A Genome Scan Conducted in a Multigenerational Pedigree with Convergent Strabismus Supports a Complex Genetic Determinism

A genome-wide linkage scan was conducted in a Northern-European multigenerational pedigree with nine of 40 related members affected with concomitant strabismus. Twenty-seven members of the pedigree including all affected individuals were genotyped using a SNP array interrogating > 300,000 common SNPs. We conducted parametric and non-parametric linkage analyses assuming segregation of an autosomal dominant mutation, yet allowing for incomplete penetrance and phenocopies. We detected two chromosome regions with near-suggestive evidence for linkage, respectively on chromosomes 8 and 18. The chromosome 8 linkage implied a penetrance of 0.80 and a rate of phenocopy of 0.11, while the chromosome 18 linkage implied a penetrance of 0.64 and a rate of phenocopy of 0. Our analysis excludes a simple genetic determinism of strabismus in this pedigree.     

MLEP: an R package for exploring the maximum likelihood estimates of penetrance parameters

ABSTRACT: BACKGROUND: Linkage analysis is a useful tool for detecting genetic variants that regulate a trait of interest, especially genes associated with a given disease. Although penetrance parameters play an important role in determining gene location, they are assigned arbitrary values according to the researcher's intuition or as estimated by the maximum likelihood principle. Several methods exist by which to evaluate the maximum likelihood estimates of penetrance, although not all of these are supported by software packages and some are biased by marker genotype information, even when disease development is due solely to the genotype of a single allele. FINDINGS: Programs for exploring the maximum likelihood estimates of penetrance parameters were developed using the R statistical programming language supplemented by external C functions. The software returns a vector of polynomial coefficients of penetrance parameters, representing the likelihood of pedigree data. From the likelihood polynomial supplied by the proposed method, the likelihood value and its gradient can be precisely computed. To reduce the effect of the supplied dataset on the likelihood function, feasible parameter constraints can be introduced into maximum likelihood estimates, thus enabling flexible exploration of the penetrance estimates. An auxiliary program generates a perspective plot allowing visual validation of the model's convergence. The functions are collectively available as the MLEP R package. CONCLUSIONS: Linkage analysis using penetrance parameters estimated by the MLEP package enables feasible localization of a disease locus. This is shown through a simulation study and by demonstrating how the package is used to explore maximum likelihood estimates. Although the input dataset tends to bias the likelihood estimates, the method yields accurate results superior to the analysis using intuitive penetrance values for disease with low allele frequencies. MLEP is part of the Comprehensive R Archive Network and is freely available at http://cran.r-project.org/web/packages/MLEP/index.html.     

Assessing the genetic diversity in small farm animal populations

Genetic variation is vital for the populations to adapt to varying environments and to respond to artificial selection; therefore, any conservation and development scheme should start from assessing the state of variation in the population. There are several marker-based and pedigree-based parameters to describe genetic variation. The most suitable ones are rate of inbreeding and effective population size, because they are not dependent on the amount of pedigree records. The acceptable level for effective population size can be considered from different angles leading to a conclusion that it should be at least 50 to 100. The estimates for the effective population size can be computed from the genealogical records or from demographic and marker information when pedigree data are not available. Marker information could also be used for paternity analysis and for estimation of coancestries. The sufficient accuracy in marker-based parameters would require typing thousands of markers. Across breeds, diversity is an important source of variation to rescue problematic populations and to introgress new variants. Consideration of adaptive variation brings new aspects to the estimation of the variation between populations.     

Impact of depth of pedigree and inclusion of historical data on the estimation of additive variance and breeding values in a sugarcane breeding program

Sugarcane breeders in Australia combine data across four selection programs to obtain estimates of breeding value for parents. When these data are combined with full pedigree information back to founding parents, computing limitations mean it is not possible to obtain information on all parents. Family data from one sugarcane selection program were analysed using two different genetic models to investigate how different depths of pedigree and amount of data affect the reliability of estimating breeding value of sugarcane parents. These were the parental and animal models. Additive variance components and breeding values estimated from different amounts of information were compared for both models. The accuracy of estimating additive variance components and breeding values improved as more pedigree information and historical data were included in analyses. However, adding years of data had a much larger effect on the estimation of variance components of the population, and breeding values of the parents. To accurately estimate breeding values of all sugarcane parents, a minimum of three generations of pedigree and 5 years of historical data were required, while more information (four generations of pedigree and 7 years of historical data) was required when identifying top parents to be selected for future cross pollination.     

