Using linkage genome scans to improve power of association in genome scans

Scanning the genome for association between markers and complex diseases typically requires testing hundreds of thousands of genetic polymorphisms. Testing such a large number of hypotheses exacerbates the trade-off between power to detect meaningful associations and the chance of making false discoveries. Even before the full genome is scanned, investigators often favor certain regions on the basis of the results of prior investigations, such as previous linkage scans. The remaining regions of the genome are investigated simultaneously because genotyping is relatively inexpensive compared with the cost of recruiting participants for a genetic study and because prior evidence is rarely sufficient to rule out these regions as harboring genes with variation of conferring liability (liability genes). However, the multiple testing inherent in broad genomic searches diminishes power to detect association, even for genes falling in regions of the genome favored a priori. Multiple testing problems of this nature are well suited for application of the false-discovery rate (FDR) principle, which can improve power. To enhance power further, a new FDR approach is proposed that involves weighting the hypotheses on the basis of prior data. We present a method for using linkage data to weight the association P values. Our investigations reveal that if the linkage study is informative, the procedure improves power considerably. Remarkably, the loss in power is small, even when the linkage study is uninformative. For a class of genetic models, we calculate the sample size required to obtain useful prior information from a linkage study. This inquiry reveals that, among genetic models that are seemingly equal in genetic information, some are much more promising than others for this mode of analysis.     

A pooling approach to detect signatures of selective sweeps in genome scans using microsatellites

We have evaluated a pooling approach that can reduce the number of polymerase chain reactions in a screen for selective sweeps by more than an order of magnitude. We show that the complex peak pattern that results from pooling of all samples from a given population is a faithful reflection of the composite pattern of the individual alleles, although with an under‐representation of the larger alleles. Candidate loci for selective sweeps can be identified by visual inspection of the pool patterns. We have also implemented a software tool, which can find suitable microsatellite loci in the vicinity of annotated genes.     

Exploration of signals of positive selection derived from genotype-based human genome scans using re-sequencing data

We have investigated whether regions of the genome showing signs of positive selection in scans based on haplotype structure also show evidence of positive selection when sequence-based tests are applied, whether the target of selection can be localized more precisely, and whether such extra evidence can lead to increased biological insights. We used two tools: simulations under neutrality or selection, and experimental investigation of two regions identified by the HapMap2 project as putatively selected in human populations. Simulations suggested that neutral and selected regions should be readily distinguished and that it should be possible to localize the selected variant to within 40 kb at least half of the time. Re-sequencing of two ~300 kb regions (chr4:158Mb and chr10:22Mb) lacking known targets of selection in HapMap CHB individuals provided strong evidence for positive selection within each and suggested the micro-RNA gene hsa-miR-548c as the best candidate target in one region, and changes in regulation of the sperm protein gene SPAG6 in the other.     

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.     

Management and visualization of whole genome shotgun assemblies using SAM

We have designed and implemented a system to manage whole genome shotgun sequences and whole genome sequence assembly data flow. The Sequence Assembly Manager (SAM) consists primarily of a MySQL relational database and Perl applications designed to easily manipulate and coordinate the analysis of sequence information and to view and report genome assembly progress through its Common Gateway Interface (CGI) web interface. The application includes a tool to compare sequence assemblies to fingerprint maps that has been used successfully to improve and validate both maps and sequence assemblies of the Rhodococcus sp.RHAI and Cryptococcus neoformans WM276 genomes.     

Detecting positive selection from genome scans of linkage disequilibrium

Background

During the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples.

Results

Surprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis.

Conclusions

Our study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting.     

Uninformative polymorphisms bias genome scans for signatures of selection

Background

A female preference for intense sexual visual signals is widespread in animals. Although the preferences for a signal per se and for the intensity of the signal were often regarded to have the identical origin, no study has demonstrated if this is true. It was suggested that the female fiddler crabs prefer males with courtship structures because of direct benefit to escape predation. Here we tested if female preference for both components (i.e. presence and size) of the courtship structure in Uca lactea is from the sensory bias to escape predation. If both components have the identical origin, females should show the same response to different-sized courtship structures regardless of predation risk.

Results

First, we observed responses of mate-searching female U. lactea to courting males with full-sized, half-sized and no semidomes which were experimentally manipulated. Females had a directional preference for males with bigger semidomes within normal variation. Thereafter, we tested the effect of predation risk on the female bias in the non-courtship context. When threatened by an avian mock predator, females preferentially approached burrows with full-sized semidomes regardless of reproductive cycles (i.e. reproductive periods and non-reproductive periods). When the predator cue was absent, however, females preferred burrows with semidomes without discriminating structure size during reproductive periods but did not show any bias during non-reproductive periods.

Conclusions

Results indicate that selection for the size of courtship structures in U. lactea may have an origin in the function to reduce predation risk, but that the preference for males with structures may have evolved by female choice, independent of predation pressure.     

