Paper of the week: Visualizing associations between paired data sets

This week's paper of the week is by González, et al., entitled "Visualising associations between paired `omics' data sets," and published in BioData Mining (vol 5:19). The pdf of this report can be found here.

The authors demonstrate that graphical outputs such as Correlation Circle plots, Relevance Networks and Clustered Image Maps are useful in the visualization and interpretation of output from integrative analysis tools. The goal is to facilitate an understanding of systems as a whole when complex data often force donning of blinders to not observe the whole forest.

The graphical tools described in the report are implemented in the freely available R package mixOmics and in its associated web application.

As an example of what the authors have built, consider their presentation of Nutrimouse data showing correlations (or not) between between large data sets, in case gene expression and metabolite levels in liver, as taken from their figure 5.The Nutrimouse data are from a nutrigenomic study in which 40 mice from two genotypes (wild-type and Ppara -/-) were fed five diets with different fatty acid compositions. Details are in the Methods section. Expression of 120 genes in liver cells was obtained with microarrays and concentrations of 21 hepatic fatty acids were measured by gas chromatography. Hence, the data matrices are of size (40 × 120) for the gene expression and (40 × 120) for the fatty acids measurements.

The Authors write: The Correlation Circle plot (above) displays all fatty acids and the genes selected on each component (100 in total in this plot). Highlighted are subsets of variables important in defining each component. For example, C18:2ω6, C20:2ω6 and C16:0 are fatty acids for which variation allows the definition of the sPLS component 2 (top and bottom of the y-axis). Similarly, genes such as Car1, Acoth, Siat4c, Scarb1 (SR.BI) and Slc10a1 (Ntcp, or Ntop [sic]) are positively correlated to each other, and to the fatty acid C16:1ω9 and their variation participate in defining the sPLS component 1 (left-hand side of the x-axis).

I find such analysis and depiction of results useful and look forward to trying this with our GWAS data.

My agenda for ASHG 2012

This week I will attend the annual conference of the American Society of Human Genetics, ASHG. It was suggested that we who will broadcast observations, comments, invites, critiques and musings on Twitter should also post an agenda of those sessions we feel are important to attend. I've done just that, listing below those sessions I plan to attend.

Hashtag will be #ASHG2012

All talks are tweetable, opt-out, meaning if the speaker says nothing to the contrary, one can tweet

Wednesday, November 7
8:00 am - 10:00 am
5. Gene Regulatory Change: The Engine of Human Evolution? Room 135, Lower Level North
9. Surveying Customer Responses to Personal Genetic Services Room 132, Lower Level North

10:30 am - 12:45 pm
15. New Loci for Obesity, Diabetes, and Related Traits Gateway Ballroom 104, Lower Level South

2:15 pm - 4:15 pm
Poster session 1

Thursday, November 8
8:00 am - 10:00 am
22. Common and Rare CNVs: Genesis, Patterns of Variations and Human Diseases Hall D, Lower Level North

10:30 am - 12:45 pm
32. Cardiovascular Genetics: GWAS and Beyond Room 134, Lower Level North
37. Metabolic Disease Discoveries Room 123, Lower Level North

2:15 pm - 4:15 pm
Poster session 2

4:30 pm - 6:45 pm
44. Tools for Phenotype Analysis Room 132, Lower Level North

Friday, November 9
8:00 am - 10:30 am
47. Structural and Regulatory Genomic Variation Hall D, Lower Level North
53. From SNP to Function in Complex Traits Room 132, Lower Level North

2:15 pm - 4:15 pm
Poster session 3

4:30 pm - 6:45 pm
61. Missing Heritability, Interactions and Sequencing Room 135, Lower Level North
63. Transcriptional Regulation, Variation and Complexity Gateway Ballroom 104, Lower Level South
64. Epigenetics Room 124, Lower Level North

Saturday, November 10
9:40 am - 11:40 am
76. The Functional Consequences of microRNA Dysregulation in Human Disease Room 134, Lower Level North

NuGO Week 2012

The following are what I took away as highlights from NuGO Week 2012, held from 28 to 31 August 2012 in Helsinki, Finland. I felt that this conference showed a marked maturity in research accomplishments of the nutrigenomics community. In the past, this conference and others, as well as personal communications, were quite often invoked intent to use omics platforms without showing much in the way of data. That changed dramatically at this conference – there were presentations with a lot of data.

Some of the top themes were: Networks and GxEs, metabolic profiling done to quantify metabolites, either known or as discovery of a metabolic process, or done to quantify adherence to a given diet/food type intake, eg plant polyphenolics, aging and health.

