Human Phenylalanine Hydroxylase Mutations and Hyperphenylalaninemia Phenotypes: A Metanalysis of Genotype-Phenotype Correlations

We analyzed correlations between mutant genotypes at the human phenylalanine hydroxylase locus (gene symbol PAH) and the corresponding hyperphenylalaninemia (HPA) phenotypes (notably, phenylketonuria [OMIM 261600]). We used reports, both published and in the PAH Mutation Analysis Consortium Database, on 365 patients harboring 73 different PAH mutations in 161 different genotypes. HPA phenotypes were classified as phenylketonuria (PKU), variant PKU, and non-PKU HPA. By analysis both of homoallelic mutant genotypes and of "functionally hemizygous" heteroallelic genotypes, we characterized the phenotypic effect of 48 of the 73 different, largely missense mutations. Among those with consistent in vivo expression, 24 caused PKU, 3 caused variant PKU, and 10 caused non-PKU HPA. However, 11 mutations were inconsistent in their effect: 9 appeared in two different phenotype classes, and 2 (I65T and Y414C) appeared in all three classes. Seven mutations were inconsistent in phenotypic effect when in vitro (unit-protein) expression was compared with the corresponding in vivo phenotype (an emergent property). We conclude that the majority of PAH mutations confer a consistent phenotype and that this is concordant with their effects, when known, predicted from in vitro expression analysis. However, significant inconsistencies, both between in vitro and in vivo phenotypes and between different individuals with similar PAH genotypes, reveal that the HPA-phenotype is more complex than that predicted by Mendelian inheritance of alleles at the PAH locus.     

TBX3 and ASIP genotypes reveal discrepancies in officially recorded coat colors of Hucul horses

Although only a few specific pigmentation types are allowed within the Hucul horse registry, accurate determination of particular coat colors can be uncertain due to the presence of variation in color shades and segregation of multiple dun dilution variants. Herein, we genotyped the previously identified polymorphisms within two coat color loci TBX3 (T-box 3) and ASIP (Agouti Signaling Protein) in 462 Hucul individuals and compared the genotype predicted phenotypes with observed pigmentation types provided in the Polish Horse Breeders Association database. We identified disagreement between the predicted and recorded coat color in 157 horses (34%). The most common error was misclassification of horses with the nd1/nd1 and nd1/nd2 genotypes, what may be related with the occurrence of some ‘intermediate’ dilution phenotypes in such individuals. We have also proven that the frequency of the dominant dun dilution allele (D) (0.30) is higher than previously predicted by available studbooks. The D allele(s) is easily ‘hidden’ in various phenotypic groups including dark bay and black, therefore we hypothesized that the dun dilution effect itself is not as strongly epistatic in the Hucul horse as described in other horse breeds. This may be the result of an additional genetic modifier suppressing D allele phenotypic effect.     

The Mammalian Phenotype Ontology as a tool for annotating,analyzing and comparing phenotypic information

The Mammalian Phenotype (MP) Ontology enables robust annotation of mammalian phenotypes in the context of mutations, quantitative trait loci and strains that are used as models of human biology and disease. The MP Ontology supports different levels and richness of phenotypic knowledge and flexible annotations to individual genotypes. It continues to develop dynamically via collaborative input from research groups, mutagenesis consortia, and biological domain experts. The MP Ontology is currently used by the Mouse Genome Database and Rat Genome Database to represent phenotypic data.     

Two-Variance-Component Model Improves Genetic Prediction in Family Datasets

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively with best linear unbiased prediction (BLUP) methods. Such methods were pioneered in plant and animal-breeding literature and have since been applied to predict human traits, with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two-variance-component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated with genetic markers. In simulations using real genotypes from the Candidate-gene Association Resource (CARe) and Framingham Heart Study (FHS) family cohorts, we demonstrate that the two-variance-component model achieves gains in prediction r² over standard BLUP at current sample sizes, and we project, based on simulations, that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two-variance-component model significantly improves prediction r² in each case, with up to a 20% relative improvement. We also find that standard mixed-model association tests can produce inflated test statistics in datasets with related individuals, whereas the two-variance-component model corrects for inflation.     

BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data

Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be downloaded from URL http://protein.bio.unipd.it/download/.     

