Konstantin Shakhbazov

Calculating statistical power in Mendelian randomization studies

In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2SLS) MR analyses based on continuous variables has previously been published. However there are presently no specific equations or software tools one can implement for calculating power of a given MR study. Using asymptotic theory, we show that in the case of continuous variables and a single instrument, for example a single-nucleotide polymorphism (SNP) or multiple SNP predictor, statistical power for a fixed sample size is a function of two parameters: the proportion of variation in the exposure variable explained by the genetic predictor and the true causal association between the exposure and outcome variable. We demonstrate that power for 2SLS MR can be derived using the non-centrality parameter (NCP) of the statistical test that is employed to test whether the 2SLS regression coefficient is zero. We show that the previously published power estimates from simulations can be represented theoretically using this NCP-based approach, with similar estimates observed when the simulation-based estimates are compared with our NCP-based approach. General equations for calculating statistical power for 2SLS MR using the NCP are provided in this note, and we implement the calculations in a web-based application.

Detection and replication of epistasis influencing transcription in humans

Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits and contributes to their variation is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms, and some examples have been reported in other species, few examples exist for epistasis among natural polymorphisms in human traits. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues. Here we show, using advanced computation and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < 2.91 × 10−16). Replication of these interactions in two independent data sets showed both concordance of direction of epistatic effects (P = 5.56 × 10−31) and enrichment of interaction P values, with 30 being significant at a conservative threshold of P < 9.98 × 10−5. Forty-four of the genetic interactions are located within 5 megabases of regions of known physical chromosome interactions (P = 1.8 × 10−10). Epistatic networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis-acting SNP is modulated by several trans-acting SNPs. For example, MBNL1 is influenced by an additive effect at rs13069559, which itself is masked by trans-SNPs on 14 different chromosomes, with nearly identical genotype–phenotype maps for each cis–trans interaction. This study presents the first evidence, to our knowledge, for many instances of segregating common polymorphisms interacting to influence human traits.

Population genetic differentiation of height and body mass index across Europe

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10−8; BMI, P < 5.95 × 10−4), and we find an among-population genetic correlation for tall and slender individuals (r = −0.80, 95% CI = −0.95, −0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

Interaction of c-Myb with p300 is required for the induction of acute myeloid leukemia (AML) by human AML oncogenes

The MYB oncogene is widely expressed in acute leukemias and is important for the continued proliferation of leukemia cells, suggesting that MYB may be a therapeutic target in these diseases. However, realization of this potential requires a significant therapeutic window for MYB inhibition, given its essential role in normal hematopoiesis, and an approach for developing an effective therapeutic. We previously showed that the interaction of c-Myb with the coactivator CBP/p300 is essential for its transforming activity. Here, by using cells from Booreana mice which carry a mutant allele of c-Myb, we show that this interaction is essential for in vitro transformation by the myeloid leukemia oncogenes AML1-ETO, AML1-ETO9a, MLL-ENL, and MLL-AF9. We further show that unlike cells from wild-type mice, Booreana cells transduced with AML1-ETO9a or MLL-AF9 retroviruses fail to generate leukemia upon transplantation into irradiated recipients. Finally, we have begun to explore the molecular mechanisms underlying these observations by gene expression profiling. This identified several genes previously implicated in myeloid leukemogenesis and HSC function as being regulated in a c-Myb-p300-dependent manner. These data highlight the importance of the c-Myb-p300 interaction in myeloid leukemogenesis and suggest disruption of this interaction as a potential therapeutic strategy for acute myeloid leukemia.

Endometriosis risk alleles at 1p36.12 act through inverse regulation of CDC42 and LINC00339

Genome-wide association studies (GWAS) have identified markers within the WNT4 region on chromosome 1p36.12 showing consistent and strong association with increasing endometriosis risk. Fine mapping using sequence and imputed genotype data has revealed strong candidates for the causal SNPs within these critical regions; however, the molecular pathogenesis of these SNPs is currently unknown. We used gene expression data collected from whole blood from 862 individuals and endometrial tissue from 136 individuals from independent populations of European descent to examine the mechanism underlying endometriosis susceptibility. Association mapping results from 7,090 individuals (2,594 cases and 4,496 controls) supported rs3820282 as the SNP with the strongest association for endometriosis risk (P = 1.84 × 10−5, OR = 1.244 (1.126-1.375)). SNP rs3820282 is a significant eQTL in whole blood decreasing expression of LINC00339 (also known as HSPC157) and increasing expression of CDC42 (P = 2.0 ×10−54 and 4.5x10−4 respectively). The largest effects were for two LINC00339 probes (P = 2.0 ×10−54; 1.0 × 10−34). The eQTL for LINC00339 was also observed in endometrial tissue (P = 2.4 ×10−8) with the same direction of effect for both whole blood and endometrial tissue. There was no evidence for eQTL effects for WNT4. Chromatin conformation capture provides evidence for risk SNPs interacting with the promoters of both LINC00339 and CDC4 and luciferase reporter assays suggest the risk SNP rs12038474 is located in a transcriptional silencer for CDC42 and the risk allele increases expression of CDC42. However, no effect of rs3820282 was observed in the LINC00339 expression in Ishikawa cells. Taken together, our results suggest that SNPs increasing endometriosis risk in this region act through CDC42, but further functional studies are required to rule out inverse regulation of both LINC00339 and CDC42.