Specific notes:

My take-away lessons from Marju Orho-Melander’s talk (Lund University): Your genetic susceptibility is affected by what you eat/how you eat/what and how much you exercise/etc. She uses the Malmö Diet and Cancer Study n=28499. 1750 have incident T2DM. Protein from animal sources increases risk of T2DM, while whole grain/high-fiber intake decreases the risk. Using the epidemiology data (eg, animal protein intake increases risk) to focus or inform the interaction work may be something worth looking into. She’s ready to perform a GxE analysis using the GWAS data from the Malmö Diet and Cancer Study. Instead of using a genetic risk score for the disease, use a pathway approach to consider a marker for the disease, prior to the endpoint of disease itself. So, look at interactions for glucose and glucose homeostasis in place of interactions for T2DM.

Aldons “Jake” Lusis of UCLA. It is difficult to go from a GWAS hit to a mechanism in humans. He uses a systems genetics approach: integrate clinical traits and intermediate phenotypes across a population using correlation and gene mapping. He uses mice because this offers a controlled environment, tissue access, and deep biochemical profiling. They use 100 classical inbred mouse strains, genome sequenced and GWAS-like association mapping. His group is looking at genetics of dietary response in 6 to 8 mice/strain. Some mice have no change in body fat going from chow to high-fat/high-sugar diet, others have substantial change. They also looked at food consumption. They always look at males and females separately. Food intake may be more strongly related to lean body mass according to the stronger correlation between food intake and body weight over food intake with fat mass. They use a T-test on 135,000 SNPs in a GWAS. Threshold is determined by permutation or simulation. Interactions will be identified using association and correlation in his systems genetics strategy. Visit http://systems.genetics.ucla.edu to see the loci that control body fat or look gene by gene to see what traits are associated with that gene.

Melissa Morine of University of Trento. Within a network, one can perform a modularity calculation – whereby members are highly connected to each other and rather unconnected to nodes outside the module.

Marjukka Kolehmainen of University of Eastern Finland. Of 82 individuals who were obese, only 34 donated abdominal subcutaneous tissue both before and after very low-calorie diet. All 3 PPAR pathways were down-regulated in the subcutaneous adipose during the very low calorie diet intervention. Energy metabolism was also strongly down-regulated. Both pathways returned to near normal levels during the maintenance period.

ETHERPATHS. Anne-Marja Aura of VTT Technical Research Centre of Finland: There is a set of known and detectable metabolites of given fatty acids that can be used as biomarkers of intake. It seems important to assess both microbial metabolites and those found in the serum. Robert Caesar (University of Gothenburg) examines diet-microbial regulation of liver and adipose transcriptomics. Macrophages from germ-free (obese-resistant) mice have decreased expression of Ccr2 chemokine receptor. Compare WAT and liver gene expression in response to metabolites from the gut microflora. Liver should be more responsive because of close link via vena cava. Liver pathways altered: Up: lymphocyte mediated immunity, adaptive immune response, innate immune response, immune effector process, cell activation, chemotaxis, positive regulation in response to stimulus; Down: sterol metabolic process, cell adhesion, lipid metabolic process, etc. Gut microbiota increases liver inflammation during high-fat diet independent of dietary lipid quality. Tuulia Hyötyläinen (VTT Technical Research Centre of Finland) looks at lipoprotein lipids and polar lipids in the lipoprotein fractions. Some of this work is published in Mol Biosyst in 2012. N-3 intervention caused TG levels to go down in females, no change in males. There were also sex differences for metabolites seen in lipoprotein fractions.

Mark Boekschoten of Wageningen University. PLS-path model gives them 44 liver and 69 adipose genes important in body weight gain. Variation in these genes in humans could manifest as GxEs for total caloric intake or saturated fat intake on body weight.

Jessica Schwarz of Wageningen University. Her poster shows that a high-protein diet restores VLDL production (which is lowered with a high-fat diet) and prevents fat accumulation in the liver in mice. She got onto this project from the observation that a high-protein diet showed lower oil-red staining in liver and lower TG levels.

