Pleiotropic effects of a chromosome 3 locus on speech-sound disorder and reading

Speech-sound disorder (SSD) is a complex behavioral disorder characterized by speech-sound production errors associated with deficits in articulation, phonological processes, and cognitive linguistic processes. SSD is prevalent in childhood and is comorbid with disorders of language, spelling, and reading disability, or dyslexia. Previous research suggests that developmental problems in domains associated with speech and language acquisition place a child at risk for dyslexia. Recent genetic studies have identified several candidate regions for dyslexia, including one on chromosome 3 segregating in a large Finnish pedigree. To explore common genetic influences on SSD and reading, we examined linkage for several quantitative traits to markers in the pericentrometric region of chromosome 3 in 77 families ascertained through a child with SSD. The quantitative scores measured several processes underlying speech-sound production, including phonological memory, phonological representation, articulation, receptive and expressive vocabulary, and reading decoding and comprehension skills. Model-free linkage analysis was followed by identification of sib pairs with linkage and construction of core shared haplotypes. In our multipoint analyses, measures of phonological memory demonstrated the strongest linkage (marker D3S2465, P=5.6 x 10(-5), and marker D3S3716, P=6.8 x 10(-4)). Tests for single-word decoding also demonstrated linkage (real word reading: marker D3S2465, P=.004; nonsense word reading: marker D3S1595, P=.005). The minimum shared haplotype in sib pairs with similar trait values spans 4.9 cM and is bounded by markers D3S3049 and D3S3045. Our results suggest that domains common to SSD and dyslexia are pleiotropically influenced by a putative quantitative trait locus on chromosome 3.     

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF,OMIM and PubMed records

Background

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

Results

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

Conclusion

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.     

The interface between genetics and psychology: lessons from developmental dyslexia

Developmental dyslexia runs in families, and twin studies have confirmed that there is a substantial genetic contribution to poor reading. The way in which discoveries in molecular genetics are reported can be misleading, encouraging us to think that there are specific genes that might be used to screen for disorder. However, dyslexia is not a classic Mendelian disorder that is caused by a mutation in a single gene. Rather, like many other common disorders, it appears to involve combined effects of many genes and environmental factors, each of which has a small influence, possibly supplemented by rare variants that have larger effects but apply to only a minority of cases. Furthermore, to see clearer relationships between genotype and phenotype, we may need to move beyond the clinical category of dyslexia to look at underlying cognitive deficits that may be implicated in other neurodevelopmental disorders.     

Further evidence of pleiotropy influencing speech and language: analysis of the DYX8 region

BACKGROUND/AIMS: Genetic studies have raised the possibility of common bases for cognitive linguistic disorders such as speech sound disorder (SSD), reading disorder (RD) and language impairment (LI). Thus, some of the same genes may jointly influence cognitive components within and between these three disorders. We examined the plausibility of this theory in a sample of families ascertained on the basis of a child with SSD. METHODS: Using the method of generalized estimating equations to solve a bivariate family predictive model we obtained measures of comorbidity and familial aggregation of SSD and LI. We then used two methods of multipoint model-free linkage analysis to evaluate SSD and LI psychometric test measures over a region previously implicated in linkage studies of RD, DYX8 region, 1p34-p36. RESULTS: Bivariate phenotypic analyses show evidence of comorbidity and within family aggregation and coaggregation of SSD and LI. In addition, two regions on chromosome 1 show suggestive evidence of linkage. The first region was previously reported in dyslexia studies. Our maximum linkage signal in this region measured articulation (p = 0.0009) in SSD sibling pairs. The second region is characterized by processes involved in language production, with the maximum linkage signal measuring listening comprehension (p = 0.0019) using all sibling pairs. CONCLUSION: We conclude that the DYX8 region could bear genes controlling pleiotropic effects on SSD, LI and RD.     