Shared genetic control of expression and methylation in peripheral blood.

BackgroundExpression QTLs and epigenetic marks are often employed to provide an insight into the possible biological mechanisms behind GWAS hits. A substantial proportion of the variation in gene expression and DNA methylation is known to be under genetic control. We address the proportion of genetic control that is shared between these two genomic features.ResultsAn exhaustive search for pairwise phenotypic correlations between gene expression and DNA methylation in samples from human blood (n = 610) was performed. Of the 5 × 109 possible pairwise tests, 0.36 % passed Bonferroni corrected p-value cutoff of 9.9 × 10-12. We determined that the correlation structure between probe pairs was largely due to blood cell type specificity of the expression and methylation probes. Upon adjustment of the expression and methylation values for observed blood cellular composition (n = 422), the number of probe pairs which survived Bonferroni correction reduced by more than 5400 fold. Of the 614 correlated probe pairs located on the same chromosome, 75 % share at least one methylation and expression QTL at nominal 10-5p-value cutoff. Those probe pairs are located within 1Mbp window from each other and have a mean of absolute value of genetic correlation equal to 0.69, further demonstrating the high degree of shared genetic control.ConclusionsOverall, this study demonstrates notable genetic covariance between DNA methylation and gene expression and reaffirms the importance of correcting for cell-counts in studies on non-homogeneous tissues.

Seasonal effects on gene expression.

Many health conditions, ranging from psychiatric disorders to cardiovascular disease, display notable seasonal variation in severity and onset. In order to understand the molecular processes underlying this phenomenon, we have examined seasonal variation in the transcriptome of 606 healthy individuals. We show that 74 transcripts associated with a 12-month seasonal cycle were enriched for processes involved in DNA repair and binding. An additional 94 transcripts demonstrated significant seasonal variability that was largely influenced by blood cell count levels. These transcripts were enriched for immune function, protein production, and specific cellular markers for lymphocytes. Accordingly, cell counts for erythrocytes, platelets, neutrophils, monocytes, and CD19 cells demonstrated significant association with a 12-month seasonal cycle. These results demonstrate that seasonal variation is an important environmental regulator of gene expression and blood cell composition. Notable changes in leukocyte counts and genes involved in immune function indicate that immune cell physiology varies throughout the year in healthy individuals.

Hemani et al. reply.

Replying to A. R. Wood et al. 514, http://dx.doi.org/10.1038/nature13691 (2014).We thank Wood et al. for their interesting observations and although their proposed mechanism does not explain all our reported results, we acknowledge that alternative mechanisms could be behind the observation of epistatic signals. Although we replicate our results in large, independent samples, 19/30 of our reported interactions (Table 1 in ref. 2), Wood et al. do not replicate in the InCHIANTI data set (n = 450) at a type-I error rate of 0.05/30 = 0.002, including none of our reported cis-trans interactions. Having insufficient data to replicate the discovery interactions makes it problematic to draw firm conclusions on the reported cis-trans effects.

Another Explanation for Apparent Epistasis

Replying to A. R. Wood et al. 514, http://dx.doi.org/10.1038/nature13691 (2014).We thank Wood et al. for their interesting observations and although their proposed mechanism does not explain all our reported results, we acknowledge that alternative mechanisms could be behind the observation of epistatic signals. Although we replicate our results in large, independent samples, 19/30 of our reported interactions (Table 1 in ref. 2), Wood et al. do not replicate in the InCHIANTI data set (n = 450) at a type-I error rate of 0.05/30 = 0.002, including none of our reported cis-trans interactions. Having insufficient data to replicate the discovery interactions makes it problematic to draw firm conclusions on the reported cis-trans effects.

Another explanation for apparent epistasis: reply

Replying to A. R. Wood et al. 514, http://dx.doi.org/10.1038/nature13691 (2014).We thank Wood et al. for their interesting observations and although their proposed mechanism does not explain all our reported results, we acknowledge that alternative mechanisms could be behind the observation of epistatic signals. Although we replicate our results in large, independent samples, 19/30 of our reported interactions (Table 1 in ref. 2), Wood et al. do not replicate in the InCHIANTI data set (n = 450) at a type-I error rate of 0.05/30 = 0.002, including none of our reported cis-trans interactions. Having insufficient data to replicate the discovery interactions makes it problematic to draw firm conclusions on the reported cis-trans effects.