Evaluation of the best linear unbiased prediction method for breeding values of fruit-quality traits in citrus

Fruit-quality trait improvement is an important objective in citrus breeding; however, fruit breeding programs often accumulate highly unbalanced phenotypic records, which are a serious obstacle in comparing and selecting genotypes. The best linear unbiased prediction (BLUP) method can be used to overcome these difficulties, but few fruit breeding programs have adopted the method, and to our knowledge, the method has not yet been used to predict breeding values of traits based on pedigree information and genetic correlations between traits in citrus. Accordingly, we used the BLUP method to predict the breeding values of nine fruit-quality traits (fruit weight, fruit skin color, fruit surface texture, peelability, flesh color, pulp firmness, segment firmness, sugar content, and acid content) utilizing phenotypic records collected over several years as part of the citrus breeding program conducted at the Kuchinotsu branch of the National Institute of Fruit Tree Science in Japan. Although the accumulated phenotypic records were highly unbalanced, the BLUP method was able to predict the breeding values of all 2122 genotypes (111 parental cultivars and 2011 F₁ offspring from 126 pair-cross families), as well as estimates of several genetic parameters, including narrow-sense heritability and phenotypic and genotypic correlations. These findings demonstrate the utility of the BLUP method in citrus crossbreeding and provide predicted breeding values, which can be used to select superior genotypes in the Kuchinotsu Citrus Breeding Program and related genetic selection endeavors.     

Single nucleotide variations: Biological impact and theoretical interpretation

Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.     

A European multicenter study of phenylalanine hydroxylase deficiency: classification of 105 mutations and a general system for genotype-based prediction of metabolic phenotype.

Phenylketonuria (PKU) and mild hyperphenylalaninemia (MHP) are allelic disorders caused by mutations in the gene encoding phenylalanine hydroxylase (PAH). Previous studies have suggested that the highly variable metabolic phenotypes of PAH deficiency correlate with PAH genotypes. We identified both causative mutations in 686 patients from seven European centers. On the basis of the phenotypic characteristics of 297 functionally hemizygous patients, 105 of the mutations were assigned to one of four arbitrary phenotype categories. We proposed and tested a simple model for correlation between genotype and phenotypic outcome. The observed phenotype matched the predicted phenotype in 79% of the cases, and in only 5 of 184 patients was the observed phenotype more than one category away from that expected. Among the seven contributing centers, the proportion of patients for whom the observed phenotype did not match the predicted phenotype was 4%-23% (P<.0001), suggesting that differences in methods used for mutation detection or phenotype classification may account for a considerable proportion of genotype-phenotype inconsistencies. Our data indicate that the PAH-mutation genotype is the main determinant of metabolic phenotype in most patients with PAH deficiency. In the present study, the classification of 105 PAH mutations may allow the prediction of the biochemical phenotype in >10,000 genotypes, which may be useful for the management of hyperphenylalaninemia in newborns.     

Estimation of Allele Frequencies at Isoloci

In some polyploid animals and plants, pairs of duplicated loci occur that share alleles encoding proteins with identical electrophoretic mobilities. Except in cases where these ``isoloci' are known to be inherited tetrasomically, individual genotypes cannot be determined unambiguously, and there is no direct way to assign observed variation to a particular locus of the pair. For a pair of diallelic isoloci, nine genotypes are possible but only five phenotypes can be identified, corresponding to individuals with 0-4 doses of the variant allele. A maximum likelihood (ML) approach is used here to identify the set of allele frequencies (p, q) at the individual gene loci with the highest probability of producing the observed phenotypic distribution. A likelihood ratio test is used to generate the asymmetrical confidence intervals around ML estimates. Simulations indicate that the standard error of p is typically about twice the binomial sampling error associated with single locus allele frequency estimates. ML estimates can be used in standard indices of genetic diversity and differentiation and in goodness-of-fit tests of genetic hypotheses. The noncentral χ(2) distribution is used to evaluate the power of a test of apparent heterozygote deficiency that results from attributing all variation to one locus when both loci are polymorphic.     

Candidate gene identification of existing or induced mutations with pipelines applicable to large genomes

Bulked segregant analysis (BSA) is used to identify existing or induced variants that are linked to phenotypes. Although it is widely used in Arabidopsis and rice, it remains challenging for crops with large genomes, such as maize. Moreover, analysis of huge data sets can present a bottleneck linking phenotypes to their molecular basis, especially for geneticists without programming experience. Here, we identified two genes of maize defective kernel mutants with newly developed analysis pipelines that require no programing skills and should be applicable to any large genome. In the 1970s, Neuffer and Sheridan generated a chemically induced defective kernel (dek) mutant collection with the potential to uncover critical genes for seed development. To locate such mutations, the dek phenotypes were introgressed into two inbred lines to take advantage of maize haplotype variations and their sequenced genomes. We generated two pipelines that take fastq files derived from next‐generation (nextGen) paired‐end DNA and cDNA sequencing as input, call on several well established and freely available genomic analysis tools to call SNPs and INDELs, and generate lists of the most likely causal mutations together with variant index plots to locate the mutation to a specific sequence position on a chromosome. The pipelines were validated with a known strawberry mutation before cloning the dek mutants, thereby enabling phenotypic analysis of large genomes by next‐generation sequencing.     

