An extensive comparison of quantitative trait Loci mapping methods.

BACKGROUND: The choices of study design and statistical approach for mapping a quantitative trait (QT) are of great importance. Larger sibships and a study design based upon phenotypically extreme siblings can be expected to have a greater statistical power. On the other hand, selected samples and/or deviation from normality can influence the robustness and power. Unfortunately, the effects of violation of multivariate normality assumptions and/or selected samples are only known for a limited number of methods. Some recommendations are available in the literature, but an extensive comparison of robustness and power under several different conditions is lacking. METHODS: We compared eight freely available and commonly applied QT mapping methods in a Monte-Carlo simulation study under 36 different models and study designs (three genetic models, three selection schemes, two family structures and the possible effect of deviation from normality). RESULTS: Empirical type I error fractions and empirical power are presented and explained as a whole and for each method separately, followed by a thorough discussion. CONCLUSIONS: The results from this extensive comparison could serve as a valuable source for the choice of the study design and the statistical approach for mapping a QT.

Evidence for linkage of restless legs syndrome to chromosome 9p: are there two distinct loci?

BACKGROUND: Restless legs syndrome (RLS) is a common sensory-motor disorder characterized by paresthesias and an intense urge to move the legs with a considerable familial aggregation. To date, no gene mutation has been found, but five gene loci have been mapped in primary RLS to chromosomes 12q, 14q, 9p, 2q, and 20p (RLS1 through 5). PATIENTS/METHODS: We identified a four-generational German RLS family with 37 family members including 15 affected cases. We performed linkage analysis using microsatellite markers at the five known loci. Prompted by the identification of a potentially shared haplotype near the RLS3 locus, we expanded the investigated linkage region on chromosome 9p using additional DNA markers. RESULTS: Mode of inheritance in our RLS family was compatible with an autosomal dominant pattern, and disease onset was mainly in childhood or adolescence. We excluded linkage to the RLS1, RLS2, RLS4, and RLS5 loci. However, we identified a likely new RLS gene locus (RLS3*) on chromosome 9p with a maximum lod score of 3.60 generated by model-based multipoint linkage analysis. A haplotype flanked by D9S974 and D9S1118 in a 9.9-Mb region, centromeric to RLS3, was shared by all 12 investigated patients. In addition, 11 of them carried a common haplotype extending telomeric to D9S2189 that is located within RLS3. CONCLUSIONS: We demonstrate linkage to a locus on chromosome 9p that is probably distinct from RLS3. Our family with a rather homogeneous phenotype and very early disease onset represents a unique opportunity to further elucidate the genetic causes of the frequent restless leg syndrome.

Interrelationship and familiality of dyslexia related quantitative measures.

Dyslexia is a complex gene-environment disorder with poorly understood etiology that affects about 5% of school-age children. Dyslexia occurs in all languages and is associated with a high level of social and psychological morbidity for the individual and their family; approximately 40-50% have persistent disability into adulthood. The core symptoms are word reading and spelling deficits, but several other cognitive components influence the core phenotype. A broad spectrum of dyslexia related phenotypes, including phonological decoding, phoneme awareness, orthographic processing, short-term memory, rapid naming and basic mathematical abilities, were investigated in large sample of 287 German dyslexia families. We explored the interrelationship between the component phenotypes using correlation and principal component analyses (PCA). In addition, we estimated familiality for phenotypes as well as for the factors suggested by PCA. The correlation between the component phenotypes varied between -0.1 and 0.7. The PCA resulted in three factors: a general dyslexia factor, a speed of processing factor and a mathematical abilities factor. The familiality estimates of single components and factors ranged between 0.25 and 0.63. Instead of analyzing single dyslexia-related components, multivariate analyses including factor analytic approaches may help in the identification of susceptibility genes.

Linkage analyses of chromosomal region 18p11-q12 in dyslexia.

Dyslexia is characterized as a significant impairment in reading and spelling ability that cannot be explained by low intelligence, low school attendance or deficits in sensory acuity. It is known to be a hereditary disorder that affects about 5% of school aged children, making it the most common of childhood learning disorders. Several susceptibility loci have been reported on chromosomes 1, 2, 3, 6, 15, and 18. The locus on chromosome 18 has been described as having the strongest influence on single word reading, phoneme awareness, and orthographic coding in the largest genome wide linkage study published to date (Fisher et al., 2002). Here we present data from 82 German families in order to investigate linkage of various dyslexia-related traits to the previously described region on chromosome 18p11-q12. Using two- and multipoint analyses, we did not find support for linkage of spelling, single word reading, phoneme awareness, orthographic coding and rapid naming to any of the 14 genotyped STR markers. Possible explanations for our non-replication include differences in study design, limited power of our study and overestimation of the effect of the chromosome 18 locus in the original study.