Hector Keun of Imperial College of London. He has two objectives: Testing for association within and between data types; incorporating background knowledge to enhance our ability to interpret associations. A multivariate model example is O2-PLS, which can be used to compare two data blocks, reducing it to the simplest list of associations. This was developed by Trygg & Wold. It is also of interest to describe what variation is not common to the two data blocks. For example, there could be variation that is specific to the metabolomic data that does not show in the proteomic data from those same animals. Pathway significance is calculated using the hypergeometric distribution test, when comparing a set of up-regulated genes with genes in a given pathway to see if that pathway is over-represented, by chance, in the set of up-regulated genes. It is also possible to use this analysis approach on the FFQ data. See Kamburov and Cavill for access to their webtools. His adjustment for background incorporates the fact that the number of observations or tests really for genes is much higher than for metabolites, for example.

Willem de Vos of Wageningen University. The Bacterioides/Firmicutes ratio found in the gut microbiome is not helpful with regard to diet, interventions and obesity. This group uses the log [CFU/g feces] on x-axis on a graph to look at correlation with some factor (he used LPS binding proteins) with changes to the microbiota.

Jacqueline Monteiro of University of São Paulo. There is a correlation or relationship between calcium in the diet and adipocyte differentiation. Kids in the lowest quintile for milk intake were in the highest quintile for BMI in their Delta Project.

Life aboard the International Space Station

As some of you who follow me on Twitter may already know, NASA astronaut Suni Williams and I were childhood friends. We were on the same swim team together. She is aboard the International Space Station (ISS) at this moment on Expedition 32, beginning a 4-month stay about a week and a half ago. Since her first trip to the ISS in 2006, I've been in touch with her and that got me on the invite list to attend a special launch party for her current mission. At that event, there was a special presentation by Captain Dan Burbank. He was Commander of Expedition 30 to the ISS and returned to Earth on 27 April 2012 after a five-month stay aboard the ISS.

I was curious to learn about the behavior of the astronauts on the ISS in terms of diet, physical activity (especially with regard to bone loss and muscle function) and sleep. Many of you know how our research group examines the role of environmental factors in modifying disease risk. These are GxE, or gene-by-environment, interactions. Diet, dietary components (eg, certain fatty acids, protein content, carbohydrates), exercise (or sedentary behavior) and sleep are key environmental factors for our work.

Dan told me that he would normally consume about 3500 calories per day on Earth but that increased by about 500 calories aboard the ISS. He could not say if it was more carbs or fat or protein or just a bit more of everything. He did not speak much about exercise other than to tell us all during his slide presentation that there is a new resistance machine on board that provides 400 pounds of resistance. The previous machine provided only 100 pounds of resistance and the 400 level is what is needed to stem bone loss. He told us that when one types on a keyboard, only a few strokes are needed to send the person across the room in microgravity. So, they "stand" with feet hooked under railings, like as bar rail. This gives them calluses on the tops of their feet, while those on the soles begin to fade.

What was perhaps the most interesting to me was Captain Dan's sleep habits. He said that on the ISS he needed only 4 to 7 hours of sleep per night. What's more, he did not strap himself in to provide a feeling of lying down, but could sleep anywhere, floating in his room.

All in all, it was a really cool experience to meet an astronaut, to learn about life aboard the ISS, and to see someone I know launch with a Soyuz rocket to begin her latest adventure.

Good luck and continued success with your mission, Suni!

The WHO's report on noncommunicable diseases

The World Health Organization of the United Nations has released a report titled "Global status report on noncommunicable diseases." Access to the report and its individual chapters is at this link. I was particularly interested in Chapter 1 and the major contributing factors to noncommunicable diseases (NCD).

According to the above report and others from the WHO, the four primary contributors to global increases in NCDs, such as type 2 diabetes, cancer, and cardiovascular diseases, are:

tobacco

harmful use of alcohol

unhealthy diet

physical inactivity

While such a list is really not surprising, what I do take from this, with respect to my own research on the genetic basis for the differential response to the diet as it pertains to metabolic diseases, is these are our key environmental factors used to assess gene by environment, or GxE, interactions. In other words, while these factors are strong contributors to NCD onset and progression, genetic differences exert different influences on the disease risk, onset and progression in different individuals. That influence could be negative - increasing risk - or positive - being more protective.

Thus, the importance of GxE identification cannot be overlooked, and ought really to be emphasized in genetic association studies.

POTW: Uncovering the function of an intergenic SNP

My choice for Paper Of The Week this week is a report from a few weeks back (digging through the pile...) in which a polymorphism conferring increased risk of renal cell carcinoma is investigated for allele-specific functions. The paper is "Common genetic variants at the 11q13.3 renal cancer susceptibility locus influence binding of HIF to an enhancer of cyclin D1 expression" by Schödel, et al. (Nature Genetics 44:420-425).