Estimation of segregation and linkage parameters in simulated data. I. Segregation analyses with different ascertainment schemes.

We report the results of a simulation study designed to assess the capability of segregation analysis to detect Mendelian transmission and to estimate genetic model parameters for complex qualitative traits, characterized by heritability in the range 0.20-0.45 and low heterozygote penetrance. The pedigree analysis package, PAP, was used to perform the analyses. For all data sets, models of no transmission could be rejected. In most cases, models of Mendelian transmission could not be rejected; however, several samples approached significance levels. When Mendelian transmission was assumed, reasonably good parameter estimates were obtained, although heterozygote penetrances were often overestimated. Different sampling schemes were imposed on the simulated data in order to examine the extent of information loss with the reduction in sample size. One of these strategies (a sequential sampling scheme) appears to have resulted in critical loss of information in some cases.     

The use of marker‐based relationship information to estimate the heritability of body weight in a natural population: a cautionary tale

A number of procedures have been developed that allow the genetic parameters of natural populations to be estimated using relationship information inferred from marker data rather than known pedigrees. Three published approaches are available; the regression, pair‐wise likelihood and Markov Chain Monte Carlo (MCMC) sib‐ship reconstruction methods. These were applied to body weight and molecular data collected from the Soay sheep population of St. Kilda, which has a previously determined pedigree. The regression and pair‐wise likelihood approaches do not specify an exact pedigree and yielded unreliable heritability estimates, that were sensitive to alteration of the fixed effects. The MCMC method, which specifies a pedigree prior to heritability estimation, yielded results closer to those determined using the known pedigree. In populations of low average relationship, such as the Soay sheep population, determination of a reliable pedigree is more useful than indirect approaches that do not specify a pedigree.     

Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman''s rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations.     

How do misassigned paternities affect the estimation of heritability in the wild?

Studies of birds have recently played an important role in the increasing success of quantitative genetics applied to natural populations. However, these studies mostly base their pedigree relationships on social information, despite the known widespread genetic polygamy in avian species. Here, we study the influence of misassigned paternities, combined with the effect of pedigree size and depth, on the estimation of heritability. First, we compute simulations of a polygenic trait for two levels of heritability (0.1 and 0.4), several extra-pair paternity rates (ranging from 5% to 40%), and varying sample sizes (20, 50 and 100 broods) or pedigree depth (2 or 4 generations). We compare heritability estimates from the social and the genetic pedigree, running a restricted maximum-likelihood 'animal model'. Social pedigree underestimates heritability by an average of 0-17% for 5-20% extra-pair paternities and by up to 18% for 40% extra-pair paternities and a heritability of 0.4. Second, we identifyied extra-pair offspring using microsatellite loci in two populations of blue tits (Parus caeruleus) showing high levels of extra-pair paternities (15% and 25% of extra-pair offspring). We compare heritabilities of tarsus length and body mass estimated with pedigrees of increasing accuracy. These analyses suggest that the bias induced by misassigned paternities on heritability estimation depends on the level of heritability and the rate of paternity error. Typical rates of extra-pair paternities in birds (around 20% of offspring) should result in an underestimation of heritability of less than 15% when estimated over a minimum of 100 broods.     

