Accounting for a Quantitative Trait Locus for Plasma Triglyceride Levels: Utilization of Variants in Multiple Genes

For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect

Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes

Quantitative differences in gene expression are thought to contribute to phenotypic differences between individuals. We generated genome-wide transcriptional profiles of lymphocyte samples from 1,240 participants in the San Antonio Family Heart Study. The expression levels of 85% of the 19,648 detected autosomal transcripts were significantly heritable. Linkage analysis uncovered 41,000 cis-regulated transcripts at a false discovery rate of 5% and showed that the expression quantitative trait loci with the most significant linkage evidence are often located at the structural locus of a given transcript. To highlight the usefulness of this much-enlarged map of cis-regulated transcripts for the discovery of genes that influence complex traits in humans, as an example we selected high-density lipoprotein cholesterol concentration as a phenotype of clinical importance, and identified the cis-regulated vanin 1 (VNN1) gene as harboring sequence variants that influence high-density lipoprotein cholesterol concentrations. Phenotypic differences among individuals are partly the result of quantitative differences in transcript abundance. Although environmental stimuli may influence the location, timing, and/or level of transcription of specific genes, genetic differences among individuals are also known to have a significant role. Transcript levels may be thought of as quantitative endophenotypes that can be subjected to statistical genetic analyses in an effort to localize and identify the underlying genetic factors, an approach that is sometimes referred to as genetical genomics 1 . Using microarray technology, it is now possible to assess the abundance of many transcripts-and, indeed, of the entire known transcriptome-simultaneously. Studies that attempt to localize the genetic regulators of gene expression have been carried out in several species, including yeast 2-5 , plants (maize and eucalyptus) 6,7 , fly 8 , mouse 6,9,10 and rat 11 . Several recent investigations have also focused on humans. In most of these studies, microarray-based gene expression profiles were generated for transformed cell lines derived from lymphocytes from members of the Centre d'Etude du Polymorphisme Humain (CEPH) families 12 , and linkage and/or linkage disequilibrium approaches were used to map the genetic determinants that regulate the expression of individual transcript

Genetic variation in selenoprotein S influences inflammatory response

Chronic inflammation has a pathological role in many common diseases and is influenced by both genetic and environmental factors. Here we assess the role of genetic variation in selenoprotein S (SEPS1, also called SELS or SELENOS), a gene involved in stress response in the endoplasmic reticulum and inflammation control. After resequencing SEPS1, we genotyped 13 SNPs in 522 individuals from 92 families. As inflammation biomarkers, we measured plasma levels of IL-6, IL-1b and TNF-a. Bayesian quantitative trait nucleotide analysis identified associations between SEPS1 polymorphisms and all three proinflammatorycytokines. One promoter variant, 105G-A, showed strong evidence for an association with each cytokine (multivariate P = 0.0000002). Functional analysis of this polymorphism showed that the A variant significantly impaired SEPS1 expression after exposure to endoplasmic reticulum stress agents (P = 0.00006). Furthermore, suppression of SEPS1 by short interfering RNA in macrophage cells increased the release of IL-6 and TNF-a. To investigate further the significance of the observed associations, we genotyped 105G-A in 419 Mexican American individuals from 23 families for replication. This analysis confirmed a significantassociation with both TNF-a (P = 0.0049) and IL-1b (P = 0.0101). These results provide a direct mechanistic link between SEPS1 and the production of inflammatory cytokines and suggest that SEPS1 has a role in mediating inflammation.<br /

Meta‐Analysis of Genome‐wide Linkage Studies in BMI and Obesity

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome‐wide logarithm of the odds (LOD) scores, non‐parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI‐defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2‐ q33.1, 12q23‐q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3‐22.3 were also observed for BMI‐defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1‐qter and 12p11.21‐q23 (p < 0.01). Conclusion: Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93663/1/oby.2007.269.pd

Impact of Adjusting for SNPS by Candidate Gene.

<p>Impact of Adjusting for SNPS by Candidate Gene.</p

Descriptive Statistics of the Take Off Pounds Sensibly (TOPS) Cohort and the subsets used for following up linkage analysis.

<p>Descriptive Statistics of the Take Off Pounds Sensibly (TOPS) Cohort and the subsets used for following up linkage analysis.</p

Plot of single SNP association across the QTL interval on human chromosome 7q36.

<p>The positions for each SNP are listed on the x-axis, and are based on the genome assembly hg19. SNPs included in the targeted multi-SNP analysis (160 SNPs) are highlighted in back. The bars on the top of the diagram highlight the position of all coding sequences in the interval, the five genes of interest are labeled.</p