Type 2 diabetes susceptibility single-nucleotide polymorphisms are not associated with polycystic ovary syndrome.

Two cohorts of women with polycystic ovary syndrome (PCOS), comprising 400 probands and affected sisters in 365 families and a case-control group including 395 women with PCOS and 171 healthy women with regular menstrual cycles, were studied to determine whether single-nucleotide polymorphisms (SNPs) identified as susceptibility loci in genomewide association studies of type 2 diabetes are also associated with PCOS. None of the 18 allelic variants in 10 genes previously shown to be associated with type 2 diabetes were found to be associated with PCOS, but some were associated with indices of beta cell function.

FTO and MC4R gene variants are associated with obesity in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility in women. It is also associated with metabolic disturbances that place women at increased risk for obesity and type 2 diabetes. There is strong evidence for familial clustering of PCOS and a genetic predisposition. However, the gene(s) responsible for the PCOS phenotypes have not been elucidated. This two-phase family-based and case-control genetic study was designed to address the question of whether SNPs identified as susceptibility loci for obesity in genome-wide association studies (GWAS) are also associated with PCOS and elevated BMI. Members of 439 families having at least one offspring with PCOS were genotyped for 15 SNPs previously shown to be associated with obesity. Linkage and association with PCOS was assessed using the transmission/disequilibrium test (TDT). These SNPs were also analyzed in an independent case-control study involving 395 women with PCOS and 176 healthy women with regular menstrual cycles. Only one of these 15 SNPs (rs2815752 in NEGR1) was found to have a nominally significant association with PCOS (χ(2) = 6.11, P = 0.013), but this association failed to replicate in the case-control study. While not associated with PCOS itself, five SNPs in FTO and two in MC4R were associated with BMI as assessed with a quantitative-TDT analysis, several of which replicated association with BMI in the case-control cohort. These findings demonstrate that certain SNPs associated with obesity contribute to elevated BMI in PCOS, but do not appear to play a major role in PCOS per se. These findings support the notion that PCOS phenotypes are a consequence of an oligogenic/polygenic mechanism.

Polymorphic cis- and trans-regulation of human gene expression.

Expression levels of human genes vary extensively among individuals. This variation facilitates analyses of expression levels as quantitative phenotypes in genetic studies where the entire genome can be scanned for regulators without prior knowledge of the regulatory mechanisms, thus enabling the identification of unknown regulatory relationships. Here, we carried out such genetic analyses with a large sample size and identified cis- and trans-acting polymorphic regulators for about 1,000 human genes. We validated the cis-acting regulators by demonstrating differential allelic expression with sequencing of transcriptomes (RNA-Seq) and the trans-regulators by gene knockdown, metabolic assays, and chromosome conformation capture analysis. The majority of the regulators act in trans to the target (regulated) genes. Most of these trans-regulators were not known to play a role in gene expression regulation. The identification of these regulators enabled the characterization of polymorphic regulation of human gene expression at a resolution that was unattainable in the past.

A variant in the fibrillin-3 gene is associated with TGF-ß and inhibin B levels in women with polycystic ovary syndrome.

In an attempt to evaluate the association between allele 8 (A8) of D19S884 in the fibrillin-3 gene and circulating transforming growth factor (TGF) ß and inhibin levels in women with polycystic ovary syndrome (PCOS), we studied 120 similarly aged women from families with PCOS and compared 40 women with PCOS who did not have A8 (A8- PCOS) with 40 women with PCOS who had A8 (A8+ PCOS) and 40 normally menstruating women who did not have either PCOS or A8 (A8- Non-PCOS). A8- PCOS is associated with higher levels of TGF-ß1 compared with A8+ PCOS or A8- Non-PCOS, similar levels of TGF-ß2 compared with A8+ PCOS but lower levels of TGF-ß2 compared with A8- Non-PCOS, and lower levels of inhibin B and aldosterone compared with A8+ PCOS.

A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families.

