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SNPs, microarrays and pooled DNA: identification of four loci associated with mild mental impairment in a sample of 6000 children

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  • Lee M. Butcher
  • Emma Meaburn
  • Jo Knight
  • Pak C. Sham
  • Leonard C. Schalkwyk
  • Ian W. Craig
  • Robert Plomin
<mark>Journal publication date</mark>15/05/2005
<mark>Journal</mark>Human Molecular Genetics
Issue number10
Number of pages11
Pages (from-to)1315-1325
Publication StatusPublished
Early online date15/05/05
<mark>Original language</mark>English


Mild mental impairment (MMI) represents the low extreme of the quantitative trait of general intelligence and is highly heritable. Quantitative trait loci (QTLs) conferring susceptibility to MMI, as for most complex traits, are likely to be of small effect size. Using a novel approach we call SNP-MaP (SNP Microarrays and Pooling), we have identified four loci associated with MMI. These four loci have been replicated in two SNP-MaP studies and verified by individual genotyping. The two SNP-MaP studies conducted were a case versus control comparison (n = 515 and n = 1028, respectively) and a low versus high general intelligence extremes group comparison (n = 503 and n = 505, respectively). Each of the four groups consisted of five independent 'subpools', with each subpool assayed on a separate microarray. Twelve loci showing the largest significant differences in both SNP-MaP studies were individually genotyped on 6154 children. Of the four loci positively associated with MMI, the minor allele of each conferred the greater risk for MMI. Two of the loci are close to known genes and may be in linkage disequilibrium with them. One of the loci is between the candidate genes KLF7 and CREB1, but given possible long-range effects on expression and the unknown importance of untranslated elements such as micro-RNAs, all four loci deserve attention as candidates. Although each SNP accounts for a small amount of variance, their effects are additive and they can be combined in a 'SNP set' that can be used as a genetic risk index for MMI in behavioral genomic analyses.