Bayesian identification of admixture events using multilocus molecular markers

Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.     

Family-based association analysis with tightly linked markers

Refining genomic regions which have been identified by linkage analysis to contain a disease susceptibility locus has proven to be a challenging task. Detecting association between the disease and a genetic marker can significantly narrow down the candidate region. Since an adequate sample of families is already available from the genome scan, family-based association tests may be used to search for association. The use of haplotypes consisting of tightly linked markers can be more powerful for detecting association than the use of individual markers. An extension of the transmission/disequilibrium test to allow the simultaneous analysis of more than one marker locus is complicated by ambiguity of phase in some families of the sample. The present paper shows that a recently proposed method for the analysis of nuclear families with a single affected child can be viewed as a special application of a more general principle. This observation justifies several modifications, potentially increasing the power, as well as an extension of the method to allow the analysis of general nuclear families. Finally, the problem of missing parental genotypes is discussed.     

Genetic analysis using trans-dominant linked markers in an F2 family

Trans-dominant linked markers pairs (trans referring to the repulsion linkage phase) provide a model for inferring the F₂ progeny genotype based upon both the conditional probabilities of F₂ genotypes, given the F₂ phenotype, and prior information on marker arrangement. Prior information of marker arrangement can be readily obtained from a linkage analysis performed on marker segregation data in a family resulting by crossing the F₁ individual to a tester parent or else can be obtained directly from the gametes of the F₁, or from recombinant inbred lines. We showed that a trans-dominant linked marker (TDLM) pair can be recoded as a co-dominant megalocus when the recombination fraction, r₁, for apair of TDLMs is less than 0.05. We obtained a maximum-likelihood estimator (MLE) of the recombination frequency, r₂, between a TDLM pair and a co-dominant marker in an F₂ family using the EM algorithm. The MLE was biased. Mean bias increased as r₁ and r₂ increased, and decreased as sample size increased. The information content for r₂ was compared to the information content of dominant and co-dominant markers segregating in an F₂ family. It was almost identical with two co-dominant markers when r₁0.01 and r₂0.05. For larger values of r₁, (0.05r₁0.15) a TDLM pair provided 75%–66% of the information content of two co-dominant markers. Although dominant markers can be converted to co-dominant markers by a laborious process of cloning, sequencing, and PCR, TDLM pairs could easily substitute for co-dominant markers in order to detect quantitative trait loci (QTLs) and estimate gene action in an F₂ family.     

Bayesian analyses of admixture in wild and domestic cats (Felis silvestris) using linked microsatellite loci

Methods recently developed to infer population structure and admixture mostly use individual genotypes described by unlinked neutral markers. However, Hardy-Weinberg and linkage disequilibria among independent markers decline rapidly with admixture time, and the admixture signals could be lost in a few generations. In this study, we aimed to describe genetic admixture in 182 European wild and domestic cats (Felis silvestris), which hybridize sporadically in Italy and extensively in Hungary. Cats were genotyped at 27 microsatellites, including 21 linked loci mapping on five distinct feline linkage groups. Genotypes were analysed with structure 2.1, a Bayesian procedure designed to model admixture linkage disequilibrium, which promises to assess efficiently older admixture events using tightly linked markers. Results showed that domestic and wild cats sampled in Italy were split into two distinct clusters with average proportions of membership Q > 0.90, congruent with prior morphological identifications. In contrast, free-living cats sampled in Hungary were assigned partly to the domestic and the wild cat clusters, with Q < 0.50. Admixture analyses of individual genotypes identified, respectively, 5/61 (8%), and 16-20/65 (25-31%) hybrids among the Italian wildcats and Hungarian free-living cats. Similar results were obtained in the past using unlinked loci, although the new linked markers identified additional admixed wildcats in Italy. Linkage analyses confirm that hybridization is limited in Italian, but widespread in Hungarian wildcats, a population that is threatened by cross-breeding with free-ranging domestic cats. The total panel of 27 loci performed better than the linked loci alone in the identification of domestic and known hybrid cats, suggesting that a large number of linked plus unlinked markers can improve the results of admixture analyses. Inferred recombination events led to identify the population of origin of chromosomal segments, suggesting that admixture mapping experiments can be designed also in wild populations.     