In a quantitative trait locus (QTL) study, it is usually not feasible to select families with offspring that simultaneously display variability in more than one phenotype. When multiple phenotypes are of interest, the sample will, with high probability, contain 'non-segregating' families, i.e. families with both parents homozygous at the QTL. These families potentially reduce the power of regression-based methods to detect linkage. Moreover, follow-up studies in individual families will be inefficient, and potentially even misleading, if non-segregating families are selected for the study. Our work extends Haseman-Elston regression using a latent class model to account for the mixture of segregating and non-segregating families. We provide theoretical motivation for the method using an additive genetic model with two distinct functions of the phenotypic outcome, squared difference (SqD) and mean-corrected product (MCP). A permutation procedure is developed to test for linkage; simulation shows that the test is valid for both phenotypic functions. For rare alleles, the method provides increased power compared to a 'marginal' approach that ignores the two types of families; for more common alleles, the marginal approach has better power. These results appear to reflect the ability of the algorithm to accurately assign families to the two classes and the relative weights of segregating and non-segregating families to the test of linkage. An application of Bayes rule is used to estimate the family-specific probability of segregating. High predictive value positive values for segregating families, particularly for MCP, suggest that the method has considerable value for identifying segregating families. The method is illustrated for gene expression phenotypes measured on 27 candidate genes previously demonstrated to show linkage in a sample of 14 families.

Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells.

The accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER) results in the condition called "ER stress," which induces the unfolded protein response (UPR), a complex cellular process that includes changes in expression of many genes. Failure to restore homeostasis in the ER is associated with human diseases. To identify the underlying changes in gene expression in response to ER stress, we induced ER stress in human B cells and then measured gene expression at ten time points. We followed up those results by studying cells from 60 unrelated people. We rediscovered genes that were known to play a role in the ER-stress response and uncovered several thousand genes that are not known to be involved. Two of these are VLDLR and INHBE, which showed significant increase in expression after ER stress in B cells and in primary fibroblasts. To study the links between UPR and disease susceptibility, we identified ER-stress-responsive genes that are associated with human diseases and assessed individual differences in the ER-stress response. Many of the UPR genes are associated with Mendelian disorders, such as Wolfram syndrome, and complex diseases, including amyotrophic lateral sclerosis and diabetes. Data from two independent samples showed extensive individual variability in ER-stress response. Additional analyses with monozygotic twins revealed significant correlations within twin pairs in their responses to ER stress, thus showing evidence for heritable variation among individuals. These results have implications for basic understanding of ER function and its role in disease susceptibility.

Family-based analysis of candidate genes for polycystic ovary syndrome.

CONTEXT: Polycystic ovary syndrome (PCOS) is a complex disorder having both genetic and environmental components. A number of association studies based on candidate genes have reported significant association, but few have been replicated. D19S884, a polymorphic marker in fibrillin 3 (FBN3), is one of the few association findings that has been replicated in independent sets of families. OBJECTIVE: The aims of the study are: 1) to genotype single nucleotide polymorphisms (SNPs) in the region of D19S884; and 2) to follow up with an independent data set, published results reporting evidence for PCOS candidate gene associations. DESIGN: The transmission disequilibrium test (TDT) was used to analyze linkage and association between PCOS and SNPs in candidate genes previously reported by us and by others as significantly associated with PCOS. SETTING: The study was conducted at academic medical centers. PATIENTS OR OTHER PARTICIPANTS: A total of 453 families having a proband with PCOS participated in the study. Sisters with PCOS were also included. There was a total of 502 probands and sisters with PCOS. INTERVENTION(S): There were no interventions. MAIN OUTCOME MEASURE(S): The outcome measure was transmission frequency of SNP alleles. RESULTS: We identified a six-SNP haplotype block spanning a 6.7-kb region on chromosome 19p13.2 that includes D19S884. SNP haplotype allele-C alone and in combination with D19S884-allele 8 is significantly associated with PCOS: haplotype-C TDT chi(2) = 10.0 (P = 0.0016) and haplotype-C/A8 TDT chi(2) = 7.6 (P = 0.006). SNPs in four of the other 26 putative candidate genes that were tested using the TDT were nominally significant (ACVR2A, POMC, FEM1B, and SGTA). One SNP in POMC (rs12473543, chi(2) = 9.1; P(corrected) = 0.042) is significant after correction for multiple testing. CONCLUSIONS: A polymorphic variant, D19S884, in FBN3 is associated with risk of PCOS. POMC is also a candidate gene of interest.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions.

