Molecular characterization and phenotypic expression of mutations in genes for gonadotropins and their receptors in humans.

Gonadotropin hormones undergo important dynamic changes during life. Their rise during puberty stimulates gonadal steroid secretion, triggering the development of secondary sexual characteristics and the acquisition of fertility. The full spectrum of possible mutations and polymorphisms in the human gonadotropins and in their receptor genes has been described in recent years. Patients harboring these mutations display a very wide range of phenotypes affecting all aspects of the reproductive axis. An important insight provided by the careful study of these patients lies in the striking gender differences in the phenotypes associated with a given mutation. As a result, the careful study of these rare patients has allowed us to better define the respective roles of luteinizing hormone and follicle-stimulating hormone in normal human pubertal development and in the achievement of full fertility potential in either males or females. In this work, we describe briefly the known mutations in the genes for both gonadotropins and their receptors, and discuss their genotype/phenotype correlations in light of these important gender differences.

Selected polymorphisms in sex hormone-related genes, circulating sex hormones and risk of endometrial cancer.

BACKGROUND: The role of estrogen and progesterone in the development of endometrial cancer is well documented. Few studies have examined the association of genetic variants in sex hormone-related genes with endometrial cancer risk. METHODS: We conducted a case-control study nested within three cohorts to examine the association of endometrial cancer risk with polymorphisms in hormone-related genes among 391 cases (92% postmenopausal at diagnosis) and 712 individually-matched controls. We also examined the association of these polymorphisms with circulating levels of sex hormones and SHBG in a cross-sectional analysis including 596 healthy postmenopausal women at blood donation (controls from this nested case-control study and from a nested case-control study of breast cancer in one of the three cohorts). RESULTS: Adjusting for endometrial cancer risk factors, the A allele of rs4775936 in CYP19 was significantly associated (OR(per allele)=1.22, 95% CI=1.01-1.47, p(trend)=0.04), while the T allele of rs10046 was marginally associated with increased risk of endometrial cancer (OR(per allele)=1.20, 95% CI=0.99-1.45, p(trend)=0.06). PGR rs1042838 was also marginally associated with risk (OR(per allele)=1.25, 95% CI=0.96-1.61, p(trend)=0.09). No significant association was found for the other polymorphisms, i.e. CYP1B1 rs1800440 and rs1056836, UGT1A1 rs8175347, SHBG rs6259 and ESR1 rs2234693. Rs8175347 was significantly associated with postmenopausal levels of estradiol, free estradiol and estrone and rs6259 with SHBG and estradiol. CONCLUSION: Our findings support an association between genetic variants in CYP19, and possibly PGR, and risk of endometrial cancer.

Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study.

Adiponectin has a variety of metabolic effects on obesity, insulin sensitivity, and atherosclerosis. To identify genes influencing variation in plasma adiponectin levels, we performed genome-wide linkage and association scans of adiponectin in two cohorts of subjects recruited in the Genetic Epidemiology of Metabolic Syndrome Study. The genome-wide linkage scan was conducted in families of Turkish and southern European (TSE, n = 789) and Northern and Western European (NWE, N = 2,280) origin. A whole genome association (WGA) analysis (500K Affymetrix platform) was carried out in a set of unrelated NWE subjects consisting of approximately 1,000 subjects with dyslipidemia and 1,000 overweight subjects with normal lipids. Peak evidence for linkage occurred at chromosome 8p23 in NWE subjects (lod = 3.10) and at chromosome 3q28 near ADIPOQ, the adiponectin structural gene, in TSE subjects (lod = 1.70). In the WGA analysis, the single-nucleotide polymorphisms (SNPs) most strongly associated with adiponectin were rs3774261 and rs6773957 (P < 10(-7)). These two SNPs were in high linkage disequilibrium (r(2) = 0.98) and located within ADIPOQ. Interestingly, our fourth strongest region of association (P < 2 x 10(-5)) was to an SNP within CDH13, whose protein product is a newly identified receptor for high-molecular-weight species of adiponectin. Through WGA analysis, we confirmed previous studies showing SNPs within ADIPOQ to be strongly associated with variation in adiponectin levels and further observed these to have the strongest effects on adiponectin levels throughout the genome. We additionally identified a second gene (CDH13) possibly influencing variation in adiponectin levels. The impact of these SNPs on health and disease has yet to be determined.

Genes mirror geography within Europe.

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing; an individual's DNA can be used to infer their geographic origin with surprising accuracy-often to within a few hundred kilometres.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.