Although the authors had several clues that the risk SNPs would (likely) affect expression of CCND1 (cyclin D1) in a manner regulated by hypoxia-induced factors - namely, that HIFs were known to regulate CCND1 but from an unknown binding site and that CCND1 is an established oncogene, among others - they accumulated much new data to nail down the role of EPAS1 (HIF-2) in regulating CCND1 expression.

One nice aspect of this work is the authors' taking advantage of signals seen in a renal carcinoma cell line and not in a breast cancer cell line (serving then as control). For example, they looked at the epigenetic enhancer marks at the 11q13.3 susceptibility locus with FAIRE (ormaldehyde-assisted isolation of regulatory elements to identify regions of nucleosome occupancy), and EPAS1 binding as assessed by ChIP-qPCR. The use of pVHL-defective RCC cell lines verified the role of VHL (von Hippel–Lindau tumor suppressor) in this cancer and consequence of allele-specific expression of CCND1.

Taken together, the data presented show that the haplotype associating with reduced renal cell cancer risk hinders EPAS1 binding, "resulting in an allelic imbalance in cyclin D1 expression, thus affecting a link between hypoxia pathways and cell cycle control." This is nice work and a fine example of the approaches needed to develop a clear understanding of polymorphism and disease risk from a functional perspective.

POTW: Bitter taste perception - a follow-up

Back in December, I posted an item on taste receptors expressed in the gut with mention of possible roles in sensing the microbiome. This week's Paper of the Week is entitled "Evolution of functionally diverse alleles associated with PTC bitter taste sensitivity in Africa" by the Tishkoff group and heightens those earlier, intriguing possibilities.

The publication dissects the long evolutionary history of the TAS2R38 gene encoding a bitter taste receptor. From RefSeq, we know that TAS2R38 encodes a seven-transmembrane G protein-coupled receptor that controls the ability to taste glucosinolates, a family of bitter-tasting compounds found in plants of the Brassica sp. Interestingly, TAS2R38 allows detection of bitter thiourea compounds, including 6-n-propylthiouracil (PROP) and phenylthiocarbamide (PTC). Humans who cannot taste these compounds tend to be poor at discriminating fat in foods, even though they prefer higher fat versions of these foods (Keller, KL 2012 J Food Science 77:S143). This would lead one to suppose, naturally, that the development of certain haplotypes of tasters and nontasters would arise as adaptation to the local diet. Tishkoff, et al show that is not likely to be the case.

First, the authors propose that the evolution of the three nonsynonymous mutations, which comprise the commonly observed haplotypes, likely represent an alternate path for building a diverse set of receptors in humans, which can then participate in various biological processes. They go on to suggest that a complex selection model, involving "ancient balancing and recent diversifying selection," has allowed both common and rare nonsynonymous variation, respectively, to persist in the coding exon of TAS2R38 in Africa. Importantly, different types of selection may have acted upon the noncoding regions compared to the TAS2R38 coding exon in all populations.

Second, diet is not the driver of haplotype frequencies. The authors propose that the three common haplotypes observed may appear at high frequencies due to selective pressures distinct from diet. Recent reports have demonstrated that bitter taste receptors are expressed in many cell types in the human gastrointestinal tract and lungs (second reference). Here this expression can affect glucose and insulin levels (Dotson et al. 2008), eliminate harmful inhaled substances, and promote relaxation of airways for better breathing. Thus, bitter taste loci, including TAS2R38, posses various functions and, as the authors write "raise[s] the possibility that common variants at TAS2R38 may be under selection due to their physiological roles in human health beyond oral gustatory function." The authors were not able to distinguish which selective forces - taste, gut microbiome organisms or biochemical production, or inhalants - are acting upon the TAS2R38 haplotypes.

Third, the genetic analysis and evolutionary history of TAS2R38 suggest that, in contrast to a common variant-common disease hypothesis, sensitivity to PTC bitter taste indicates that both rare and common variants together are able to significantly affect normal variation of phenotypes. This, of course, has implications, as genome-wide association studies tackle a wider range of phenotypes in a more diverse set of populations, and as genome sequencing (whole and exome) seek to identify and associate rare variants with disease risk and occurrence.

POTW: Evolutionary constraints and the discovery of disease markers

My selection for Paper of the Week for 16 March 2012 is by Joel Dudley, et al. and published as a letter in Molecular Biology & Evolution. Its title is "Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery."