Construction of a high-throughput rat genetic mapping system with 466 arbitrarily primed-representational difference analysis markers

Linkage mapping of quantitative trait loci (QTLs) requires genetic markers that can be efficiently genotyped for a large number of individuals. To isolate genetic markers suitable for this purpose, we previously established the arbitrarily primed RDA (AP-RDA) method. Dot-blotting AP-PCR products (AP-amplicons) onto filters at a high density and hybridization of the filters with the AP-RDA markers made it possible to genotype a large number of individuals simultaneously for multiple loci. In this study, by using 25 primers or primer combinations, we isolated a total of 419 AP-RDA markers by subtracting the AP-amplicon of BUF rats from that of ACI rats, and vice versa. By combining 47 previously isolated markers, a rat genetic map was drawn with 466 AP-RDA markers. Between two given strains of rats other than ACI and BUF, the average informativeness of the markers was 38%. As for the intercross of ACI and BUF rats, 12 selected primers served to genotype 259 loci. In addition, the amounts and quality of genomic DNA to be used for AP-PCR were examined to guarantee reliable genotyping. Now, initial genome scanning of the rat for linkage analysis can be performed efficiently using this mapping system with AP-RDA markers. Received: 28 April 2000 / Accepted: 14 June 2000     

Bayesian analysis of population structure based on linked molecular information

The Bayesian model-based approach to inferring hidden genetic population structures using multilocus molecular markers has become a popular tool within certain branches of biology. In particular, it has been shown that heterogeneous data arising from genetically dissimilar latent groups of individuals can be effectively modelled using an unsupervised classification formulation. However, most currently employed models ignore potential linkage within the employed molecular information, and can therefore lead to biased inferences under certain circumstances. Utilizing the general theory of graphical models, we develop a framework that accounts for dependences both within linked molecular marker loci and DNA sequence data. Due to a high level of sequence conservation among eukaryotic species, the latter aspect is particularly relevant for analyzing rapidly evolving microbial species. The advantages of incorporating the dependence due to linkage in the classification models are illustrated by analyses of both simulated data and real samples of Bacillus cereus.     

Juxtaposed microsatellite systems as diagnostic markers for admixture: an empirical evaluation with brown trout (Salmo trutta) as model organism

A juxtaposed microsatellite system (JMS) is composed of two microsatellite repeat arrays separated by a sequence of less than 200 bp and more than 20 bp. This paper presents the first empirical evaluation of JMSs for the study of genetic admixture induced by man, with brown trout (Salmo trutta) as model organism. Two distinct admixture situations were studied: native populations from streams of the Atlantic basin and of the Mediterranean basin, respectively, all stocked with domestic strains originating from the Atlantic basin. For these two situations, we first evaluated by simulation the ability of JMSs to differentiate between alien alleles and naturally shared homoplasious or ancestral alleles, and thus to behave as diagnostic markers for admixture. Simulations indicated that JMSs are expected to be reliable diagnostic markers in most divergent (i.e. Mediterranean) populations and nonreliable diagnostic markers in most closely related (i.e. Atlantic) populations. Three JMSs were genotyped in domestic strains as well as in nonstocked and stocked populations of brown trout sampled in different rivers of the Mediterranean and Atlantic basins. The observed distributions of JMS haplotypes were consistent with simulation predictions confirming that JMSs were reliable diagnostic markers only over a given proportion of the species range, i.e. in substantially divergent populations. JMSs also reinforced the diagnostic character of three microsatellite sites for the studied Mediterranean populations. This last result is consistent with our simulation results which showed that, although much less frequently than at JMSs, diagnostic markers are likely to be found at single site microsatellites provided that the native Mediterranean population has a sufficiently small effective population size. For each population of the Mediterranean basin admixture coefficients did not differ significantly across JMSs and mean admixture coefficients sometimes differ among populations. The interpretation of the origin of JMS haplotypes based on the allele length variants was supported by nucleotide sequence analysis.     

Genetic analysis of cystic fibrosis using linked DNA markers.

Genetic linkage has been analyzed between cystic fibrosis (CF) and a number of markers on the long arm of chromosome 7, including D7S15, COL1A2, PON, MET, D7S8, and TCRB, using a cohort of 47 Canadian and 13 Danish CF families. The analysis confirms the previous observations that both MET and D7S8 are closely linked to CF. Based on the result from one family, MET appears to be more proximal to the centromere than CF. Our analysis also suggests that genetic heterogeneity may account for the high recombination fraction between CF and D7S8 observed in another family. In addition, a strong linkage disequilibrium has been observed between CF and the two closely flanking markers.     

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

Background  

A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction.     

Genetic diversity and admixture analysis of Sanfratellano and three other Italian horse breeds assessed by microsatellite markers

Sanfratellano is a native Sicilian horse breed, mainly reared in the north east of the Island, developed in the 19th century from local dams and sires with a restricted introgression of Oriental, African and, more recently, Maremmano stallions. In this study, the genetic relationships and admixture among Sanfratellano, the other two Sicilian autochthonous breeds and Maremmano breed were assessed using a set of microsatellites. The main goals were to infer the impact of Maremmano breed in the current Sanfratellano horse and to provide genetic information useful to improve the selection strategies of the Sanfratellano horse. The whole sample included 384 horses (238 Sanfratellano, 50 Sicilian Oriental Purebred, 30 Sicilian Indigenous and 66 Maremmano), chosen avoiding closely related animals. A total of 111 alleles from 11 microsatellite loci were detected, from four at HTG7 to 15 at ASB2 locus. The mean number of alleles was the lowest in Oriental Purebred (6.7), the highest in Sanfratellano (8.3). All the breeds showed a high level of gene diversity (He) ranging from 0.71 ± 0.04 in Sicilian Oriental Purebred to 0.81 ± 0.02 in Sicilian Indigenous. The genetic differentiation index was low; only about 6% of the diversity was found among breeds. Nei's standards (DS) and Reynolds' (DR) genetic distances reproduced the same population ranking. Individual genetic distances and admixture analysis revealed that: (a) nowadays Maremmano breed does not significantly influence the current Sanfratellano breed; (b) within Sanfratellano breed, it is possible to distinguish two well-defined groups with different proportions of Indigenous blood.     

