A genome-wide association study identifies novel gene associations with facial skin wrinkling and mole count in Latin Americans.

BACKGROUND: Genome-wide association studies (GWASs) have identified genes influencing skin ageing and mole count in Europeans, but little is known about the relevance of these (or other genes) in non-Europeans. OBJECTIVES: To conduct a GWAS for facial skin ageing and mole count in adults < 40 years old, of mixed European, Native American and African ancestry, recruited in Latin America. METHODS: Skin ageing and mole count scores were obtained from facial photographs of over 6000 individuals. After quality control checks, three wrinkling traits and mole count were retained for genetic analyses. DNA samples were genotyped with Illumina's HumanOmniExpress chip. Association testing was performed on around 8 703 729 single-nucleotide polymorphisms (SNPs) across the autosomal genome. RESULTS: Genome-wide significant association was observed at four genome regions: two were associated with wrinkling (in 1p13·3 and 21q21·2), one with mole count (in 1q32·3) and one with both wrinkling and mole count (in 5p13·2). Associated SNPs in 5p13·2 and in 1p13·3 are intronic within SLC45A2 and VAV3, respectively, while SNPs in 1q32·3 are near the SLC30A1 gene, and those in 21q21·2 occur in a gene desert. Analyses of SNPs in IRF4 and MC1R are consistent with a role of these genes in skin ageing. CONCLUSIONS: We replicate the association of wrinkling with variants in SLC45A2, IRF4 and MC1R reported in Europeans. We identify VAV3 and SLC30A1 as two novel candidate genes impacting on wrinkling and mole count, respectively. We provide the first evidence that SLC45A2 influences mole count, in addition to variants in this gene affecting melanoma risk in Europeans.

Evaluating DNA evidence in a genetically complex population.

In forensic genetics, the likelihood ratio (LR), measuring the value of DNA profile evidence, is computed from a database of allele frequencies. Here, we address the choice of database and adjustments for population structure and sample size in the context of Brazil. The Brazilian population underwent a complex process of colonization, migration and mating, which created an admixed genetic composition that makes it difficult to obtain an appropriate database for a given case. National databases are now available, as well as databases for many Brazilian states. However, those databases are not statistically random samples, and state boundaries may not accurately reflect the sub-structuring of genetic diversity. We compared the LR calculated using the relevant state-specific database with the statistics calculated when a national database and when international databases were used. We evaluated two methods of adjustment for population structure, due to Wright [13] and Balding and Nichols [14]. We also considered two adjustments for database sample size: the Balding size bias correction [15] and a minimum allele frequency [16]. Our results show that the use of a national database with the Balding and Nichols adjustment and θ = 0.002 generated lower LR values than did the state-specific database in more than 50% of the profiles simulated using the state-based allele frequencies, while θ = 0.01 produced lower LRs for more than 90% of the profiles. We conclude that the utilization of a national database for Brazilian cases can be justified in association with the appropriate adjustment for population structure.

Bridging trees for posterior inference on ancestral recombination graphs.

We present a new Markov chain Monte Carlo algorithm, implemented in the software Arbores, for inferring the history of a sample of DNA sequences. Our principal innovation is a bridging procedure, previously applied only for simple stochastic processes, in which the local computations within a bridge can proceed independently of the rest of the DNA sequence, facilitating large-scale parallelization.

Differential coexpression analysis of obesity-associated networks in human subcutaneous adipose tissue.

OBJECTIVE: To use a unique obesity-discordant sib-pair study design to combine differential expression analysis, expression quantitative trait loci (eQTLs) mapping and a coexpression regulatory network approach in subcutaneous human adipose tissue to identify genes relevant to the obese state. STUDY DESIGN: Genome-wide transcript expression in subcutaneous human adipose tissue was measured using Affymetrix U133 Plus 2.0 microarrays (Affymetrix, Santa Clara, CA, USA), and genome-wide genotyping data was obtained using an Applied Biosystems (Applied Biosystems; Life Technologies, Carlsbad, CA, USA) SNPlex linkage panel. SUBJECTS: A total of 154 Swedish families ascertained through an obese proband (body mass index (BMI) >30 kg m(-2)) with a discordant sibling (BMI>10 kg m(-2) less than proband). RESULTS: Approximately one-third of the transcripts were differentially expressed between lean and obese siblings. The cellular adhesion molecules (CAMs) KEGG grouping contained the largest number of differentially expressed genes under cis-acting genetic control. By using a novel approach to contrast CAMs coexpression networks between lean and obese siblings, a subset of differentially regulated genes was identified, with the previously GWAS obesity-associated neuronal growth regulator 1 (NEGR1) as a central hub. Independent analysis using mouse data demonstrated that this finding of NEGR1 is conserved across species. CONCLUSION: Our data suggest that in addition to its reported role in the brain, NEGR1 is also expressed in subcutaneous adipose tissue and acts as a central 'hub' in an obesity-related transcript network.

