Association of FOXE1 Polyalanine Repeat Region with Papillary Thyroid Cancer

CONTEXT: Polyalanine tract variations in transcription factors have been identified for a wide spectrum of developmental disorders. The thyroid transcription factor forkhead factor E1 (FOXE1) contains a polymorphic polyalanine tract with 12-22 alanines. Single-nucleotide polymorphisms (SNP) close to this locus are associated with papillary thyroid cancer (PTC), and a strong linkage disequilibrium block extends across this region. OBJECTIVE: The objective of the study was to assess whether the FOXE1 polyalanine repeat region was associated with PTC and to assess the effect of polyalanine repeat region variants on protein expression, DNA binding, and transcriptional function on FOXE1-responsive promoters. DESIGN: This was a case-control study. SETTING: The study was conducted at a tertiary referral hospital. PATIENTS AND METHODS: The FOXE1 polyalanine repeat region and tag SNP were genotyped in 70 PTC, with a replication in a further 92 PTC, and compared with genotypes in 5767 healthy controls (including 5667 samples from the Wellcome Trust Case Control Consortium). In vitro studies were performed to examine the protein expression, DNA binding, and transcriptional function for FOXE1 variants of different polyalanine tract lengths. RESULTS: All the genotyped SNP were in tight linkage disequilibrium, including the FOXE1 polyalanine repeat region. We confirmed the strong association of rs1867277 with PTC (overall P = 1 × 10(-7), odds ratio 1.84, confidence interval 1.31-2.57). rs1867277 was in tight linkage disequilibrium with the FOXE1 polyalanine repeat region (r(2) = 0.95). FOXE1(16Ala) was associated with PTC with an odds ratio of 2.23 (confidence interval 1.42-3.50; P = 0.0005). Functional studies in vitro showed that FOXE1(16Ala) was transcriptionally impaired compared with FOXE1(14Ala), which was not due to differences in protein expression or DNA binding. CONCLUSIONS: We have confirmed the previous association of FOXE1 with PTC. Our data suggest that the coding polyalanine expansion in FOXE1 may be responsible for the observed association between FOXE1 and PTC.

Multistage genome-wide association meta-analyses identified two new loci for bone mineral density

Aiming to identify novel genetic variants and to confirm previously identified genetic variants associated with bone mineral density (BMD), we conducted a three-stage genome-wide association (GWA) meta-analysis in 27 061 study subjects. Stage 1 meta-analyzed seven GWA samples and 11 140 subjects for BMDs at the lumbar spine, hip and femoral neck, followed by a Stage 2 in silico replication of 33 SNPs in 9258 subjects, and by a Stage 3 de novo validation of three SNPs in 6663 subjects. Combining evidence from all the stages, we have identified two novel loci that have not been reported previously at the genome-wide significance (GWS; 5.0 × 10-8) level: 14q24.2 (rs227425, P-value 3.98 × 10-13, SMOC1) in the combined sample of males and females and 21q22.13 (rs170183, P-value 4.15 × 10-9, CLDN14) in the female-specific sample. The two newly identified SNPs were also significant in the GEnetic Factors for OSteoporosis consortium (GEFOS, n 5 32 960) summary results. We have also independently confirmed 13 previously reported loci at the GWS level: 1p36.12 (ZBTB40), 1p31.3 (GPR177), 4p16.3 (FGFRL1), 4q22.1 (MEPE), 5q14.3 (MEF2C), 6q25.1 (C6orf97, ESR1), 7q21.3 (FLJ42280, SHFM1), 7q31.31 (FAM3C, WNT16), 8q24.12 (TNFRSF11B), 11p15.3 (SOX6), 11q13.4 (LRP5), 13q14.11 (AKAP11) and 16q24 (FOXL1). Gene expression analysis in osteogenic cells implied potential functional association of the two candidate genes (SMOC1 and CLDN14) in bone metabolism. Our findings independently confirm previously identified biological pathways underlying bone metabolism and contribute to the discovery of novel pathways, thus providing valuable insights into the intervention and treatment of osteoporosis. © The Author 2013. Published by Oxford University Press.

Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture

The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 x 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 x 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 x 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. Prostate 75:1467–1474, 2015. © 2015 Wiley Periodicals, Inc.