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.

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.

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.

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.

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.

The genetics of variation in gene expression.

The genetic basis of variation in gene expression lends itself to investigation by microarrays. For genetic analysis, we view the expression level of a gene as a quantitative or 'complex' trait, analogous to an individual's height or cholesterol level, and, therefore, as an inherited phenotype. Several genetic analyses of 'gene expression phenotypes' have been carried out in experimental organisms, and initial steps have been taken toward similar studies in humans--although these present challenging technical and statistical problems. Further advances in the genetic analysis of variation in gene expression will contribute to our understanding of transcriptional regulation and will provide models for studying other quantitative and complex traits.

Natural variation in human gene expression assessed in lymphoblastoid cells.

The sequencing of the human genome has resulted in greater attention to genetic variation among individuals, and variation at the DNA sequence level is now being extensively studied. At the same time, it has become possible to study variation at the level of gene expression by various methods. At present, it is largely unknown how widespread this variation in transcript levels is over the entire genome and to what extent individual differences in expression level are genetically determined. In the present study, we used lymphoblastoid cells to examine variation in gene expression and identified genes whose transcript levels differed greatly among unrelated individuals. We also found evidence for familial aggregation of expression phenotype by comparing variation among unrelated individuals, among siblings within families and between monozygotic twins. These observations suggest that there is a genetic contribution to polymorphic variation in the level of gene expression.

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.

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.

Mapping determinants of human gene expression by regional and genome-wide association.

To study the genetic basis of natural variation in gene expression, we previously carried out genome-wide linkage analysis and mapped the determinants of approximately 1,000 expression phenotypes. In the present study, we carried out association analysis with dense sets of single-nucleotide polymorphism (SNP) markers from the International HapMap Project. For 374 phenotypes, the association study was performed with markers only from regions with strong linkage evidence; these regions all mapped close to the expressed gene. For a subset of 27 phenotypes, analysis of genome-wide association was performed with >770,000 markers. The association analysis with markers under the linkage peaks confirmed the linkage results and narrowed the candidate regulatory regions for many phenotypes with strong linkage evidence. The genome-wide association analysis yielded highly significant results that point to the same locations as the genome scans for about 50% of the phenotypes. For one candidate determinant, we carried out functional analyses and confirmed the variation in cis-acting regulatory activity. Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases.

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.

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 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.

Genetic analysis of genome-wide variation in human gene expression.

Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.

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.

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.

Assessment of 115 candidate genes for diabetic nephropathy by transmission/disequilibrium test.

Several lines of evidence, including familial aggregation, suggest that allelic variation contributes to risk of diabetic nephropathy. To assess the evidence for specific susceptibility genes, we used the transmission/disequilibrium test (TDT) to analyze 115 candidate genes for linkage and association with diabetic nephropathy. A comprehensive survey of this sort has not been undertaken before. Single nucleotide polymorphisms and simple tandem repeat polymorphisms located within 10 kb of the candidate genes were genotyped in a total of 72 type 1 diabetic families of European descent. All families had at least one offspring with diabetes and end-stage renal disease or proteinuria. As a consequence of the large number of statistical tests and modest P values, findings for some genes may be false-positives. Furthermore, the small sample size resulted in limited power, so the effects of some tested genes may not be detectable, even if they contribute to susceptibility. Nevertheless, nominally significant TDT results (P < 0.05) were obtained with polymorphisms in 20 genes, including 12 that have not been studied previously: aquaporin 1; B-cell leukemia/lymphoma 2 (bcl-2) proto-oncogene; catalase; glutathione peroxidase 1; IGF1; laminin alpha 4; laminin, gamma 1; SMAD, mothers against DPP homolog 3; transforming growth factor, beta receptor II; transforming growth factor, beta receptor III; tissue inhibitor of metalloproteinase 3; and upstream transcription factor 1. In addition, our results provide modest support for a number of candidate genes previously studied by others.

Linkage and association with type 1 diabetes on chromosome 1q42.

Type 1 diabetes is a complex disorder with multiple genetic loci and environmental factors contributing to disease etiology. In the current study, a human type 1 diabetes candidate region on chromosome 1q42 was mapped at high marker density in a panel of 616 multiplex type 1 diabetic families. To facilitate the identification and evaluation of candidate genes, a physical map of the 7-cM region surrounding the maximum logarithm of odds (LOD) score (2.46, P = 0.0004) was constructed. Genes were identified in the 500-kb region surrounding the marker yielding the peak LOD score and evaluated for polymorphism by resequencing. Single-nucleotide polymorphisms (SNPs) identified in these genes as well as other anonymous markers were tested for allelic association with type 1 diabetes by both family-based and case-control methods. A haplotype formed by common alleles at three adjacent markers (D1S225, D1S2383, and D1S251) was preferentially transmitted to affected offspring in type 1 diabetic families (nominal P = 0.006). These findings extend the evidence supporting the existence of a type 1 diabetes susceptibility locus on chromosome 1q42 and identify a candidate region amenable to positional cloning efforts.

Variation in resistin gene promoter not associated with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility and affects approximately 4-7% of reproductive age women in the U.S. It is characterized by hyperandrogenemia and chronic anovulation and is associated with insulin resistance, obesity, and increased risk for type 2 diabetes. In a screen of candidate genes, a region on chromosome 19p13.3 was identified that shows significant evidence for both linkage and association with PCOS. A promising candidate gene for PCOS, resistin, maps to exactly this region. Resistin is a protein hormone thought to modulate glucose tolerance and insulin action. We tested for association between a single nucleotide polymorphism in the promoter region of the resistin gene and three phenotypes: PCOS, obesity, and insulin resistance. We did not find evidence for association with any of the phenotypes. It is therefore unlikely that variation in the resistin gene accounts for the strong association that we observe between chromosome 19p13.3 and PCOS. Instead, this association is most likely due to a gene or genetic element in this region that has not been identified.

Bridging genetics and genomics in neurology.

The Human Genome Project has stimulated a change in the ways human diseases are studied genetically. The field is moving towards a genome-wide approach to identifying susceptibility genes and to understanding the molecular basis of diseases. In this article, we review the emerging technologies and discuss results from studies that have used genomic approaches.