Systematic inference of copy-number genotypes from personal genome sequencing data reveals extensive olfactory receptor gene content diversity

Copy-number variations (CNVs) are widespread in the human genome, but comprehensive assignments of integer locus copy-numbers (i.e., copy-number genotypes) that, for example, enable discrimination of homozygous from heterozygous CNVs, have remained challenging. Here we present CopySeq, a novel computational approach with an underlying statistical framework that analyzes the depth-of-coverage of high-throughput DNA sequencing reads, and can incorporate paired-end and breakpoint junction analysis based CNV-analysis approaches, to infer locus copy-number genotypes. We benchmarked CopySeq by genotyping 500 chromosome 1 CNV regions in 150 personal genomes sequenced at low-coverage. The assessed copy-number genotypes were highly concordant with our performed qPCR experiments (Pearson correlation coefficient 0.94), and with the published results of two microarray platforms (95-99% concordance). We further demonstrated the utility of CopySeq for analyzing gene regions enriched for segmental duplications by comprehensively inferring copy-number genotypes in the CNV-enriched >800 olfactory receptor (OR) human gene and pseudogene loci. CopySeq revealed that OR loci display an extensive range of locus copy-numbers across individuals, with zero to two copies in some OR loci, and two to nine copies in others. Among genetic variants affecting OR loci we identified deleterious variants including CNVs and SNPs affecting ~15% and ~20% of the human OR gene repertoire, respectively, implying that genetic variants with a possible impact on smell perception are widespread. Finally, we found that for several OR loci the reference genome appears to represent a minor-frequency variant, implying a necessary revision of the OR repertoire for future functional studies. CopySeq can ascertain genomic structural variation in specific gene families as well as at a genome-wide scale, where it may enable the quantitative evaluation of CNVs in genome-wide association studies involving high-throughput sequencing.     

Copy-Number Mutations on Chromosome 17q24.2-q24.3 in Congenital Generalized Hypertrichosis Terminalis with or without Gingival Hyperplasia

Congenital generalized hypertrichosis terminalis (CGHT) is a rare condition characterized by universal excessive growth of pigmented terminal hairs and often accompanied with gingival hyperplasia. In the present study, we describe three Han Chinese families with autosomal-dominant CGHT and a sporadic case with extreme CGHT and gingival hyperplasia. We first did a genome-wide linkage scan in a large four-generation family. Our parametric multipoint linkage analysis revealed a genetic locus for CGHT on chromosome 17q24.2-q24.3. Further two-point linkage and haplotyping with microsatellite markers from the same chromosome region confirmed the genetic mapping and showed in all the families a microdeletion within the critical region that was present in all affected individuals but not in unaffected family members. We then carried out copy-number analysis with the Affymetrix Genome-Wide Human SNP Array 6.0 and detected genomic microdeletions of different sizes and with different breakpoints in the three families. We validated these microdeletions by real-time quantitative PCR and confirmed their perfect cosegregation with the disease phenotype in the three families. In the sporadic case, however, we found a de novo microduplication. Two-color interphase FISH analysis demonstrated that the duplication was inverted. These copy-number variations (CNVs) shared a common genomic region in which CNV is not reported in the public database and was not detected in our 434 unrelated Han Chinese normal controls. Thus, pathogenic copy-number mutations on 17q24.2-q24.3 are responsible for CGHT with or without gingival hyperplasia. Our work identifies CGHT as a genomic disorder.     

Detecting maternal-effect loci by statistical cross-fostering

Great progress has been made in understanding the genetic architecture of phenotypic variation, but it is almost entirely focused on how the genotype of an individual affects the phenotype of that same individual. However, in many species the genotype of the mother is a major determinant of the phenotype of her offspring. Therefore, a complete picture of genetic architecture must include these maternal genetic effects, but they can be difficult to identify because maternal and offspring genotypes are correlated and therefore, partially confounded. We present a conceptual framework that overcomes this challenge to separate direct and maternal effects in intact families through an analysis that we call "statistical cross-fostering." Our approach combines genotype data from mothers and their offspring to remove the confounding effects of the offspring's own genotype on measures of maternal genetic effects. We formalize our approach in an orthogonal model and apply this model to an experimental population of mice. We identify a set of six maternal genetic effect loci that explain a substantial portion of variation in body size at all ages. This variation would be missed in an approach focused solely on direct genetic effects, but is clearly a major component of genetic architecture. Our approach can easily be adapted to examine maternal effects in different systems, and because it does not require experimental manipulation, it provides a framework that can be used to understand the contribution of maternal genetic effects in both natural and experimental populations.     

Gene-Network Analysis Identifies Susceptibility Genes Related to Glycobiology in Autism

The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD), and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.     