线粒体DNA G7444A突变可能影响A1555G突变的表型表达

线粒体12S rRNA和tRNASer(UCN) 基因是导致非综合征型听力损失的两个突变热点区域。作者收集了1个母系遗传感音神经性聋家系, 该家系同时携带线粒体DNA (mtDNA) A1555G和G7444A突变。临床资料分析表明, 该家系包括药物致聋的耳聋外显率(所有耳聋患者/所有母系成员)为58%, 而非药物致聋的耳聋外显率(非药物性聋患者/所有母系成员)为25%, 明显高于其他携带A1555G突变的耳聋家系。先证者的线粒体全序列分析表明, 该线粒体基因组共有28个多态位点, 属于东亚人群B4c1单体型。在这些多态位点中, 除A1555G和G7444A突变外, 未发现其他有功能意义的突变。这表明mtDNA G7444A突变可能加重由A1555G突变造成的线粒体功能缺失, 从而增加耳聋的外显率。     

Comparison of genetic parameters from marker-based relationship, sibship, and combined models in Scots pine multi-site open-pollinated tests

Nine microsatellite DNA markers (simple sequence repeats, SSRs) were used to estimate pairwise relationships among 597 Scots pine (Pinus sylvestris) trees as well as to generate a sibship structure for quantitative genetic parameters’ estimation comparison. The studied trees were part of an open-pollinated progeny test of 102 first-generation parents. Three methods were used to estimate variance components and heritabilities, namely, structured pedigree (half- and full-sib), marker-based pairwise relationships (four pairwise estimators), and a combined pedigree and marker-based relationship. In each of the three methods, the same animal model was used to compute variances except when marker-based relationship was used wherein we substituted the average numerator relationship matrix (i.e., pedigree-based matrix) with that computed based on markers’ pairwise relationships. Our results showed a high correlation in estimated breeding values between the pedigree (full-sib) and the combined marker-pedigree estimates. The marker-based relationship method produced high correlations when individual site data were analyzed. In contrast, the marker-based relationship method resulted in a significant decrease in both variance estimation and their standard errors which were in concordance with earlier published results; however, no estimates were produced when across-site analyses were attempted. We concluded that the combined pedigree method is the best approach as it represents the historical (pairwise) and contemporary (pedigree) relationships among the tested individuals, a situation that cannot be attained by any of the used methods individually. This method is dependent on the number and informativeness of the markers used.     

Estimating genealogies from linked marker data: a Bayesian approach

Background  

Answers to several fundamental questions in statistical genetics would ideally require knowledge of the ancestral pedigree and of the gene flow therein. A few examples of such questions are haplotype estimation, relatedness and relationship estimation, gene mapping by combining pedigree and linkage disequilibrium information, and estimation of population structure.     

The ND4 G11696A mutation may influence the phenotypic manifestation of the deafness-associated 12S rRNA A1555G mutation in a four-generation Chinese family

We report here the clinical, genetic and molecular characterization of a large Han Chinese family with aminoglycoside-induced and nonsyndromic hearing loss. The penetrance of hearing loss (affected matrilineal relatives/total matrilineal relatives) in this pedigree was 53%, when aminoglycoside-induced deafness was included. When the effect of aminoglycosides was excluded, the penetrance of hearing loss in this pedigree was 42%. These matrilineal relatives exhibited a wide range of severity of hearing loss, varying from profound to normal hearing. Furthermore, these affected matrilineal relatives shared some common features: bilateral hearing loss of high frequencies and symmetries. Sequence analysis of mitochondrial DNA (mtDNA) in the pedigree identified the homoplasmic 12S rRNA A1555G mutation and other 35 variants belonging to Eastern Asian haplogroup D4. Of these, the V313I (G11696A) mutation in ND4 was associated with vision loss. However, the extremely low penetrance of visual loss, and the mild biochemical defect and the presence of one/167 Chinese controls indicted that the G11696A mutation is itself not sufficient to produce a clinical phenotype. Thus, the G11696A mutation may act in synergy with the primary deafness-associated 12S rRNA A1555G mutation in this Chinese family, thereby increasing the penetrance and expressivity of hearing loss in this Chinese pedigree.     