Bayesian survival analysis using a MARS model

A Bayesian multivariate adaptive regression spline fitting approach is used to model univariate and multivariate survival data with censoring. The possible models contain the proportional hazards model as a subclass and automatically detect departures from this. A reversible jump Markov chain Monte Carlo algorithm is described to obtain the estimate of the hazard function as well as the survival curve.     

Prenatal diagnosis of cystic fibrosis using linked DNA markers and microvillar intestinal enzyme analysis

Summary Prenatal dignosis was performed for 47 pregnancies with 1 in 4 risk of cystic fibrosis, including 7 cases analyzed with linked DNA markers, 16 cases analyzed by microvillar intestinal enzyme testing, and 24 cases where both methods of testing were attempted. DNA was obtained by chorionic villus sampling in 10 cases and by amniocentesis in 21 cases, and diagnosis was based on analysis with the tightly linked DNA markers D7S8 and met. DNA analysis using these probes was fully informative in 74.4% of 90 couples with 1 in 4 risk. In 18 cases where both DNA results and microvillar intestinal enzyme data were diagnostic, there was agreement regarding the predicted status of the fetus. No adequate diagnosis was achieved in two cases where both diagnostic tests were attempted. Ourcome is known for 24 pregnancies including 10 where DNA analysis was diagnostic, and no errors in diagnosis were detected. Prenatal diagnosis of cystic fibrosis using DNA markers is highly informative and accurate, but microvillar intestinal enzyme analysis remains a valuable part of a complete diagnostic program.     

Genetic admixture and population structure analysis of Indian water buffaloes (Bubalus bubalis) using STR markers

Molecular Biology Reports - India has a vast riverine and swamp buffalo diversity adapted to various agro-ecological conditions. In the present study, genetic diversity data for 10 different...     

Bayesian analysis and model selection for interval-censored survival data

Interval-censored data occur in survival analysis when the survival time of each patient is only known to be within an interval and these censoring intervals differ from patient to patient. For such data, we present some Bayesian discretized semiparametric models, incorporating proportional and nonproportional hazards structures, along with associated statistical analyses and tools for model selection using sampling-based methods. The scope of these methodologies is illustrated through a reanalysis of a breast cancer data set (Finkelstein, 1986, Biometrics 42, 845-854) to test whether the effect of covariate on survival changes over time.     

Efficiency of typing unaffected relatives in an affected-sib-pair linkage study with single-locus and multiple tightly linked markers.

In an affected-sib-pair study, the parents are often unavailable for typing, particularly for diseases of late onset. In many cases, however, it is possible to sample unaffected siblings. It is therefore desirable to assess the contribution of such siblings to the power of such a study. The likelihood ratio introduced by Risch and improved by Holmans was extended to incorporate data from unaffected siblings. Tests based on two likelihoods were considered: the full likelihood of the data, based on the identity-by-descent (IBD) sharing states of the entire sibship, and a pseudolikelihood based on the IBD sharing states of the affected pair only, using the unaffected siblings to infer parental genotypes. The latter approach was found to be more powerful, except when penetrance was high. Typing an unaffected sibling, or just one parent, was found to give only a small increase in power except when the PIC of the marker was low. Even then, typing an unaffected relative increased the overall number of individuals that had to be typed to achieve a given power. If there is no highly informative marker locus in the area under study, it may be possible to "build" one by combining the alleles from two or more neighboring tightly linked loci into haplotypes. Typing two loci gave a sizeable power increase over a single locus, but typing further loci gave much smaller gains. Building haplotypes will introduce phase uncertainties, with the result that such a system will yield less power than will a single locus with the same number of alleles. This power loss was small, however, and did not affect the conclusions regarding the worth of typing unaffected relatives.     

Estimating the error rate in DNA diagnosis with linked markers

Recombination between the marker locus and disease locus introduces a risk of diagnostic error that must be considered when performing indirect diagnosis of monogenic disorders by means of a linked DNA polymorphism or another marker. A method is presented which improves the hitherto used estimates of the magnitude of this error. Principally, it makes use of the fact that recombination between marker and disease locus needs not necessarily increase the error rate; if it occurs twice or several times during the diagnostic process, the final diagnosis may be correct.