Heritability and genetic correlations of insulin resistance and component phenotypes in Asian Indian families using a multivariate analysis.

AIMS/HYPOTHESIS: Insulin resistance and related metabolic disturbances are more common among Asian Indians than European whites. Little is known about the heritability of insulin resistance traits in Asian Indians. Our objective was to estimate heritabilities and genetic correlations in Asian Indian families. METHODS: Phenotypic data were assembled for 181 UK Asian Indian probands with premature CHD, and their 1,454 first-, second- and third-degree relatives. We calculated (narrow-sense) heritabilities and genetic correlations for insulin resistance traits, and common environmental effects using all study participants and a multivariate model. The analysis was repeated in a subsample consisting of individuals not on drug therapy. RESULTS: Heritability estimates (SE) for individuals not on drug therapy were: BMI 0.31 (0.04), WHR 0.27 (0.04), systolic BP 0.29 (0.03), triacylglycerol 0.40 (0.04), HDL-cholesterol 0.53 (0.04), glucose 0.37 (0.03), HOMA of insulin resistance (HOMA-IR) 0.22 (0.04), and HbA(1c) 0.60 (0.04). We observed many significant genetic correlations between the traits, in particular between HOMA-IR and BMI. Heritability estimates were lower for all phenotypes when analysed among all participants. CONCLUSIONS/INTERPRETATION: Genetic factors contribute to a significant proportion of the total variance in insulin resistance and related metabolic disturbances in Asian Indian CHD families.

A likelihood ratio approach to family-based association studies with covariates.

We introduce a procedure for association based analysis of nuclear families that allows for dichotomous and more general measurements of phenotype and inclusion of covariate information. Standard generalized linear models are used to relate phenotype and its predictors. Our test procedure, based on the likelihood ratio, unifies the estimation of all parameters through the likelihood itself and yields maximum likelihood estimates of the genetic relative risk and interaction parameters. Our method has advantages in modelling the covariate and gene-covariate interaction terms over recently proposed conditional score tests that include covariate information via a two-stage modelling approach. We apply our method in a study of human systemic lupus erythematosus and the C-reactive protein that includes sex as a covariate.

Fine mapping of disease genes via haplotype clustering.

We propose an algorithm for analysing SNP-based population association studies, which is a development of that introduced by Molitor et al. [2003: Am J Hum Genet 73:1368-1384]. It uses clustering of haplotypes to overcome the major limitations of many current haplotype-based approaches. We define a between-haplotype score that is simple, yet appears to capture much of the information about evolutionary relatedness of the haplotypes in the vicinity of a (unobserved) putative causal locus. Haplotype clusters can then be defined via a putative ancestral haplotype and a cut-off distance. The number of an individual's two haplotypes that lie within the cluster predicts the individual's genotype at the causal locus. This predicted genotype can then be investigated for association with the phenotype of interest. We implement our approach within a Markov-chain Monte Carlo algorithm that, in effect, searches over locations and ancestral haplotypes to identify large, case-rich clusters. The algorithm successfully fine-maps a causal mutation in a test analysis using real data, and achieves almost 98% accuracy in predicting the genotype at the causal locus. A simulation study indicates that the new algorithm is substantially superior to alternative approaches, and it also allows us to identify situations in which multi-point approaches can substantially improve over single-SNP analyses. Our algorithm runs quickly and there is scope for extension to a wide range of disease models and genomic scales.

Little loss of information due to unknown phase for fine-scale linkage-disequilibrium mapping with single-nucleotide-polymorphism genotype data.

We present the results of a simulation study that indicate that true haplotypes at multiple, tightly linked loci often provide little extra information for linkage-disequilibrium fine mapping, compared with the information provided by corresponding genotypes, provided that an appropriate statistical analysis method is used. In contrast, a two-stage approach to analyzing genotype data, in which haplotypes are inferred and then analyzed as if they were true haplotypes, can lead to a substantial loss of information. The study uses our COLDMAP software for fine mapping, which implements a Markov chain-Monte Carlo algorithm that is based on the shattered coalescent model of genetic heterogeneity at a disease locus. We applied COLDMAP to 100 replicate data sets simulated under each of 18 disease models. Each data set consists of haplotype pairs (diplotypes) for 20 SNPs typed at equal 50-kb intervals in a 950-kb candidate region that includes a single disease locus located at random. The data sets were analyzed in three formats: (1). as true haplotypes; (2). as haplotypes inferred from genotypes using an expectation-maximization algorithm; and (3). as unphased genotypes. On average, true haplotypes gave a 6% gain in efficiency compared with the unphased genotypes, whereas inferring haplotypes from genotypes led to a 20% loss of efficiency, where efficiency is defined in terms of root mean integrated square error of the location of the disease locus. Furthermore, treating inferred haplotypes as if they were true haplotypes leads to considerable overconfidence in estimates, with nominal 50% credibility intervals achieving, on average, only 19% coverage. We conclude that (1). given appropriate statistical analyses, the costs of directly measuring haplotypes will rarely be justified by a gain in the efficiency of fine mapping and that (2). a two-stage approach of inferring haplotypes followed by a haplotype-based analysis can be very inefficient for fine mapping, compared with an analysis based directly on the genotypes.