The accuracy of a heritability estimator using molecular information

The heritability of a quantitative trait is a key parameter to quantify the genetic variation present in a population. Although estimates of heritability require accurate information on the genetic relationship among individuals, pedigree data is generally lacking in natural populations. Nowadays, the increasing availability of DNA markers is making possible the estimation of coancestries from neutral molecular information. In 1996, K. Ritland developed an approach to estimate heritability from the regression of the phenotypic similarity on the marker-based coancestry. We carried out simulations to analyze the accuracy of the estimates of heritability obtained by this method using information from a variable number of neutral codominant markers. Because the main application of the estimator is on populations with no family structure, such as natural populations, its accuracy was tested under this scenario. However, the method was also investigated under other scenarios, in order to test the influence of different factors (family structure, assortative mating and phenotypic selection) on the precision. Our results suggest that the main factor causing a directional bias in the estimated heritability is the presence of phenotypic selection, and that very noisy estimates are obtained in the absence of a familiar structure and for small population sizes. The estimated heritabilities from marker-based coancestries showed lower accuracy than the estimated heritabilities from genealogical coancestries. However, a large amount of bias occurred even in the most favourable situation where genealogical coancestries are known. The results also indicate that the molecular markers are more suitable to infer coancestry than inbreeding.     

Estimation of genotype error rate using samples with pedigree information--an application on the GeneChip Mapping 10K array

Currently, most analytical methods assume all observed genotypes are correct; however, it is clear that errors may reduce statistical power or bias inference in genetic studies. We propose procedures for estimating error rate in genetic analysis and apply them to study the GeneChip Mapping 10K array, which is a technology that has recently become available and allows researchers to survey over 10,000 SNPs in a single assay. We employed a strategy to estimate the genotype error rate in pedigree data. First, the "dose-response" reference curve between error rate and the observable error number were derived by simulation, conditional on given pedigree structures and genotypes. Second, the error rate was estimated by calibrating the number of observed errors in real data to the reference curve. We evaluated the performance of this method by simulation study and applied it to a data set of 30 pedigrees genotyped using the GeneChip Mapping 10K array. This method performed favorably in all scenarios we surveyed. The dose-response reference curve was monotone and almost linear with a large slope. The method was able to estimate accurately the error rate under various pedigree structures and error models and under heterogeneous error rates. Using this method, we found that the average genotyping error rate of the GeneChip Mapping 10K array was about 0.1%. Our method provides a quick and unbiased solution to address the genotype error rate in pedigree data. It behaves well in a wide range of settings and can be easily applied in other genetic projects. The robust estimation of genotyping error rate allows us to estimate power and sample size and conduct unbiased genetic tests. The GeneChip Mapping 10K array has a low overall error rate, which is consistent with the results obtained from alternative genotyping assays.     

PEDIGREE ERROR DUE TO EXTRA‐PAIR REPRODUCTION SUBSTANTIALLY BIASES ESTIMATES OF INBREEDING DEPRESSION

Understanding the evolutionary dynamics of inbreeding and inbreeding depression requires unbiased estimation of inbreeding depression across diverse mating systems. However, studies estimating inbreeding depression often measure inbreeding with error, for example, based on pedigree data derived from observed parental behavior that ignore paternity error stemming from multiple mating. Such paternity error causes error in estimated coefficients of inbreeding (f) and reproductive success and could bias estimates of inbreeding depression. We used complete “apparent” pedigree data compiled from observed parental behavior and analogous “actual” pedigree data comprising genetic parentage to quantify effects of paternity error stemming from extra‐pair reproduction on estimates of f, reproductive success, and inbreeding depression in free‐living song sparrows (Melospiza melodia). Paternity error caused widespread error in estimates of f and male reproductive success, causing inbreeding depression in male and female annual and lifetime reproductive success and juvenile male survival to be substantially underestimated. Conversely, inbreeding depression in adult male survival tended to be overestimated when paternity error was ignored. Pedigree error stemming from extra‐pair reproduction therefore caused substantial and divergent bias in estimates of inbreeding depression that could bias tests of evolutionary theories regarding inbreeding and inbreeding depression and their links to variation in mating system.     

