BACKGROUND/AIMS: Genome-wide association studies revealed associations of single nucleotide polymorphisms (SNPs) flanking MC4R with body mass index variability and obesity. We genotyped 28 SNPs, covering MC4R, and searched for haplotypes discriminating between obese mutation carriers and non-carriers. METHODS: We analyzed all three-marker haplotype combinations of the 28 SNPs to discriminate between obese mutation carriers and non-carriers - overall and in functional categories for 25 different MC4R mutations: (a) 'like wild type', (b) 'partial loss of function', and (c) 'complete loss of function'. We checked for the possible impact of 'cryptic relatedness' by sensitivity analyses including only 1 randomly selected patient per mutation. RESULTS: Overall analyses revealed a haplotype of 3 SNPs downstream of the MC4R discriminating between obese mutation carriers and obese non-carriers. However, sensitivity analyses showed that the finding is most likely due to cryptic relatedness. CONCLUSION: Given a mutation prevalence of 1-5%, the sample size of 62 obese mutation carriers with overall 25 different MC4R mutations represents a unique feature of our study. Taking MC4R as an example, we demonstrate the impact of cryptic relatedness when trying to link non-coding SNPs to functionally relevant mutations. Hence, a thorough mutation screen can currently not be guided by SNP genotyping.
BACKGROUND/AIMS: Genome-wide association studies revealed associations of single nucleotide polymorphisms (SNPs) flanking MC4R with body mass index variability and obesity. We genotyped 28 SNPs, covering MC4R, and searched for haplotypes discriminating between obese mutation carriers and non-carriers. METHODS: We analyzed all three-marker haplotype combinations of the 28 SNPs to discriminate between obese mutation carriers and non-carriers - overall and in functional categories for 25 different MC4R mutations: (a) 'like wild type', (b) 'partial loss of function', and (c) 'complete loss of function'. We checked for the possible impact of 'cryptic relatedness' by sensitivity analyses including only 1 randomly selected patient per mutation. RESULTS: Overall analyses revealed a haplotype of 3 SNPs downstream of the MC4R discriminating between obese mutation carriers and obese non-carriers. However, sensitivity analyses showed that the finding is most likely due to cryptic relatedness. CONCLUSION: Given a mutation prevalence of 1-5%, the sample size of 62 obese mutation carriers with overall 25 different MC4R mutations represents a unique feature of our study. Taking MC4R as an example, we demonstrate the impact of cryptic relatedness when trying to link non-coding SNPs to functionally relevant mutations. Hence, a thorough mutation screen can currently not be guided by SNP genotyping.
Authors: Anna-Lena Volckmar; Chung Ting Han; Carolin Pütter; Stefan Haas; Carla I G Vogel; Nadja Knoll; Christoph Struve; Maria Göbel; Katharina Haas; Nikolas Herrfurth; Ivonne Jarick; Harald Grallert; Annette Schürmann; Hadi Al-Hasani; Johannes Hebebrand; Sascha Sauer; Anke Hinney Journal: PLoS One Date: 2016-02-01 Impact factor: 3.240