Investigation of quantitative measures related to reading disability in a large sample of sib-pairs from the UK.

We describe a family-based sample of individuals with reading disability collected as part of a quantitative trait loci (QTL) mapping study. Eighty-nine nuclear families (135 independent sib-pairs) were identified through a single proband using a traditional discrepancy score of predicted/actual reading ability and a known family history. Eight correlated psychometric measures were administered to each sibling, including single word reading, spelling, similarities, matrices, spoonerisms, nonword and irregular word reading, and a pseudohomophone test. Summary statistics for each measure showed a reduced mean for the probands compared to the co-sibs, which in turn was lower than that of the population. This partial co-sib regression back to the mean indicates that the measures are influenced by familial factors and therefore, may be suitable for a mapping study. The variance of each of the measures remained largely unaffected, which is reassuring for the application of a QTL approach. Multivariate genetic analysis carried out to explore the relationship between the measures identified a common factor between the reading measures that accounted for 54% of the variance. Finally the familiality estimates (range 0.32-0.73) obtained for the reading measures including the common factor (0.68) supported their heritability. These findings demonstrate the viability of this sample for QTL mapping, and will assist in the interpretation of any subsequent linkage findings in an ongoing genome scan.

A sibling-pair based approach for mapping genetic loci that influence quantitative measures of reading disability.

Family and twin studies consistently demonstrate a significant role for genetic factors in the aetiology of the reading disorder dyslexia. However, dyslexia is complex at both the genetic and phenotypic levels, and currently the nature of the core deficit or deficits remains uncertain. Traditional approaches for mapping disease genes, originally developed for single-gene disorders, have limited success when there is not a simple relationship between genotype and phenotype. Recent advances in high-throughput genotyping technology and quantitative statistical methods have made a new approach to identifying genes involved in complex disorders possible. The method involves assessing the genetic similarity of many sibling pairs along the lengths of all their chromosomes and attempting to correlate this similarity with that of their phenotypic scores. We are adopting this approach in an ongoing genome-wide search for genes involved in dyslexia susceptibility, and have already successfully applied the method by replicating results from previous studies suggesting that a quantitative trait locus at 6p21.3 influences reading disability.

A quantitative-trait locus on chromosome 6p influences different aspects of developmental dyslexia.

Recent application of nonparametric-linkage analysis to reading disability has implicated a putative quantitative-trait locus (QTL) on the short arm of chromosome 6. In the present study, we use QTL methods to evaluate linkage to the 6p25-21.3 region in a sample of 181 sib pairs from 82 nuclear families that were selected on the basis of a dyslexic proband. We have assessed linkage directly for several quantitative measures that should correlate with different components of the phenotype, rather than using a single composite measure or employing categorical definitions of subtypes. Our measures include the traditional IQ/reading discrepancy score, as well as tests of word recognition, irregular-word reading, and nonword reading. Pointwise analysis by means of sib-pair trait differences suggests the presence, in 6p21.3, of a QTL influencing multiple components of dyslexia, in particular the reading of irregular words (P=.0016) and nonwords (P=.0024). A complementary statistical approach involving estimation of variance components supports these findings (irregular words, P=.007; nonwords, P=.0004). Multipoint analyses place the QTL within the D6S422-D6S291 interval, with a peak around markers D6S276 and D6S105 consistently identified by approaches based on trait differences (irregular words, P=.00035; nonwords, P=.0035) and variance components (irregular words, P=.007; nonwords, P=.0038). Our findings indicate that the QTL affects both phonological and orthographic skills and is not specific to phoneme awareness, as has been previously suggested. Further studies will be necessary to obtain a more precise localization of this QTL, which may lead to the isolation of one of the genes involved in developmental dyslexia.

Molecular genetic investigations of autism.

Genetic factors are likely to play a major role in the etiology of autism. The genetics of the disorder is however complex, probably involving the action of several genes. In an attempt to identify autism susceptibility loci we are currently undertaking a systematic screening of the whole human genome using multiplex families. We describe the resources and the methods needed to achieve such a task, including extensive collection of family data, semiautomated genotyping technology, and specialized statistical approaches for linkage analysis of complex traits.

Multipoint analysis of quantitative traits.

Complete multipoint analysis as implemented by MAPMAKER/SIBS was used to identify the chromosomal regions with the strongest evidence of linkage. Three different quantitative sib-pair methods were compared. For each trait the maximum lod/Z score was used to implicate a chromosomal region. For trait Q5, chromosome 5, the analysis was repeated in a second data set to test for replication of the result from the first data set. The initial screen was carried out using complete parental information and the first sibling pair from each nuclear family. The effect of removing the parental genotypes and the use of multiple independent sibs was examined in the second data set for Q5, chromosome 5.

Prediction of genetic risks from segregation analyses of morbid risks.

Segregation analysis provides a genetic model of disease based upon morbid risks in age-specific classes, from which the model derives genotype-specific morbid risks. In the absence of disease-specific mortality each morbid risk is a cumulative incidence (age-specific penetrance). If disease-specific mortality reduces the number of affected in the older classes, the morbid risk is less than the corresponding penetrance. When counselling individuals with a family history of disease, their genetic risk is a function of penetrances, which can be calculated from morbid risks. We have applied this method to breast, ovarian and colorectal cancer.