The authors examined over 5800 disease-associating variants, comparing the genomic neighborhood across a panel of species. This covered 230 different disease and disease risk phenotypes. Importantly, the authors demonstrate that there is a propensity to discover such disease SNPs at "conserved genomic positions, because the effect size (odds ratio) and allelic P-value of genetic association of a SNP relates strongly to the evolutionary conservation of their genomic position." This then allowed them to develop a new means to rank such association SNPs in which a conservation score, based on the evolutionary analysis, is incorporated into the P-value of the genotype-phenotype association.

As many GWAS SNPs alter gene expression - either through altered transcription factor binding or microRNA-mRNA interaction, and as such evolutionary mechanisms most likely involve a sensing or monitoring of the environment with concomitant changes in gene expression, this makes sense. In fact, the role of such types of SNPs (those under selective pressure) and their role in heart disease, was a topic on which we published in 2010.

The article by Dudley, et al. is really nice work and one whose insight we will use to inform our GWAS analysis.

POTW: Exercise and gene methylation

The Paper of the Week for 9 March 2012 was entitled "Acute Exercise Remodels Promoter Methylation in Human Skeletal Muscle" by Barres, et al. It appeared in Cell Metabolism as a Short Article.

The exercise test was performed on a stationary bicycle. One cohort of subjects were exercised until reaching either 40% or 80% of VO₂ peak. A second cohort was exercised until 1,674 kJ were expended. These were acute interventions, making the findings all the more remarkable.

I found the following to be key points of this paper:

1. In both healthy, sedentary women and men, it was observed that whole genome methylation was decreased in skeletal muscle.

2. While exercise induced expression of PPARGC1A (PGC-1α), PDK4, and PPARD, the authors also noted reduced methylation at each of the promoters for these genes.

PPARGC1A is a key transcriptional regulator of OXPHOS (oxidative phosphorylation) genes. It is also an important type 2 diabetes gene.

POTW: Epigenetics and cognitive function

This weeks Paper of the Week adds some detail to connections between cognitive function and epigenetics as histone modifications. The paper is "An epigenetic blockade of cognitive functions in the neurodegenerating brain" by Gräff, et al. The paper was published in Nature on 29 Feb 2012.

What makes this a noteworthy paper, in my opinion, is the link between Alzheimer disease and lifestyle choices. The lifestyle choices of smoking, diet and physical activity (and likely others) have the ability to affect epigenetic patterns of either DNA methylation or histone acetylation. The authors demonstrate that cognitive abilities in a brain with developing neurodegeneration are held in check by an epigenetic-based restriction of gene transcription, and this is potentially reversible. This repression of mRNA synthesis is mediated by histone deacetylase 2 (or HDAC2). Furthermore, this repression is increased by Alzheimer’s-disease-related neurotoxic insults in vitro, in two mouse models of neurodegeneration and in patients with Alzheimer’s disease.

Imagine if something in the diet or something like exercise could reduce or repress the built-up activity of HDAC2 that occurs as a result of the neurotoxic insults described in the paper. That would be exciting. Thus, I see this work as important in showing, again, how environment and epigenetics can affect disease state. It is certainly likely that certain lifestyle choices would have greater or lesser impact on neurodegenerative processes and either augment or enhance the genetic risk of disease. Although not demonstrated in this article, it could be that an APOE epsilon 4 (E4) genotype, with its increased risk of Alzheimer disease could be partially ameliorated via those lifestyle choices that inhibit or curtail excessive HDAC2 activity. That woud indeed be quite exciting.

Proteomics of fasting

It was with keen interest that I read the article by Bouwman, et al. entitled “2D-electrophoresis and multiplex immunoassay proteomic analysis of different body fluids and cellular components reveal known and novel markers for extended fasting,” which appeared in BMC Medical Genomics last week. As we are interested in the genetic basis for the differential response to diet, we view perturbations to the system, either by a high-fat intervention or fasting/calorie restriction, as instrumental in deciphering this response. Overall, I found this to be nice work and deserving of a wide audience.

The authors report that “[p]rincipal component analysis applied to the multiplex immunoassay (RBM) data set revealed that each of the subjects could be identified based on levels of 89 plasma proteins (see figure 3). It appears that such data can be used to provide a metabolic fingerprint of the individual volunteers participating in this intervention study. However, this demonstrates that the between-subject effects are larger than that of the fasting effect.”