From phenotype to genotype: a Bayesian solution

The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.     

Dosage-Dependent Severity of the Phenotype in Patients with Mental Retardation Due to a Recurrent Copy-Number Gain at Xq28 Mediated by an Unusual Recombination

We report on the identification of a 0.3 Mb inherited recurrent but variable copy-number gain at Xq28 in affected males of four unrelated families with X-linked mental retardation (MR). All aberrations segregate with the disease in the families, and the carrier mothers show nonrandom X chromosome inactivation. Tiling Xq28-region-specific oligo array revealed that all aberrations start at the beginning of the low copy repeat LCR-K1, at position 153.20 Mb, and end just distal to LCR-L2, at 153.54 Mb. The copy-number gain always includes 18 annotated genes, of which RPL10, ATP6AP1 and GDI1 are highly expressed in brain. From these, GDI1 is the most likely candidate gene. Its copy number correlates with the severity of clinical features, because it is duplicated in one family with nonsyndromic moderate MR, is triplicated in males from two families with mild MR and additional features, and is present in five copies in a fourth family with a severe syndromic form of MR. Moreover, expression analysis revealed copy-number-dependent increased mRNA levels in affected patients compared to control individuals. Interestingly, analysis of the breakpoint regions suggests a recombination mechanism that involves two adjacent but different sets of low copy repeats. Taken together, our data strongly suggest that an increased expression of GDI1 results in impaired cognition in a dosage-dependent manner. Moreover, these data also imply that a copy-number gain of an individual gene present in the larger genomic aberration that leads to the severe MECP2 duplication syndrome can of itself result in a clinical phenotype as well.     

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth–based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth–based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.     

Bayesian modeling for genetic anticipation in presence of mutational heterogeneity: a case study in Lynch syndrome

Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation subtypes known to be associated with the disease phenotype. In this article, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect inferential conclusions. We compare the fit of four-candidate random effects distributions via Bayesian model fit diagnostics. A related statistical issue here is isolating the confounding effect of changes in secular trends, screening, and medical practices that may affect time to disease detection across birth cohorts. Using historic cancer registry data, we borrow from relative survival analysis methods to adjust for changes in age-specific incidence across birth cohorts. Our motivating case study comes from a Danish cancer register of 124 families with mutations in mismatch repair (MMR) genes known to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family-level anticipation effects that are potentially useful in genetic counseling clinics for high-risk families.     

Copy-number fluctuation by unequal crossing-over in the chicken avidin gene family

The chicken avidin gene (AVD) forms a closely clustered gene family together with several avidin-related genes (AVRs). In this study, we used fluorescence in situ hybridization on extended DNA fibers (fiber-FISH) to show that the number of the AVD and AVR genes differs between individuals. Furthermore, the gene copy-number showed wide somatic variation in white blood cells of the individuals. The molecular mechanism underlying the fluctuation is most probably unequal crossing-over and/or unequal sister chromatid exchange, as judged by the Gaussian distribution of the gene counts. By definition, an increase in gene number on one locus should be accompanied by a decrease on the other locus in unequal sequence exchange. The results suggest that copy-number lability may be more common among gene families than previously thought. The chicken avidin gene family also provides an excellent model for studying the mechanisms of recombination and gene conversion.     

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (> 12-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.     

Overexpression of CNP in chondrocytes rescues achondroplasia through a MAPK-dependent pathway

Achondroplasia is the most common genetic form of human dwarfism, for which there is presently no effective therapy. C-type natriuretic peptide (CNP) is a newly identified molecule that regulates endochondral bone growth through GC-B, a subtype of particulate guanylyl cyclase. Here we show that targeted overexpression of CNP in chondrocytes counteracts dwarfism in a mouse model of achondroplasia with activated fibroblast growth factor receptor 3 (FGFR-3) in the cartilage. CNP prevented the shortening of achondroplastic bones by correcting the decreased extracellular matrix synthesis in the growth plate through inhibition of the MAPK pathway of FGF signaling. CNP had no effect on the STAT-1 pathway of FGF signaling that mediates the decreased proliferation and the delayed differentiation of achondroplastic chondrocytes. These results demonstrate that activation of the CNP-GC-B system in endochondral bone formation constitutes a new therapeutic strategy for human achondroplasia.     