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes.

Genetics of human gene expression: mapping DNA variants that influence gene expression.

There is extensive natural variation in human gene expression. As quantitative phenotypes, expression levels of genes are heritable. Genetic linkage and association mapping have identified cis- and trans-acting DNA variants that influence expression levels of human genes. New insights into human gene regulation are emerging from genetic analyses of gene expression in cells at rest and following exposure to stimuli. The integration of these genetic mapping results with data from co-expression networks is leading to a better understanding of how expression levels of individual genes are regulated and how genes interact with each other. These findings are important for basic understanding of gene regulation and of diseases that result from disruption of normal gene regulation.

PDE8A genetic variation, polycystic ovary syndrome and androgen levels in women.

Polycystic ovary syndrome (PCOS) is characterized by excessive theca cell androgen secretion, dependent upon LH, which acts through the intermediacy of 3',5'-cyclic adenosine monophosphate (cAMP). cAMP signaling pathways are controlled through regulation of its synthesis by adenylyl cyclases, and cAMP degradation by phosphodiesterases (PDEs). PDE8A, a high-affinity cAMP-specific PDE is expressed in the ovary and testis. Leydig cells from mice with a targeted mutation in the Pde8a gene are sensitized to the action of LH in terms of testosterone production. These observations led us to evaluate the human PDE8A gene as a PCOS candidate gene, and the hypothesis that reduced PDE8A activity or expression would contribute to excessive ovarian androgen production. We identified a rare variant (R136Q; NM_002605.2 c.407G > A) and studied another known single nucleotide polymorphism (SNP) (rs62019510, N401S) in the PDE8A coding sequence causing non-synonymous amino acid substitutions, and a new SNP in the promoter region (NT_010274.16:g.490155G > A). Although PDE8A kinetics were consistent with reduced activity in theca cell lysates, study of the expressed variants did not confirm reduced activity in cell-free assays. Sub-cellular localization of the enzyme was also not different among the coding sequence variants. The PDE8A promoter SNP and a previously described promoter SNP did not affect promoter activity in in vitro assays. The more common coding sequence SNP (N401S), and the promoter SNPs were not associated with PCOS in our transmission/disequilibrium test-based analysis, nor where they associated with total testosterone or dehydroepiandrosterone sulfate levels. These findings exclude a significant role for PDE8A as a PCOS candidate gene, and as a Las major determinant of androgen levels in women.

Genetic analysis of radiation-induced changes in human gene expression.

Humans are exposed to radiation through the environment and in medical settings. To deal with radiation-induced damage, cells mount complex responses that rely on changes in gene expression. These gene expression responses differ greatly between individuals and contribute to individual differences in response to radiation. Here we identify regulators that influence expression levels of radiation-responsive genes. We treated radiation-induced changes in gene expression as quantitative phenotypes, and conducted genetic linkage and association studies to map their regulators. For more than 1,200 of these phenotypes there was significant evidence of linkage to specific chromosomal regions. Nearly all of the regulators act in trans to influence the expression of their target genes; there are very few cis-acting regulators. Some of the trans-acting regulators are transcription factors, but others are genes that were not known to have a regulatory function in radiation response. These results have implications for our basic and clinical understanding of how human cells respond to radiation.

Effects of cis and trans genetic ancestry on gene expression in African Americans.