Genetics of autism: from genome scans to candidate genes

The autistic disorder was firstly described by Leo Kanner sixty years ago. This complex developmental disability is characterized by social and communicative impairments and repetitive and stereotyped behaviours and interests. The prevalence of autism in the general population is about 1 in 1,000, with four males affected for one female. In approximately 15% of the cases, autism is associated with known genetic disorders, such as fragile X syndrome, tuberous sclerosis or Rett syndrome. Nevertheless, a recognised medical etiology can only be identified in a minority of cases. A higher recurrence risk in families with autistic subjects (45 times greater than the prevalence in the general population) and higher concordance for autism among monozygotic (60-90%) than dizygotic (0-10%) twins argue for a genetic predisposition to idiopathic autism. The past decade has been marked by an increased interest in the genetic basis of autism, with a series of multiple independent whole genome scans and chromosomal abnormalities studies. These analyses have pointed out several candidate regions on chromosomes 2q, 7q, 6q, 15q and sex chromosomes. These regions possess candidate genes that have been screened for mutations or association with autism. However, a clear involvement of a major susceptibility gene (or genes) in autism remains far from clear. The results from linkage studies and the clear drop in the concordance rates between monozygotic and dizygotic twins suggests that the genetic aetiology of autism is certainly heterogeneous (different genes in different families) and polygenic (more than one affected gene per individual). The almost finished sequence of the human genome and the generation of haplotype maps will shed light on the inter-individual genetic variability and will certainly increase the power and reliability of association studies for complex traits, such as autism.     

Controlling the false-positive rate in multilocus genome scans for selection

Rapid typing of genetic variation at many regions of the genome is an efficient way to survey variability in natural populations in an effort to identify segments of the genome that have experienced recent natural selection. Following such a genome scan, individual regions may be chosen for further sequencing and a more detailed analysis of patterns of variability, often to perform a parametric test for selection and to estimate the strength of a recent selective sweep. We show here that not accounting for the ascertainment of loci in such analyses leads to false inference of natural selection when the true model is selective neutrality, because the procedure of choosing unusual loci (in comparison to the rest of the genome-scan data) selects regions of the genome with genealogies similar to those expected under models of recent directional selection. We describe a simple and efficient correction for this ascertainment bias, which restores the false-positive rate to near-nominal levels. For the parameters considered here, we find that obtaining a test with the expected distribution of P-values depends on accurately accounting both for ascertainment of regions and for demography. Finally, we use simulations to explore the utility of relying on outlier loci to detect recent selective sweeps. We find that measures of diversity and of population differentiation are more effective than summaries of the site-frequency spectrum and that sequencing larger regions (2.5 kbp) in genome-scan studies leads to more power to detect recent selective sweeps.     

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.     

Chromosome substitution strains: some quantitative considerations for genome scans and fine mapping

A chromosome substitution strain (CSS) is an inbred strain in which one chromosome has been substituted from a different inbred strain by repeated backcrossing. A complete CSS set has one strain representing each chromosome against a uniform background, thus allowing genome-wide scans to be carried out for quantitative trait loci (QTLs) influencing any trait of interest. A one-way ANOVA by strain is first carried out, followed by planned comparisons using Dunnetts method. A QTL is detected and mapped to a chromosome when a significant difference is observed in a background strain vs CSS comparison. The most efficient ratio of background to CSS mice in any one comparison is 4.5:1, and the threshold for p < .05 genome-wide significance is estimated to be p = .003 to .004, a much less stringent criterion than any other mammalian mapping population. The use of false discovery rates tends to further reduce threshold stringency. Comparisons are made to the widely used conventional F₂ intercross, and both advantages and disadvantages are noted. The proportion of the trait variance due to a QTL is often much larger than the same QTL in an F₂, and the number of generations to attain fine mapping is greatly reduced. To serve as guidelines for planning experiments, methods to estimate sample sizes for QTL detection are presented for the initial genome scan and for subsequent fine mapping.     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Estimating odds ratios in genome scans: an approximate conditional likelihood approach

In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.     

Identifying insecticide resistance genes in mosquito by combining AFLP genome scans and 454 pyrosequencing

AFLP‐based genome scans are widely used to study the genetics of adaptation and to identify genomic regions potentially under selection. However, this approach usually fails to detect the actual genes or mutations targeted by selection owing to the difficulties of obtaining DNA sequences from AFLP fragments. Here, we combine classical AFLP outlier detection with 454 sequencing of AFLP fragments to obtain sequences from outlier loci. We applied this approach to the study of resistance to Bacillus thuringiensis israelensis (Bti) toxins in the dengue vector Aedes aegypti. A genome scan of Bti‐resistant and Bti‐susceptible A. aegypti laboratory strains was performed based on 432 AFLP markers. Fourteen outliers were detected using two different population genetic algorithms. Out of these, 11 were successfully sequenced. Three contained transposable elements (TEs) sequences, and the 10 outliers that could be mapped at a unique location in the reference genome were located on different supercontigs. One outlier was in the vicinity of a gene coding for an aminopeptidase potentially involved in Bti toxin‐binding. Patterns of sequence variability of this gene showed significant deviation from neutrality in the resistant strain but not in the susceptible strain, even after taking into account the known demographic history of the selected strain. This gene is a promising candidate for future functional analysis.     

Clinical interpretation of copy number variants in the human genome

Molecular methods, by which copy number variants (CNVs) detection is available, have been gradually introduced into routine diagnostics over the last 15 years. Despite this, some CNVs continue to be a huge challenge when it comes to clinical interpretation. CNVs are an important source of normal and pathogenic variants, but, in many cases, their impact on human health depends on factors that are not yet known. Therefore, perception of their clinical consequences can change over time, as our knowledge grows. This review summarises guidelines that facilitate correct classification of identified changes and discusses difficulties with the interpretation of rare, small CNVs.