Inference of kinship coefficients from Korean SNP genotyping data

The determination of relatedness between individuals in a family is crucial in analysis of common complex diseases. We present a method to infer close inter-familial relationships based on SNP genotyping data and provide the relationship coefficient of kinship in Korean families. We obtained blood samples from 43 Korean individuals in two families. SNP data was obtained using the Affymetrix Genome-wide Human SNP array 6.0 and the Illumina Human 1M-Duo chip. To measure the kinship coefficient with the SNP genotyping data, we considered all possible pairs of individuals in each family. The genetic distance between two individuals in a pair was determined using the allele sharing distance method. The results show that genetic distance is proportional to the kinship coefficient and that a close degree of kinship can be confirmed with SNP genotyping data. This study represents the first attempt to identify the genetic distance between very closely related individuals. [BMB Reports 2013; 46(6): 305-309]     

An algorithm for inferring complex haplotypes in a region of copy-number variation

Recent studies have extensively examined the large-scale genetic variants in the human genome known as copy-number variations (CNVs), and the universality of CNVs in normal individuals, along with their functional importance, has been increasingly recognized. However, the absence of a method to accurately infer alleles or haplotypes within a CNV region from high-throughput experimental data hampers the finer analyses of CNV properties and applications to disease-association studies. Here we developed an algorithm to infer complex haplotypes within a CNV region by using data obtained from high-throughput experimental platforms. We applied this algorithm to experimental data and estimated the population frequencies of haplotypes that can yield information on both sequences and numbers of DNA copies. These results suggested that the analysis of such complex haplotypes is essential for accurately detecting genetic differences within a CNV region between population groups.     

The Genetic Basis of Mendelian Phenotypes: Discoveries,Challenges, and Opportunities

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying ∼50% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.     

Gene Duplication and Phenotypic Changes in the Evolution of Mammalian Metabolic Networks

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines.     

Phenotype Ontologies and Cross-Species Analysis for Translational Research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.     

Bayesian model to detect phenotype-specific genes for copy number data

ABSTRACT: BACKGROUND: An important question in genetic studies is to determine those genetic variants, in particular CNVs, that arespecific to different groups of individuals. This could help in elucidating differences in disease predispositionand response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copynumber variants (CNVs) where only few of them are expected to be associated with a specific phenotype. RESULTS: The model is illustrated by analyzing three major human groups belonging to HapMap data. We also show howthe model can be used to determine specific CNVs related to response to treatment in patients diagnosed withovarian cancer. The model is also extended to address the problem of how to adjust for confounding covariates(e.g., population stratification). Through a simulation study, we show that the proposed model outperforms otherapproaches that are typically used to analyze this data when analyzing common copy-number polymorphisms(CNPs) or complex CNVs. We have developed an R package, called bayesGen, that implements the model andestimating algorithms. CONCLUSIONS: Our proposed model is useful to discover specific genetic variants when different subgroups of individuals areanalyzed. The model can address studies with or without control group. By integrating all data in a unique modelwe can obtain a list of genes that are associated with a given phenotype as well as a different list of genes that areshared among the different subtypes of cases.     

Exploring the genetics of aging in a wild passerine bird

Senescence is the decline in survival and reproduction as an organism ages and is known to occur in collared flycatchers Ficedula albicollis. We consider annual fitness (the estimated genetic contribution that an individual makes to next year's gene pool) as a measure of age-specific fitness. We apply a restricted maximum likelihood linear mixed-model approach on 25 years of data on 3,844 male and 4,992 female collared flycatchers. Annual fitness had a significant additive genetic component (h2 of about 4%). Annual fitness declined at later ages in both sexes. Using a random regression animal model, we show that the observed age-related phenotypic changes in annual fitness were not present on the additive genetic level, contrary to predictions of genetic hypotheses of senescence. Our study suggests that patterns of aging in the wild need to be interpreted with caution in terms of underlying genetics because they may be largely determined by environmental processes.     

Genetic pattern of the recent recovery of European otters in southern France

We investigated how landscape affects the population genetic structure and the dispersal of the elusive European otter Lutra lutra in a contemporary colonization context, over several generations and at the level of hydrographic basins. Our study area included 10 basins located in the Cévennes National Park (CNP), at the southern front of the natural otter recovery in France. Each basin comprised 50 to 300 km of permanent rivers that were surveyed for otter presence from 1991 to 2005. Faecal samples collected in 2004 and 2005 in this area were genotyped at 9 microsatellite loci, resulting in the identification of 70 genetically distinct individuals. Bayesian clustering methods were used to infer genetic structure of the populations and to compare recent gene flow to the observed colonization. At the regional level, we identified 2 distinct genetic clusters (NE and SW; FST=0.102) partially separated by ridges, suggesting that the CNP was recolonized by 2 genetically distinct otter populations. At the basin level, the genetic distance between groups of individuals in different basins was positively correlated to the mean slope separating these basins. The probable origins and directions of individual movements (i.e. migration between clusters and basin colonization inside clusters) were inferred from assignment tests. This approach shows that steep and dry lands can stop, impede or divert the dispersal of a mobile carnivore such as the otter.