This is not surprising given that of the 44 different proteins identified as responding to extended fasting (see tables 2 & 3, figure 4), nine are encoded by genes harboring variants responding differentially to environmental factors such as dietary intake and physical activity. The dietary component most often modulating the association between those genes (and their variants) and a phenotype pertinent to metabolic syndrome is fat. Physical activity is also a wide-reaching modulator of the association between genetic variation and various phenotypes pertinent to metabolic syndrome. In other words, a combination of genetic variation between study participants in combination with each individual’s lifestyle choices (say, more or less exercise) could indeed influence the levels of certain proteins found to respond to the fasting intervention.

At the same time, I cannot dispute, as the authors write, that the between-subject variation may have arisen from heterogeneity of the study cohort “with regard to various parameters, including gender and BMI.” This is logical, but again other factors such as habitual diet and exercise, even sleep patterns could be at work here. Another source of between-subject variation is certainly genetic.

The authors observe that “[m]ost interesting biomarkers are involved in metabolic pathways, as well as those related to inflammation and oxidative stress.” This is where my quite minor complaint with the work arises – I would have liked to see more interpretation of the results from a biological or even medical perspective. Thus, I note that IL10, IL1B, TNF, SERPINE1, INS and CCL2 respond to extended fasting and are members of the Insulin resistance inflammation network (Olefsky, Glass (2010) in a review of Macrophages, Inflammation and Insulin Resistance (Annu Rev Physiol 72:219-46)). Furthermore, VCAM1, APCS, CRP, IL1B, TNF, IL18 and CCL2 are assigned an inflammation role within the set of PPARA target genes (Rakhshandehroo, Kersten 2010 PPAR Research pii: 612089).

A second comparison I undertook was to look at the number of genes responding to the fasting intervention and to an intervention termed AIDM: Anti-inflammatory dietary mix (Bakker, et al 2010 Am J Clin Nutr 91:1044). Large-scale assays of genes, proteins, and metabolites in plasma, urine, and adipose tissue showed that an intervention with selected dietary components influenced inflammatory processes, oxidative stress and metabolism in humans. Eight genes are in common and we’d expect about one by chance. These eight genes are FABP3, VCAM1, IL12A, AFP, FTH1, IL18, APOA1 and F7. Most of these eight were described in the AIDM article as down-regulated (lower levels) in plasma by the dietary intervention, similar to the response to fasting. This raises the intriguing hypothesis that the AIDM diet at least partially mimics fasting.

Adipokines are signaling proteins that are secreted from adipocytes. It is an interesting observation, then, that four of the altered proteins seen during fasting are described by Rosenow, et al as adipokines. These are SERPINE1, SERPINF1, C3 and TIMP1. Perhaps fasting-induced changes to the signaling potential of adipose tissue should focus on these four proteins.

POTW: January, 2012 choices

Three papers published last month that I found to be of interest are listed here. These are:

The mystery of missing heritability: Genetic interactions create phantom heritability, by Zuk, et al. This addresses the missing heritability question, suggesting that "the total heritability may be much smaller and thus the proportion of heritability explained much larger."

Characterisation and discovery of novel miRNAs and moRNAs in JAK2V617F mutated SET2 cells, by Botoluzzi, et al. What interested me in this article was the generation of novel microRNAs that were induced by the cancerous state triggered by this JAK2 variant. This indicates to me that the microRNA realm is broad and rich with many as yet undiscovered relationships.

The PLoS One paper entitled "Genetic signatures of exceptional longevity in humans," by Sebastiani, et al. We here were very curious how this was different from the version retracted from Science and what findings are now reported. TOMM40 near APOE is indeed interesting.

Paper of the Week: cis-eQTL between normal and cancer tissue

My choice for Paper of the Week this week is "cis-Expression QTL Analysis of Established Colorectal Cancer Risk Variants in Colon Tumors and Adjacent Normal Tissue," by Loo, Cheng, Tiirikainen, Lum-Jones, Seifried, et al. (2012) appearing in PLoS One.

I find this article of interest because I feel that many GWAS hits for disease risk will serve to alter expression of a near or distant gene(s) in an allele-specific manner. This group looked at gene expression differences between tissues that were either colorectal tumors or their paired, adjacent normal tissue, and then associated those gene expression differences with allelic variation. Assaying 40 individuals was sufficient to identify 3 SNPs affecting expression of 4 genes: ATP5C1, DLGAP5, NOL3 and DDX28.

A link to this paper is here.

Citation:
Loo LWM, Cheng I, Tiirikainen M, Lum-Jones A, Seifried A, et al. (2012) cis-Expression QTL Analysis of Established Colorectal Cancer Risk Variants in Colon Tumors and Adjacent Normal Tissue. PLoS ONE 7(2): e30477.