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.

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.

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.

Uricotelism and low evaporative water loss in a South American frog.

A South American anuran (Phyllomedusa sauvagii) produced large amounts of semisolid urate when it was maintained on a diet of insects. Rates of evaporative water loss in Phyllomedusa sauvagii were only about 5 to 10 percent of those other anurans tested and were similar to those of lizards of comparable size.

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.

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.

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.

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.

Inferring coalescence times from DNA sequence data.

The paper is concerned with methods for the estimation of the coalescence time (time since the most recent common ancestor) of a sample of intraspecies DNA sequences. The methods take advantage of prior knowledge of population demography, in addition to the molecular data. While some theoretical results are presented, a central focus is on computational methods. These methods are easy to implement, and, since explicit formulae tend to be either unavailable or unilluminating, they are also more useful and more informative in most applications. Extensions are presented that allow for the effects of uncertainty in our knowledge of population size and mutation rates, for variability in population sizes, for regions of different mutation rate, and for inference concerning the coalescence time of the entire population. The methods are illustrated using recent data from the human Y chromosome.

Detecting gene conversion: primate visual pigment genes.

The effects of gene conversion can be detected in the DNA sequences of multigene families. We develop a permutation test of the significance of patterns of sequence mismatches, and apply it to the sequences of the red- and green-sensitive visual pigment genes of human and the diana monkey. Whereas conventional tests of the rate of sequence divergence are equivocal, the permutation test convincingly excludes divergence in the absence of gene conversion (p = 10(-6)).

DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands.

In DNA profile analysis, uncertainty arises due to a number of factors such as sampling error, single bands and correlations within and between loci. One of the most important of these factors is kinship: criminal and innocent suspect may share one or more bands through identity by descent from a common ancestor. Ignoring this uncertainty is consistently unfair to innocent suspects. The effect is usually small, but may be important in some cases. The report of the US National Research Committee proposed a complicated, ad-hoc and overly-conservative method of dealing with some of these problems. We propose an alternative approach which addresses directly the effect of kinship. Whilst remaining conservative, it is simple, logically coherent and makes efficient use of the data.

Evaluating DNA profile evidence when the suspect is identified through a database search.

The paper is concerned with the strength of DNA evidence when a suspect is identified via a search through a database of the DNA profiles of known individuals. Consideration of the appropriate likelihood ratio shows that in this setting the DNA evidence is (slightly) stronger than when a suspect is identified by other means, subsequently profiled, and found to match. The recommendation of the 1992 report of the US National Research Council that DNA evidence that is used to identify the suspect should not be presented at trial thus seems unnecessarily conservative. The widely held view that DNA evidence is weaker when it results from a database search seems to be based on a rationale that leads to absurd conclusions in some examples. Moreover, this view is inconsistent with the principle, which enjoys substantial support, that evidential weight should be measured by likelihood ratios. The strength of DNA evidence is shown also to be slightly increased for other forms of search procedure. While the DNA evidence is stronger after a database search, the overall case against the suspect may not be, and the problems of incorporating the DNA with the non-DNA evidence can be particularly important in such cases.

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.

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.

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.

Design and analysis of chromosome physical mapping experiments.

Mathematical and statistical aspects of constructing ordered-clone physical maps of chromosomes are reviewed. Three broad problems are addressed: analysis of fingerprint data to identify configurations of overlapping clones, prediction of the rate of progress of a mapping strategy and optimal design of pooling schemes for screening large clone libraries.

Inferring identify from DNA profile evidence.

The controversy over the interpretation of DNA profile evidence in forensic identification can be attributed in part to confusion over the mode(s) of statistical inference appropriate to this setting. Although there has been substantial discussion in the literature of, for example, the role of population genetics issues, few authors have made explicit the inferential framework which underpins their arguments. This lack of clarity has led both to unnecessary debates over ill-posed or inappropriate questions and to the neglect of some issues which can have important consequences. We argue that the mode of statistical inference which seems to underlie the arguments of some authors, based on a hypothesis testing framework, is not appropriate for forensic identification. We propose instead a logically coherent framework in which, for example, the roles both of the population genetics issues and of the nonscientific evidence in a case are incorporated. Our analysis highlights several widely held misconceptions in the DNA profiling debate. For example, the profile frequency is not directly relevant to forensic inference. Further, very small match probabilities may in some settings be consistent with acquittal. Although DNA evidence is typically very strong, our analysis of the coherent approach highlights situations which can arise in practice where alternative methods for assessing DNA evidence may be misleading.

Statistical analysis of DNA fingerprint data for ordered clone physical mapping of human chromosomes.

A statistical framework is proposed for analysing DNA fingerprint data from experiments aimed at constructing ordered clone physical maps of chromosomes. The fingerprint data consists of the lengths and hybridization states of restriction digest fragments and the paper develops a solution to the fundamental problem of deciding whether or not two randomly selected clones overlap. Overlap probabilities are calculated using Bayes' rule together with appropriate statistical descriptions of the chromosome and experimental procedure. The analysis is flexible, allowing a variety of assumptions to account for experimental errors and difficulties, such as unobserved fragments. The approach described here provides a basis for predicting the rate of progress of an experimental protocol and hence for comparing alternate protocols. It is readily generalized to related problems with a wide range of possible data. Results are presented for the clone mapping protocol currently being employed at Los Alamos National Laboratory on human chromosome 16 (Stallings et al., 1990, Proc. natl. Acad. Sci. U.S.A., 87, 6218-6222).