Quantitative analysis of ventral denticular patterns of Drosophila melanogaster larvae and the regulation of the bithorax complex

A quantitative model of the effect of the bithorax complex on segmentation is presented which could explain the known data of the spatiotemporal regulation of key gene complex during early Drosophila development, in relation to their effects on some of the segmentation landmarks. The model tries to put together the two different genetic levels, the genotypic and the phenotypic. At the genotypic level, a minimal cross-regulatory network of the different genes involved, Antp, Ubx, abd-A and Abd-B which explains the reported levels of expressions of these genes. At the phenotypic level, the pattern of the ventral denticle belts across the larva which are characteristics of the different segments have been compared by calculating a value of the degree of similarity in the case of the wild-type and several mutant combinations. Finally the two parts of the model are combined, showing that a satisfactory agreement between the two can be achieved. Therefore, this work is a first attempt to develop a method which will provide an explanatory solution of the old question in morphogenesis of how the phenotype is directed by the genotype of a cell or organism.     

Developmental dyslexia--recurrence risk estimates from a german bi-center study using the single proband sib pair design

OBJECTIVE: Several studies have demonstrated a genetic component for dyslexia. However, both segregation and linkage analyses show contradictory results pointing at the necessity of an optimal ascertainment scheme for molecular genetic studies. Previously, we have argued that the single proband sib pair design (SPSP) would be optimal. The aims of this paper therefore are to demonstrate the practicability of the SPSP design and the estimation of recurrence risks for reading and writing. METHODS: We assessed spelling and reading in a family sample ascertained through the SPSP design. 287 families with at least two siblings and their parents were recruited. At least one child was affected with spelling disorder according to a one standard deviation (1SD) discrepancy criterion. RESULTS: Mean values for probands and their siblings were different for both the spelling and the reading phenotype. For the probands, variances of the phenotype spelling were smaller. These effects became stronger with more extreme selection criteria. Both siblings fulfilled the 1SD criterion for spelling and reading in 60.3 and 28.9% of the families, respectively, indicating a low cost efficiency of the double proband sib pair approach. A recurrence risk of 4.52 (CI: 4.07-4.93) was obtained for spelling when the 1SD criterion was applied to both siblings. Recurrence risk estimates were similar for reading. CONCLUSION: The study demonstrates the suitability of the SPSP design for genetic analysis of dyslexia. The recurrence risk estimates may be used for determining sample sizes in gene mapping studies.     

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.     

Population, quantitative and comparative genomics of adaptation in forest trees

High-throughput DNA sequencing and genotyping technologies have enabled a new generation of research in plant genetics where combined quantitative and population genetic approaches can be used to better understand the relationship between naturally occurring genotypic and phenotypic diversity. Forest trees are highly amenable to such studies because of their combined undomesticated and partially domesticated state. Forest geneticists are using association genetics to dissect complex adaptive traits and discover the underlying genes. In parallel, they are using resequencing of candidate genes and modern population genetics methods to discover genes under natural selection. This combined approach is identifying the most important genes that determine patterns of complex trait adaptation observed in many tree populations.     

Evidence for the late MMN as a neurophysiological endophenotype for dyslexia

Dyslexia affects 5-10% of school-aged children and is therefore one of the most common learning disorders. Research on auditory event related potentials (AERP), particularly the mismatch negativity (MMN) component, has revealed anomalies in individuals with dyslexia to speech stimuli. Furthermore, candidate genes for this disorder were found through molecular genetic studies. A current challenge for dyslexia research is to understand the interaction between molecular genetics and brain function, and to promote the identification of relevant endophenotypes for dyslexia. The present study examines MMN, a neurophysiological correlate of speech perception, and its potential as an endophenotype for dyslexia in three groups of children. The first group of children was clinically diagnosed with dyslexia, whereas the second group of children was comprised of their siblings who had average reading and spelling skills and were therefore "unaffected" despite having a genetic risk for dyslexia. The third group consisted of control children who were not related to the other groups and were also unaffected. In total, 225 children were included in the study. All children showed clear MMN activity to/da/-/ba/contrasts that could be separated into three distinct MMN components. Whilst the first two MMN components did not differentiate the groups, the late MMN component (300-700 ms) revealed significant group differences. The mean area of the late MMN was attenuated in both the dyslexic children and their unaffected siblings in comparison to the control children. This finding is indicative of analogous alterations of neurophysiological processes in children with dyslexia and those with a genetic risk for dyslexia, without a manifestation of the disorder. The present results therefore further suggest that the late MMN might be a potential endophenotype for dyslexia.     