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle

Background

In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.

Methods

Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.

Results

Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.

Conclusions

Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0149-x) contains supplementary material, which is available to authorized users.     

Rethinking phenotypic plasticity and its consequences for individuals,populations and species

Much research has been devoted to identify the conditions under which selection favours flexible individuals or genotypes that are able to modify their growth, development and behaviour in response to environmental cues, to unravel the mechanisms of plasticity and to explore its influence on patterns of diversity among individuals, populations and species. The consequences of developmental plasticity and phenotypic flexibility for the performance and ecological success of populations and species have attracted a comparatively limited but currently growing interest. Here, I re-emphasize that an increased understanding of the roles of plasticity in these contexts requires a ‘whole organism'' (rather than ‘single trait'') approach, taking into consideration that organisms are integrated complex phenotypes. I further argue that plasticity and genetic polymorphism should be analysed and discussed within a common framework. I summarize predictions from theory on how phenotypic variation stemming from developmental plasticity and phenotypic flexibility may affect different aspects of population-level performance. I argue that it is important to distinguish between effects associated with greater interindividual phenotypic variation resulting from plasticity, and effects mediated by variation among individuals in the capacity to express plasticity and flexibility as such. Finally, I claim that rigorous testing of predictions requires methods that allow for quantifying and comparing whole organism plasticity, as well as the ability to experimentally manipulate the level of and capacity for developmental plasticity and phenotypic flexibility independent of genetic variation.     

On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene''s test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women''s Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.     

Methodological Considerations in Estimation of Phenotype Heritability Using Genome-Wide SNP Data,Illustrated by an Analysis of the Heritability of Height in a Large Sample of African Ancestry Adults

Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R² as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today''s GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today''s GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.     

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r² value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.     

Cultural and biological evolutionary processes, selection for a trait under complex transmission.

We consider the evolution of a trait, which is under both genetic and phenotypic transmission. An individual is always born in one state but can be converted to the other before reaching adulthood. If the conversion takes place by a learning process, the native state is called “unskilled,” and that acquired by learning is called “skilled.” If phenotypic conversion takes place by way of infection, the native state is uninfected, and can be converted to infected. Native and converted phenotypes may be subject to selection; acquiring a skill may lead to selective advantage of skilled versus unskilled, while contracting a disease may involve a selective disadvantage. Conversion probability is a function of the parental phenotypes. In some of our models we assume that only one parent has teaching ability (or transmits the disease) and in others we consider more general situations. The probability of learning (or of taking the disease) may be determined by the individual's genotype. A diallelic locus is considered. The evolution of the genotypes and the phenotypes is studied in a variety of situations. Equilibria, and in a few simple cases the dynamics of the phenotypes and genotypes in the population are given. The usual equilibrium for heterozygote advantage is found to depend, in the present case, on the parameters of the learning process. Oscillatory equilibria and more than one stable equilibrium can exist in certain circumstances. Even in the absence of genotypic differences for the conversion probability gene frequencies may change.     

Introgressive hybridization and morphological transgression in the contact zone between two Mediterranean Solea species

Hybrid zones provide natural experiments where new combinations of genotypes and phenotypes are produced. Studying the reshuffling of genotypes and remodeling of phenotypes in these zones is of particular interest to document the building of reproductive isolation and the possible emergence of transgressive phenotypes that can be a source of evolutionary novelties. Here, we specifically investigate the morphological variation patterns associated with introgressive hybridization between two species of sole, Solea senegalensis and Solea aegyptiaca. The relationship between genetic composition at nuclear loci and individual body shape variation was studied in four populations sampled across the hybrid zone located in northern Tunisia. A strong correlation between genetic and phenotypic variation was observed among all individuals but not within populations, including the two most admixed ones. Morphological convergence between parental species was observed close to the contact zone. Nevertheless, the samples taken closest to the hybrid zone also displayed deviant segregation of genotypes and phenotypes, as well as transgressive phenotypes. In these samples, deviant body shape variation could be partly attributed to a reduced condition index, and the distorted genetic composition was most likely due to missing allelic combinations. These results were interpreted as an indication of hybrid breakdown, which likely contributes to postmating reproductive isolation between the two species.