Identification of functionally important regions of a haemoglobin receptor from Neisseria meningitidis.

The HmbR outer-membrane receptor enables Neisseria meningitidis to use haemoglobin (Hb) as a source of iron. This protein functions by binding Hb, removing haem from it, and releasing the haem into the periplasm. Functionally important HmbR receptor domains were discerned using a series of HmbR deletions and site-directed mutations. Mutations exhibiting similar defective phenotypes in N. meningitidis fell into two groups. The first group of mutations affected Hb binding and were located in putative extracellular loops (L) L2 (amino acid residues (aa) 192-230) and L3 (aa 254-284). The second group of mutations resulted in a failure to utilize Hb but proficiency in Hb binding was retained. These mutations localized to the putative extracellular loops L6 (aa 420-462) and L7 (aa 486-516). A highly conserved protein motif found in all haem/Hb receptors, within putative extracellular loop L7 of HmbR, is essential for Hb utilization but not required for Hb binding. This finding suggests a mechanistic involvement of this motif in haem removal from Hb. In addition, an amino-terminal deletion in the putative cork-like domain of HmbR affected Hb usage but not Hb binding. This result supports a role of the cork domain in utilization steps that are subsequent to Hb binding.

Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots.

Recent studies of human populations suggest that the genome consists of chromosome segments that are ancestrally conserved ('haplotype blocks'; refs. 1-3) and have discrete boundaries defined by recombination hot spots. Using publicly available genetic markers, we have constructed a first-generation haplotype map of chromosome 19. As expected for this marker density, approximately one-third of the chromosome is encompassed within haplotype blocks. Evolutionary modeling of the data indicates that recombination hot spots are not required to explain most of the observed blocks, providing that marker ascertainment and the observed marker spacing are considered. In contrast, several long blocks are inconsistent with our evolutionary models, and different mechanisms could explain their origins.

Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies.

We present a Bayesian, Markov-chain Monte Carlo method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps. The method explicitly models the genealogy underlying a sample of case chromosomes in the vicinity of a putative disease locus, in contrast with the assumption of a star-shaped tree made by many existing multipoint methods. Within this modeling framework, we can allow for missing marker information and for uncertainty about the true underlying genealogy and the makeup of ancestral marker haplotypes. A crucial advantage of our method is the incorporation of the shattered coalescent model for genealogies, allowing for multiple founding mutations at the disease locus and for sporadic cases of disease. Output from the method includes approximate posterior distributions of the location of the disease locus and population-marker haplotype proportions. In addition, output from the algorithm is used to construct a cladogram to represent genetic heterogeneity at the disease locus, highlighting clusters of case chromosomes sharing the same mutation. We present detailed simulations to provide evidence of improvements over existing methodology. Furthermore, inferences about the location of the disease locus are shown to remain robust to modeling assumptions.

MAC5: Bayesian inference of phylogenetic trees from DNA sequences incorporating gaps.

SUMMARY: MAC5 implements MCMC sampling of the posterior distribution of tree topologies from DNA sequences containing gaps by using a five state model of evolution (the four nucleotides and the gap character).

Models of sequence evolution for DNA sequences containing gaps.

Most evolutionary tree estimation methods for DNA sequences ignore or inefficiently use the phylogenetic information contained within shared patterns of gaps. This is largely due to the computational difficulties in implementing models for insertions and deletions. A simple way to incorporate this information is to treat a gap as a fifth character (with the four nucleotides being the other four) and to incorporate it within a Markov model of nucleotide substitution. This idea has been dismissed in the past, since it treats a multiple-site insertion or deletion as a sequence of independent events rather than a single event. While this is true, we have found that under many circumstances it is better to incorporate gap information inadequately than to ignore it, at least for topology estimation. We propose an extension to a class of nucleotide substitution models to incorporate the gap character and show that, for data sets (both real and simulated) with short and medium gaps, these models do lead to effective use of the information contained within insertions and deletions. We also implement an ad hoc method in which the likelihood at columns containing multiple-site gaps is downweighted in order to avoid giving them undue influence. The precision of the estimated tree, assessed using Markov chain Monte Carlo techniques to find the posterior distribution over tree space, improves under these five-state models compared with standard methods which effectively ignore gaps.

Measuring gametic disequilibrium from multilocus data.

