Genomewide linkage analysis of stature in multiple populations reveals several regions with evidence of linkage to adult height

Abstract

Genomewide linkage analysis has been extremely successful at identification of the genetic variation underlying single-gene disorders. However, linkage analysis has been less successful for common human diseases and other complex traits in which multiple genetic and environmental factors interact to influence disease risk. We hypothesized that a highly heritable complex trait, in which the contribution of environmental factors was relatively limited, might be more amenable to linkage analysis. We therefore chose to study stature (adult height), for which heritability is approximately 75%-90% (Phillips and Matheny 1990; Carmichael and McGue 1995; Preece 1996; Silventoinen et al. 2000). We reanalyzed genomewide scans from four populations for which genotype and height data were available, using a variance-components method implemented in GENEHUNTER 2.0 (Pratt et al. 2000). The populations consisted of 408 individuals in 58 families from the Botnia region of Finland, 753 individuals in 183 families from other parts of Finland, 746 individuals in 179 families from Southern Sweden, and 420 individuals in 63 families from the Saguenay-Lac-St.-Jean region of Quebec. Four regions showed evidence of linkage to stature: 6q24-25, multipoint LOD score 3.85 at marker D6S1007 in Botnia (genomewide P<.06), 7q31.3-36 (LOD 3.40 at marker D7S2195 in Sweden, P<.02), 12p11.2-q14 (LOD 3.35 at markers D12S10990-D12S398 in Finland, P<.05) and 13q32-33 (LOD 3.56 at markers D13S779-D13S797 in Finland, P<.05). In a companion article (Perola et al. 2001 [in this issue]), strong supporting evidence is obtained for linkage to the region on chromosome 7. These studies suggest that highly heritable complex traits such as stature may be genetically tractable and provide insight into the genetic architecture of complex traits.

Figure 1

Graph of the heritability of height Z scores in the Botnia population. Each point on the X-axis represents a different family; the families are arranged in increasing order of mean Z score. For each family, the maximum (×), mean (thick line), and minimum (•) height Z scores are shown. Heritability is reflected in the correlation of Z scores within each family. In this population, a difference in Z score of 1 represents ∼6 cm in height.

Figure 2

Multipoint LOD scores for linkage to stature in each of the four populations. For each chromosome, the total genetic length is shown below the X-axis. The multipoint variance-components (VC) LOD score for each population at markers along each chromosome (proceeding pter→qter) is plotted in dashed lines. Black lines represent data for Botnia, green lines represent data for Finland, red lines represent data for Sweden, and blue lines represent data for Saguenay–Lac-St.-Jean. The two solid bars in the graphs for chromosomes 7 and 9 indicate the locations of the two regions with multipoint LOD scores >2.5 in Perola et al. ( [in this issue]). The X chromosome was not analyzed.

Figure 3

Expected distribution of LOD scores in the presence of a modest QTL. 500 data sets with the pedigree structure of the Swedish sample used in the study were generated under the assumption of an additive QTL explaining 20% of total variance. A histogram of the percentage of the 500 simulated scans yielding different observed LOD scores is shown.

Figure 4

Ordered subset analysis for linkage to chromosome 6, ranking families by intrafamilial variance in stature. Families from Botnia were ranked by total intrafamilial variance in height Z score, and subsets of families were analyzed for linkage of stature to chromosome 6. Subsets were successively increased in size by ∼10 families, beginning with the families with the greatest variance. The % maximum LOD score (triangles) indicates the LOD score obtained using the number of families indicated on the X-axis divided by the LOD score obtained using the complete set; % of total individuals (circles) is the fraction of the total population contained in the subset of families being analyzed. The difference between the curves reflects the gain in efficiency by considering only a subset of families.