Variation in gene expression is a fundamental aspect of human phenotypic variation. Several recent studies have analyzed gene expression levels in populations of different continental ancestry and reported population differences at a large number of genes. However, these differences could largely be due to non-genetic (e.g., environmental) effects. Here, we analyze gene expression levels in African American cell lines, which differ from previously analyzed cell lines in that individuals from this population inherit variable proportions of two continental ancestries. We first relate gene expression levels in individual African Americans to their genome-wide proportion of European ancestry. The results provide strong evidence of a genetic contribution to expression differences between European and African populations, validating previous findings. Second, we infer local ancestry (0, 1, or 2 European chromosomes) at each location in the genome and investigate the effects of ancestry proximal to the expressed gene (cis) versus ancestry elsewhere in the genome (trans). Both effects are highly significant, and we estimate that 12+/-3% of all heritable variation in human gene expression is due to cis variants.

A review of family-based tests for linkage disequilibrium between a quantitative trait and a genetic marker.

Quantitative trait transmission/disequilibrium tests (quantitative TDTs) are commonly used in family-based genetic association studies of quantitative traits. Despite the availability of various quantitative TDTs, some users are not aware of the properties of these tests and the relationships between them. This review aims at outlining the broad features of the various quantitative TDT procedures carried out in the frequently used QTDT and FBAT packages. Specifically, we discuss the "Rabinowitz" and the "Monks-Kaplan" procedures, as well as the various "Abecasis" and "Allison" regression-based procedures. We focus on the models assumed in these tests and the relationships between them. Moreover, we discuss what hypotheses are tested by the various quantitative TDTs, what testing procedures are best suited to various forms of data, and whether the regression-based tests overcome population stratification problems. Finally, we comment on power considerations in the choice of the test to be used. We hope this brief review will shed light on the similarities and differences of the various quantitative TDTs.

Disease associations and family-based tests.

This unit describes the statistical techniques necessary for performing family-based association studies (such as the TDT) for genetic polymorphisms. Such studies have become increasingly important in the identification of genes that confer an increased risk to disease, particularly for common diseases with a complex etiology. The family-based approach avoids some of the problems often encountered when applying the traditional case-control design to genetic studies. The unit includes the Sib TDT (S-TDT) method, which allows application of the principle of the TDT to sibships without parental data, and several related tests.

Monozygotic twins reveal germline contribution to allelic expression differences.

Variation in the level of gene expression is a major determinant of a cell's function and characteristics. Common allelic variants of genes can be expressed at different levels and thus contribute to phenotypic diversity. We have measured allelic expression differences at heterozygous loci in monozygotic twins and in unrelated individuals. We show that the extent of differential allelic expression is highly similar within monozygotic twin pairs for many loci, implying that allelic differences in gene expression are under genetic control. We also show that even subtle departures from equal allelic expression are often genetically determined.

Ovulatory response to treatment of polycystic ovary syndrome is associated with a polymorphism in the STK11 gene.

CONTEXT: Clomiphene and insulin sensitizers such as metformin are used to induce ovulation in polycystic ovary syndrome (PCOS), but the ovulatory response is variable, and the causes of this variation are poorly understood. OBJECTIVE: Our objective was to identify predictive genetic polymorphisms and other determinants of ovulatory response. DESIGN: This was a substudy of a multicenter randomized clinical trial. SETTING: This study was performed at academic medical centers and their affiliates. PARTICIPANTS: A total of 312 women with PCOS were included in the study. MAIN OUTCOME MEASURES: Historical, biometric, biochemical, and genetic parameters were performed. RESULTS: We found that the C allele of a single nucleotide polymorphism in the STK11 gene (expressed in liver; also known as LKB1) was associated with a significantly decreased chance of ovulation in PCOS women treated with metformin. In an analysis of ovulation per cycle, the adjusted odds ratio (OR) comparing the C/C genotype to the G/G genotype was 0.30 [95% confidence interval (CI) 0.14, 0.66], and the OR for the C/G genotype vs. the G/G genotype was also 0.30 (95% CI 0.14, 0.66). In an analysis of metformin-treated subjects, we found that the percentage of women who ovulated increased with the number of G alleles present: 48% (10 of 21) of C/C women, 67% (32 of 48) of C/G women, and 79% (15 of 19) of G/G women ovulated. We also found that increased frequency of ovulation was associated with lower body mass index (BMI) [adjusted OR of 2.36 (95% CI 1.65, 3.36) and 2.05 (95% CI 1.46, 2.88), respectively, for comparisons of BMI less than 30 vs. BMI equal to or more than 35, BMI 30-34 vs. BMI equal to or more than 35, in the analysis of ovulation per cycle], a lower free androgen index (FAI) [adjusted OR of 1.59 (95% CI 1.17, 2.18) for FAI<10 vs. FAI>or=10], and a shorter duration of attempting conception [adjusted OR of 1.63 (95% CI 1.20, 2.21) for<1.5 vs.>or=1.5 yr]. CONCLUSIONS: We have demonstrated that a polymorphism in STK11, a kinase gene expressed in liver and implicated in metformin action, is associated with ovulatory response to treatment with metformin alone in a prospective randomized trial. The interaction with the effects of changes in modifiable factors (e.g. BMI or FAI) requires further study.