Association genetics in Pinus taeda L. II. Carbon isotope discrimination

Dissection of complex traits that influence fitness is not only a central topic in evolutionary research but can also assist breeding practices for economically important plant species, such as loblolly pine (Pinus taeda L). In this study, 46 single nucleotide polymorphisms (SNPs) from 41 disease and abiotic stress-inducible genes were tested for their genetic association with carbon isotope discrimination (CID), a time-integrated trait measure of stomatal conductance. A family-based approach to detect genotype/phenotype genetic association was developed for the first time in plants by applying the quantitative transmission disequilibrium test on an association population of 961 clones from 61 families (adopted from previous breeding programs) evaluated for phenotypic expression of CID at two sites. Two particularly promising candidates for their genetic effects on CID are: dhn-1, involved in stabilization of cell structures, and lp5-like, a glycine rich protein putatively related to cell wall reinforcement proteins, both of which were shown in previous studies to be water-deficit inducible. Moreover, association in lp5-like involves a nonsynonymous mutation in linkage disequilibrium with two other nonsynonymous polymorphisms that could, by acting together, enhance overall phenotypic effects. This study highlights the complexity of dissecting CID traits and provides insights for designing second-generation association studies based on candidate gene approaches in forest trees.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Connectionist neuropsychology: uncovering ultimate causes of acquired dyslexia

Acquired dyslexia offers a unique window on to the nature of the cognitive and neural architecture supporting skilled reading. This paper provides an integrative overview of recent empirical and computational work on acquired dyslexia within the context of the primary systems framework as implemented in connectionist neuropsychological models. This view proposes that damage to general visual, phonological or semantic processing abilities are the root causes of different forms of acquired dyslexia. Recent case-series behavioural evidence concerning pure alexia, phonological dyslexia and surface dyslexia that supports this perspective is presented. Lesion simulations of these findings within connectionist models of reading demonstrate the viability of this approach. The commitment of such models to learnt representations allows them to capture key aspects of performance in each type of acquired dyslexia, particularly the associated non-reading deficits, the role of relearning and the influence of individual differences in the premorbid state of the reading system. Identification of these factors not only advances our understanding of acquired dyslexia and the mechanisms of normal reading but they are also relevant to the complex interactions underpinning developmental reading disorders.     

Phenotypic evolution from genetic polymorphisms in a radial network architecture

Background  

The genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support.     

Genetic and molecular dissection of naturally occurring variation

Recent progress in plant genome analysis has made it possible to examine the naturally occurring allelic variation underlying complex traits. Many studies have described the genetic mapping of quantitative trait loci for several kinds of complex phenotypic traits. Some researchers have taken up the challenge of performing the molecular cloning of genes at these loci, and examples of cloning have recently been reported. Naturally occurring allelic variation could be a new resource for the functional analysis of plant genes.     

Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders

Identifying genes involved in complex neuropsychiatric disorders through classic human genetic approaches has proven difficult. To overcome that barrier, we have developed a translational approach called Convergent Functional Genomics (CFG), which cross-matches animal model microarray gene expression data with human genetic linkage data as well as human postmortem brain data and biological role data, as a Bayesian way of cross-validating findings and reducing uncertainty. Our approach produces a short list of high probability candidate genes out of the hundreds of genes changed in microarray datasets and the hundreds of genes present in a linkage peak chromosomal area. These genes can then be prioritized, pursued, and validated in an individual fashion using: (1) human candidate gene association studies and (2) cell culture and mouse transgenic models. Further bioinformatics analysis of groups of genes identified through CFG leads to insights into pathways and mechanisms that may be involved in the pathophysiology of the illness studied. This simple but powerful approach is likely generalizable to other complex, non-neuropsychiatric disorders, for which good animal models, as well as good human genetic linkage datasets and human target tissue gene expression datasets exist.     

