Evaluating the results of genomewide linkage scans of complex traits by locus counting

Abstract

The evaluation of results from primary genomewide linkage scans of complex human traits remains an area of importance and considerable debate. Apart from the usual assessment of statistical significance by use of asymptotic and empirical calculations, an additional means of evaluation--based on counting the number of distinct regions showing evidence of linkage--is possible. We have explored the characteristics of such a locus-counting method over a range of experimental conditions typically encountered during genomewide scans for complex trait loci. Under the null hypothesis, factors that have an impact on the informativeness of the data--such as map density, availability of parental data, and completeness of genotyping--are seen to markedly influence the number of regions of excess allele sharing and the empirically derived genomewide significance of the associated LOD score thresholds. In some circumstances, the expected number of regions is less than one-quarter of that predicted under the assumption of a dense map and complete extraction of inheritance information. We have applied this method to a previously analyzed data set--the Warren 2 genome scan for type 2-diabetes susceptibility--and demonstrate that more regions showing evidence for linkage were observed in the primary genome scan than would be expected by chance, across the whole range of LOD scores, even though no single linkage result achieved empirical genomewide statistical significance. Locus counting may be useful in assessing the results from genome scans for complex traits in general, especially because relatively few scans generate evidence for linkage reaching genomewide significance by dense-map criteria. By taking account of the effects of reduced data informativeness on the expected number of regions showing evidence for linkage, a more meaningful, and less conservative, evaluation of the results from such linkage studies is possible.

Figure 1

Null IRL distributions for 500 two-sib families, simulated with a missing genotype rate of 15% and analyzed with a linear model using GENEHUNTER PLUS. The figure shows the effects of three different marker-map densities and the availability of parental genotypes on the expected number of IRLs per genome scan, over the LOD score threshold range 0.59–6. The position of one IRL expected by chance per genome scan is indicated by the horizontal broken line.

Figure 2

Empirical genomewide significance levels for 500 two-sib families, simulated with a missing genotype rate of 15% and analyzed with a linear model using GENEHUNTER PLUS. The figure shows the effects of three different marker-map densities and the availability of parental genotypes on the empirical genomewide significance levels of the full range of IRL LOD score thresholds. The position of an empirical genomewide significance level of .05 is indicated by the horizontal broken line.

Figure 3

Null IRL distribution (i.e., the mean number of IRLs at each LOD score threshold under the null hypothesis) generated from the Warren 2 genome scan data, and the actual IRL distribution observed from the Warren 2 genome scan, analyzed with an exponential model using ALLEGRO. The P=.05 significance limits for the number of IRLs per genome scan, estimated empirically and from a Poisson distribution, are shown in order to indicate the significance of the actual IRL distribution. The position of one IRL expected by chance per genome scan is indicated by the horizontal broken line.