Genetic Linkage to Human Height is Identified to 9q22 and Xq24

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Liu, Yaozhong
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Human height is a highly heritable trait, with heritability generally above 0.75. Segregation analyses in different populations supported existence of major genes accounting for a considerable fraction of height variation. Association studies suggested a list of candidate genes associated with height Two previous consecutive whole genome linkage studies using 630 (WG1 study) and an extended sample of 1,816 Caucasians (WG2 study) suggested 9q22 [MLS (maximum LOD score) = 2.74 in WG2 study] and Xq24 (two-point LOD score = 1.91 in WG1 study, 2.64 in WG2 study) linked to height Therefore, the general hypothesis for the current study is that 9q22 and Xq24 are two important genomic regions containing QTLs (quantitative trait loci) for height variation. To test this hypothesis, using a greatly extended large sample containing 3,726 Caucasians, a new genome-wide linkage scan was performed. A MLS of 4.34 was detected on 9q22 and a two-point LOD score of 5.63 was attained for Xq24. In an independent sample (ie., the subjects not involved in WG1 and WG2 studies), 9q22 and Xq24 also achieved significant empirical p values (0.002 and 0.004, respectively) for "region-wise" linkage confirmation. Importantly, the two regions were replicated on a genotyping platform different from that used in the WG1 and WG2 studies (ie., a different set of markers and different genotyping instruments). Interestingly, 9q22 harbors the ROR2 gene, that is required for growth plate development and Xq24 has been linked to idiopathic short stature. With the largest sample from a single population of the same ethnicity in linkage studies for complex traits, the current study, together with two previous ones, has provided overwhelming evidence identifying two new regions for human height variation. In particular, the data from the three consecutive whole genome studies are uniquely valuable and exemplary. They represent one of the first practical (rather than simulated) examples of how significant increase in sample size will improve linkage detection for human complex traits.
Creighton University
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