True and false positive peaks in genomewide scans: The long and the short of it

HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families

In the search for sequence variants underlying disease, commonly applied filtering steps usually result in a number of candidate variants that cannot further be narrowed down. In autosomal recessive families, disease usually occurs only in one generation so that genetic linkage analysis is unlikely to help. Because homozygous recessive mutations tend to be inherited together with flanking homozygous variants, we developed a statistical method to detect pathogenic variants in autosomal recessive families: We look for differences in patterns of homozygosity around candidate variants between patients and control individuals and expect that such differences are greater for pathogenic variants than random candidate variants. In six autosomal recessive mitochondrial disease families, in which pathogenic homozygous variants have already been identified, our approach succeeded in prioritizing pathogenic mutations. Our method is applicable to single patients from recessive families with at least a few dozen control individuals from the same population; it is easy to use and is highly effective for detecting causative mutations in autosomal recessive families.

The inspection paradox and whole-genome analysis

One of the major challenges of modern biology is distinguishing meaningful patterns from the random fluctuations of DNA sequences resulting from chromosome shuffling in each generation. A disease-causing mutation is more likely to be found in a large recombination interval. The paradoxical observation that causal genetic variants are more likely to be found in larger intervals is a consequence of sampling bias and is known as the inspection paradox. According to this paradox, the interval containing a fixed point (the causal gene variant) is around double the length of an interval not subject to this constraint, but this average doubling of length is attenuated or neutralized at the ends of chromosomes, where the distribution of interval sizes gradually returns to normal. This prediction is experimentally testable. The consequences of sampling biases for haplotype patterns are small in large studies of many families, but may be more marked when trying to counsel an individual family, because the doubling of the size of segments is only a large-number average, and the effect may be much larger for an unusual number of recombination events. The challenge of identifying a causal signature from haplotype patterns is illustrated by the problem of the proportion of X-linked mutations in pairs of affected brothers.

Ranks of genuine associations in whole-genome scans

With the recent advances in high-throughput genotyping techniques, it is now possible to perform whole-genome association studies to fine map causal polymorphisms underlying important traits that influence susceptibility to human diseases and efficacy of drugs. Once a genome scan is completed the results can be sorted by the association statistic value. What is the probability that true positives will be encountered among the first most associated markers? When a particular polymorphism is found associated with the trait, there is a chance that it represents either a "true" or a "false" association (TA vs. FA). Setting appropriate significance thresholds has been considered to provide assurance of sufficient odds that the associations found to be significant are genuine. However, the problem with genome scans involving thousands of markers is that the statistic values of FAs can reach quite extreme magnitudes. In such situations, the distributions corresponding to TAs and the most extreme FAs become comparable and significance thresholds tend to penalize TAs and FAs in a similar fashion. When sorting between true and false associations, the "typical" place (i.e., rank) of TAs among the most significant outcomes becomes important, ordered by the association statistic value. The distribution of ranks that we study here allows calculation of several useful quantities. In particular, it gives the number of most significant markers needed for a follow-up study to guarantee that a true association is included with certain probability. This can be calculated conditionally on having applied a multiple-testing correction. Effects of multilocus (e.g., haplotype association) tests and impact of linkage disequilibrium on the distribution of ranks associated with TAs are evaluated and can be taken into account.

Identification of a major locus for age-related cortical cataract on chromosome 6p12-q12 in the Beaver Dam Eye Study

Age-related cataracts are one of the leading causes of visual impairment and blindness among the elderly worldwide. Among age-related cataracts, cortical opacities rank as the second most common type; however, little is known about their molecular pathogenesis or genetics. To identify susceptibility loci for cortical cataracts, we genotyped a subset of families (102 families; n = 224 sib pairs) from the Beaver Dam Eye Study and performed a model-free genome-wide linkage analysis for markers linked to a quantitative measure of cortical opacity. We obtained evidence for linkage at marker D1S1622 on chromosome 1p35 (P < 0.0002) and at marker D6S1053 on 6q12 (P < 0.00008) in the initial scan. Five additional regions on 1q31, 2p24, 2q11, 4q28, and 15q13 that are suggestive of linkage (P < or = 0.01 or logarithm of the likelihood ratio > or = 1.18) were observed. The region on chromosomes 6p12-q12 was selected for fine mapping, and the intermarker distance was reduced to 3 cM by adding 11 markers in the interval between D6S1017 and D6S1021. After fine mapping, significant evidence of linkage remained on chromosome 6p12-q12 at D6S1053 (P < 0.00005). The current genome scan for age-related cortical cataracts may lead to identification of novel genes, because few regions identified in the current scan have previously been implicated in congenital or age-related cataracts.

A comprehensive method for genome scans

In applications involving the use of genome scans the problem of correcting for multiple testing figures prominently. A frequently used approach is the Bonferroni adjustment, but this is known to be often severely conservative. As an alternative we use the method of importance sampling to accurately and efficiently obtain required exceedance probabilities. This method is comprehensive in the sense that it has application to exceedance probabilities for other classes of test statistics, such as those for linkage disequilibrium or Hardy-Weinberg equilibrium at multiple loci. We illustrate the importance sampling technique by focusing on affected sib pair tests done at a large number of fully informative markers. We demonstrate how our approach can be used to obtain exceedance probabilities for arbitrary marker spacings, and we compare our approach with that of Feingold et al. [1993], which uses the method of large deviations and does not provide the means for adjusting for unequal marker spacing.

A chromosome 14 risk locus for simple phobia: results from a genomewide linkage scan

We conducted a 10 centimorgan (cM) linkage genome scan in a set of American extended pedigrees ascertained through probands with panic disorder. Several anxiety disorders segregate in these families. In this article, we describe results for simple phobia from 14 of these families (including 129 subjects of whom 57 are affected). A total of 422 markers were genotyped. Multipoint lod score analyses (fully parametric and simple parametric models) and nonparametric analyses were completed using ALLEGRO. We observed significant linkage of simple phobia to chromosome 14 markers. The highest lod score under a fully parametric model was 3.17, at marker D14S75, under a dominant model. Under a fully parametric recessive model, the maximum lod score, also at D14S75, was 2.86. Analysis under a simple parametric model resulted in lod scores of 3.70 (dominant model) or 3.30 (recessive model). The highest Zlr score observed was 3.93 (P = 4.1 x 10(-5)). The Zlr score was >1 for an extensive region, >77 cM. In all, 12 of the 14 families studied provided positive or zero lod scores at marker D14S75 (dominant model). The homologous genomic region has been implicated by studies mapping quantitative trait loci for a mouse model of fear. The linkage peak may be regarded as highly promising, owing to the breadth of the peak, the convergence of results under different models of inheritance and different analysis methods, and the support from an animal model. This is the first genome scan linkage study for simple phobia, a common disorder that causes high morbidity in the US population.

Deciphering the gene expression profile of long-lived snell mice

In a recent publication, gene expression profiling experiments for the long-lived Snell dwarf mouse were reported. In this Perspective, the author argues that although the authors have developed a solid analytical approach, further refinements are needed to produce robust conclusions.