPase activity of Galpha subunits of heterotrimeric G proteins, thereby negatively regulating G protein-coupled receptor signaling. RGS2, which preferentially alters Galphaq-mediated signaling, may be important for cardiovascular health, because knockout of RGS2 in mice is associated with altered smooth muscle relaxation and hypertension. In this study, we determined genetic variation in the human RGS2 gene by sequencing DNA in normotensive and hypertensive populations of whites (n=128) and blacks (n=122). We identified 14 single nucleotide polymorphisms and 2 two-base insertion/deletions (in/del; 1891 to 1892 TC and 2138 to 2139 AA). Although most of the genetic variants were found at low allelic frequency, in particular in coding regions, the 1891 to 1892 TC and 2138 to 2139 AA intronic in/del were in linkage disequilibrium and were associated with hypertension in blacks (P<0.05). We defined several haplotypes for the RGS2 gene, certain of which showed striking differences between whites and blacks. Additionally, 2 haplotypes had significantly different frequencies between hypertensive and normotensive black groups (P<0.05). We conclude that RGS2 is genetically conserved within coding regions but that the intronic in/del define ethnicity-specific haplotypes. Moreover, certain RGS2 variants that occur at greater frequency in hypertensive blacks may serve as ethnicity-specific genetic variants for this disease.

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 bench-marked 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.

The discovery of novel noncoding RNAs has been among the most exciting recent developments in biology. It has been hypothesized that there is, in fact, an abundance of functional noncoding RNAs (ncRNAs) with various catalytic and regulatory functions. However, the inherent signal for ncRNA is weaker than the signal for protein coding genes, making these harder to identify. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently detect all structural homologs in a genomic database by computing the sequence and structure similarity to the query. Our approach, based on structural filters that eliminate a large portion of the database while retaining the true homologs, allows us to search a typical bacterial genome in minutes on a standard PC. The results are two orders of magnitude better than the currently available software for the problem. We applied FastR to the discovery of novel riboswitches, which are a class of RNA domains found in the untranslated regions. They are of interest because they regulate metabolite synthesis by directly binding metabolites. We searched all available eubacterial and archaeal genomes for riboswitches from purine, lysine, thiamin, and riboflavin subfamilies. Our results point to a number of novel candidates for each of these subfamilies and include genomes that were not known to contain riboswitches.

Individual differences in DNA sequence are the genetic basis of human variability. We have characterized whole-genome patterns of common human DNA variation by genotyping 1,586,383 single-nucleotide polymorphisms (SNPs) in 71 Americans of European, African, and Asian ancestry. Our results indicate that these SNPs capture most common genetic variation as a result of linkage disequilibrium, the correlation among common SNP alleles. We observe a strong correlation between extended regions of linkage disequilibrium and functional genomic elements. Our data provide a tool for exploring many questions that remain regarding the causal role of common human DNA variation in complex human traits and for investigating the nature of genetic variation within and between human populations.