We describe a Bayesian approach to analyzing multilocus genotype or haplotype data to assess departures from gametic (linkage) equilibrium. Our approach employs a Markov chain Monte Carlo (MCMC) algorithm to approximate the posterior probability distributions of disequilibrium parameters. The distributions are computed exactly in some simple settings. Among other advantages, posterior distributions can be presented visually, which allows the uncertainties in parameter estimates to be readily assessed. In addition, background knowledge can be incorporated, where available, to improve the precision of inferences. The method is illustrated by application to previously published datasets; implications for multilocus forensic match probabilities and for simple association-based gene mapping are also discussed.

Bayesian fine-scale mapping of disease loci, by hidden Markov models.

We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers-in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain-Monte Carlo simulation methods, we obtain posterior distributions of model parameter estimates including disease-gene location and the age of the disease-predisposing mutation. In addition, we allow for heterogeneity in recombination rates, across the candidate region, to account for recombination hot and cold spots. We also obtain, for the ancestral marker haplotype, a posterior distribution that is unique to our method and that, unlike maximum-likelihood estimation, can properly account for uncertainty. We apply the method to data for cystic fibrosis and Huntington disease, for which mutations in disease genes have already been identified. The new method performs well compared with existing multi-locus mapping methods.

Excision of IS492 requires flanking target sequences and results in circle formation in Pseudoalteromonas atlantica.

The gram-negative marine bacterium Pseudoalteromonas atlantica produces extracellular polysaccharide (EPS) that is important in biofilm formation by this bacterium. Insertion and precise excision of IS492 at a locus essential for extracellular polysaccharide production (eps) controls phase variation of EPS production in P. atlantica. Examination of IS492 transposition in P. atlantica by using a PCR-based assay revealed a circular form of IS492 that may be an intermediate in transposition or a terminal product of excision. The DNA sequence of the IS492 circle junction indicates that the ends of the element are juxtaposed with a 5-bp spacer sequence. This spacer sequence corresponds to the 5-bp duplication of the chromosomal target sequence found at all IS492 insertion sites on the P. atlantica chromosome that we identified by using inverse PCR. IS492 circle formation correlated with precise excision of IS492 from the P. atlantica eps target sequence when introduced into Escherichia coli on a plasmid. Deletion analyses of the flanking host sequences at the eps insertion site for IS492 demonstrated that the 5-bp duplicated target sequence is essential for precise excision of IS492 and circle formation in E. coli. Excision of IS492 in E. coli also depends on the level of expression of the putative transposase, MooV. A regulatory role for the circular form of IS492 is suggested by the creation of a new strong promoter for expression of mooV by the joining of the ends of the insertion sequence element at the circle junction.

When can a DNA profile be regarded as unique?

The probability that a defendant's DNA profile is unique in a population of untyped individuals is shown to be bounded below by one minus twice the sum of the match probabilities over the population. This bound assumes that the possibility of laboratory or handling error can be neglected, and applies only when there is no non-DNA evidence in favour of the defendant. There cannot be a completely general lower bound: if there is overwhelming non-DNA evidence that the defendant is not the source of the crime stain, then that is also overwhelming evidence of non-uniqueness. Application to k-locus short tandem repeat (STR) profiles is discussed, and illustrated with calculations based on the 6-STR-locus system used in current UK casework. However, because of the problem of the non-DNA evidence, there seems to be no satisfactory way for an expert witness to address the question of uniqueness in court.

Genealogical inference from microsatellite data.

Ease and accuracy of typing, together with high levels of polymorphism and widespread distribution in the genome, make microsatellite (or short tandem repeat) loci an attractive potential source of information about both population histories and evolutionary processes. However, microsatellite data are difficult to interpret, in particular because of the frequency of back-mutations. Stochastic models for the underlying genetic processes can be specified, but in the past they have been too complicated for direct analysis. Recent developments in stochastic simulation methodology now allow direct inference about both historical events, such as genealogical coalescence times, and evolutionary parameters, such as mutation rates. A feature of the Markov chain Monte Carlo (MCMC) algorithm that we propose here is that the likelihood computations are simplified by treating the (unknown) ancestral allelic states as auxiliary parameters. We illustrate the algorithm by analyzing microsatellite samples simulated under the model. Our results suggest that a single microsatellite usually does not provide enough information for useful inferences, but that several completely linked microsatellites can be informative about some aspects of genealogical history and evolutionary processes. We also reanalyze data from a previously published human Y chromosome microsatellite study, finding evidence for an effective population size for human Y chromosomes in the low thousands and a recent time since their most recent common ancestor: the 95% interval runs from approximately 15, 000 to 130,000 years, with most likely values around 30,000 years.

Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient.

Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects-of uncertainty about the nuisance parameters--the allele frequencies--as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to he investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.