Introduction to Genetic Analysis Workshop 15 summaries.

The 15th biennial Genetic Analysis Workshop (GAW15) took place November 11-15, 2006 in St. Pete Beach, Florida. The workshop's primary focus was on the appropriate linkage, association, and other analyses of the increasingly large datasets generated by genetics research. A record number of participants (N=350) contributed 252 papers to GAW15. These contributions were organized into 17 presentation groups, with a range of 11 to 18 papers in each group (median of 15 papers per group). The data sets--or "problems"--for GAW15 included information from two real data sets and a simulated data set. The first problem utilizing real data included gene expression as the phenotype and genome-wide markers for linkage and association studies. The second problem allowed for detecting and characterizing genetic effects for rheumatoid arthritis. And the simulated problem was generated to reflect the data structure underlying the rheumatoid arthritis study. Further details on GAW15 are provided here, and the primary findings from the workshop are highlighted in the following group summary papers.

Common genetic variants account for differences in gene expression among ethnic groups.

Variation in DNA sequence contributes to individual differences in quantitative traits, but in humans the specific sequence variants are known for very few traits. We characterized variation in gene expression in cells from individuals belonging to three major population groups. This quantitative phenotype differs significantly between European-derived and Asian-derived populations for 1,097 of 4,197 genes tested. For the phenotypes with the strongest evidence of cis determinants, most of the variation is due to allele frequency differences at cis-linked regulators. The results show that specific genetic variation among populations contributes appreciably to differences in gene expression phenotypes. Populations differ in prevalence of many complex genetic diseases, such as diabetes and cardiovascular disease. As some of these are probably influenced by the level of gene expression, our results suggest that allele frequency differences at regulatory polymorphisms also account for some population differences in prevalence of complex diseases.

Data for Genetic Analysis Workshop (GAW) 15, Problem 1: genetics of gene expression variation in humans.

Here we describe the data provided for Problem 1 of Genetic Analysis Workshop 15. The data provided for Problem 1 were unusual in two ways. First, the phenotype was the level of gene expression for each gene, not a conventional phenotype like height or disease, and second, there were more than 3500 such phenotypes. Natural variation in gene expression was a new idea in 2004 when these data were collected and published. Because the phenotypes were measured in members of 14 Centre d'Etude du Polymorphisme Humain (CEPH) families, there was an opportunity for linkage mapping on a very large scale. For this purpose, 2882 single-nucleotide polymorphism genotypes were also provided for each family member.

Genetic heterogeneity and trans regulators of gene expression.

Heterogeneity poses a challenge to linkage mapping. Here, we apply a latent class extension of Haseman-Elston regression to expression phenotypes with significant evidence of linkage to trans regulators in 14 large pedigrees. We test for linkage, accounting for heterogeneity, and classify individual families as "linked" and "unlinked" on the basis of their contribution to the overall evidence of linkage.