Segregation analysis of phenotypic components of learning disabilities. I. Nonword memory and digit span

Dyslexia is a common and complex disorder with evidence for a genetic component. Multiple loci (i.e., quantitative-trait loci [QTLs]) are likely to be involved, but the number is unknown. Diagnosis is complicated by the lack of a standard protocol, and many diagnostic measures have been proposed as understanding of the component processes has evolved. One or more genes may, in turn, influence these measures. To date, little work has been done to evaluate the mode of inheritance of individual component-as opposed to composite-phenotypes, beyond family or twin correlation studies that initially demonstrate evidence for a genetic basis of such components. Here we use two approaches to segregation analysis in 102 nuclear families to estimate genetic models for component phenotypes associated with dyslexia: digit span and a nonword-repetition task. Both measures are related to phonological skills, one of the key component processes in dyslexia. We use oligogenic-trait segregation analysis to estimate the number of QTLs contributing to each phenotype, and we use complex segregation analysis to identify the most parsimonious inheritance models. We provide evidence in support of both a major-gene mode of inheritance for the nonword-repetition task, with approximately 2.4 contributing QTLs, and for a genetic basis of digit span, with approximately 1.9 contributing QTLs. Results obtained by reciprocal adjustment of measures suggest that genes contributing to digit span may contribute to the nonword-repetition score but that there are additional QTLs involved in nonword repetition. Our study adds to existing studies of the genetic basis of composite phenotypes related to dyslexia, by providing evidence for major-gene modes of inheritance of these single-measure component phenotypes.     

Caenorhabditis elegans as a model for lysosomal storage disorders

The nematode Caenorhabditis elegans is the simplest animal model available to study human disease. In this review, the worm homologues for the 58 human genes involved in lysosomal storage disorders and for 105 human genes associated with lysosomal function have been compiled. Most human genes had at least one worm homologue. In addition, the phenotypes of 147 mutants, in which these genes have been disrupted or knocked down, have been summarized and discussed. The phenotypic spectrum of worm models of lysosomal storage disorders varies from lethality to none obvious, with a large variety of intermediate phenotypes. The genetic power of C. elegans provides a means to identify genes involved in specific processes with relative ease. The overview of potential lysosomal phenotypes presented here might be used as a starting point for the phenotypic characterization of newly developed knock-out models or for the design of genetic screens selecting for loss or gain of suitable knock-out model phenotypes. Screens for genes involved in lysosomal biogenesis and function have been performed successfully resulting in the cup and glo mutants, but screens involving subtle phenotypes are likely to be difficult.     

From genotype to phenotype: genetics and medical practice in the new millennium

The completion of the human genome project will provide a vast amount of information about human genetic diversity. One of the major challenges for the medical sciences will be to relate genotype to phenotype. Over recent years considerable progress has been made in relating the molecular pathology of monogenic diseases to the associated clinical phenotypes. Studies of the inherited disorders of haemoglobin, notably the thalassaemias, have shown how even in these, the simplest of monogenic diseases, there is remarkable complexity with respect to their phenotypic expression. Although studies of other monogenic diseases are less far advanced, it is clear that the same level of complexity will exist. This information provides some indication of the difficulties that will be met when trying to define the genes that are involved in common multigenic disorders and, in particular, in trying to relate disease phenotypes to the complex interactions between many genes and multiple environmental factors.     

Quantitative trait locus mapping for atherosclerosis susceptibility

PURPOSE OF REVIEW: Atherosclerosis is a complex trait with both environmental and genetic aspects. Although some progress has been made in defining genes associated with atherosclerosis in humans, animal models have been useful in learning about pathways and genes involved in atherogenesis. This review describes an unbiased genetic mapping method called quantitative trait locus mapping and progress in using this method to identify genes that alter atherosclerosis susceptibility in mice. RECENT FINDINGS: Approximately 10 well defined genetic loci have been described that are associated with lesion severity in diet-induced or gene knockout mouse models of atherosclerosis. Recently, two of these genetic loci were narrowed considerably by analysis of genetic recombinants within these loci. In addition, a computational method to discover quantitative trait loci has been applied to atherosclerosis. However, none of the genes responsible for these atherosclerosis quantitative trait loci has been definitively identified. The recent completion of the mouse draft genome should facilitate the task of identifying these genes. SUMMARY: Quantitative trait locus mapping studies in mouse models of atherosclerosis have defined genetic regions that alter lesion severity. The identification of the responsible genes may lead to insights into the pathogenesis of atherosclerosis as well as to candidates for human genetic association studies.