Genome-Wide Estimates of Coancestry,Inbreeding and Effective Population Size in the Spanish Holstein Population

Estimates of effective population size in the Holstein cattle breed have usually been low despite the large number of animals that constitute this breed. Effective population size is inversely related to the rates at which coancestry and inbreeding increase and these rates have been high as a consequence of intense and accurate selection. Traditionally, coancestry and inbreeding coefficients have been calculated from pedigree data. However, the development of genome-wide single nucleotide polymorphisms has increased the interest of calculating these coefficients from molecular data in order to improve their accuracy. In this study, genomic estimates of coancestry, inbreeding and effective population size were obtained in the Spanish Holstein population and then compared with pedigree-based estimates. A total of 11,135 animals genotyped with the Illumina BovineSNP50 BeadChip were available for the study. After applying filtering criteria, the final genomic dataset included 36,693 autosomal SNPs and 10,569 animals. Pedigree data from those genotyped animals included 31,203 animals. These individuals represented only the last five generations in order to homogenise the amount of pedigree information across animals. Genomic estimates of coancestry and inbreeding were obtained from identity by descent segments (coancestry) or runs of homozygosity (inbreeding). The results indicate that the percentage of variance of pedigree-based coancestry estimates explained by genomic coancestry estimates was higher than that for inbreeding. Estimates of effective population size obtained from genome-wide and pedigree information were consistent and ranged from about 66 to 79. These low values emphasize the need of controlling the rate of increase of coancestry and inbreeding in Holstein selection programmes.     

Combining the meiosis Gibbs sampler with the random walk approach for linkage and association studies with a general complex pedigree and multimarker loci

A linkage analysis for finding inheritance states and haplotype configurations is an essential process for linkage and association mapping. The linkage analysis is routinely based upon observed pedigree information and marker genotypes for individuals in the pedigree. It is not feasible for exact methods to use all such information for a large complex pedigree especially when there are many missing genotypic data. Proposed Markov chain Monte Carlo approaches such as a single-site Gibbs sampler or the meiosis Gibbs sampler are able to handle a complex pedigree with sparse genotypic data; however, they often have reducibility problems, causing biased estimates. We present a combined method, applying the random walk approach to the reducible sites in the meiosis sampler. Therefore, one can efficiently obtain reliable estimates such as identity-by-descent coefficients between individuals based on inheritance states or haplotype configurations, and a wider range of data can be used for mapping of quantitative trait loci within a reasonable time.     

Estimates of genetic parameters and breeding values from western larch open-pollinated families using marker-based relationship

The availability and affordability of genetic markers made it possible to estimate quantitative genetic parameters without mating designs' structured pedigree. Here, we compared 4-year height's heritability and individuals' breeding values for a western larch common-garden population of 1,418 offspring representing 15 open-pollinated families from a 41-clone seed orchard using (a) classical pedigree models such as half- and full-sib families and (b) a molecular marker-based pedigree-free model using four pair-wise relationship estimation methods using eight informative SSR markers. The results highlighted the commonly observed inflated estimates of genetic parameters often obtained from half-sib analyses, as well as demonstrating some of the full-sib analyses' caveats. The pedigree reconstruction permitted the identification of selfed individuals, thus allowing evaluating the impact of selfing on marker-based genetic parameter estimation. The results demonstrated the utility of marker-based methods as an alternative to the classical pedigree-based approaches. Unlike the pedigree-based methods, the marker-based approach allowed better partitioning the variance components as well as separating the non-additive and additive genetic variance. The theoretical underpinning of the marker-based approach was discussed.     

Efficiency and robustness of pedigree segregation analysis.

Different pedigree structures and likelihoods are examined to determine their efficiency for parameter estimation under one-locus models. For the cases simulated, family size has little effect; estimates based on unconditional likelihoods are generally more efficient than those based on conditional likelihoods. The proposed method of pedigree analysis under a one-locus model is found to be robust in the analysis of nuclear families: skewness of the data and polygenic inheritance will not lead to the spurious detection of major loci unless they occur simultaneously, and together with a moderate amount of environmental correlation among sibs.     

Calculating genetic risk figures using a programmable pocket calculator

Summary In autosomal irregular dominant as well as in sex-linked recessive inheritance, heterozygosity probabilities for up to six or eight pedigree members with unknown genotype can easily be calculated using a programmable pocket calculator. The application of the underlying combinatorial programs is explained by means of two relevant examples. Genetic counsellors without a computer at hand are provided with a simple and accurate method for calculating genetic risk figures taking into account pedigree data, incidence and/or mutation rate, reproductive fitness, as well as penetrance or results of heterozygosity tests of the disease in question.