Mendelian Randomization using Public Data from Genetic Consortia

Thompson, John R.; Minelli, Cosetta; M, Fabiola Del Greco

doi:10.1515/ijb-2015-0074

Cited by 79 publications

(67 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the two approaches address similar questions of identifying the link between height and osteosarcoma, the estimates obtained from the two approaches have different interpretations 50 . The former, an estimate of osteosarcoma risk associated with height-related genetic variants, aims more generally to determine common biological mechanisms between the two phenotypes while the latter, an estimate of osteosarcoma risk due to height, requires that additional assumptions be satisfied for the estimate to be valid.…”

Section: Discussionmentioning

confidence: 99%

Genetic determinants of childhood and adult height associated with osteosarcoma risk

Zhang

Morimoto

Smith

et al. 2018

Cancer

View full text Add to dashboard Cite

Background: Although increased height has been associated with osteosarcoma risk in previous epidemiologic studies, the relative contribution of stature during different developmental time-points remains unclear. Furthermore, how genetic determinants of height impact osteosarcoma etiology remains unexplored. Genetic variants associated with stature in previous genome-wide association studies may be biomarkers of osteosarcoma risk. Methods: We tested the associations between osteosarcoma risk and polygenic scores for adult height (416 variants), childhood height, (six variants), and birth length (five variants) in 864 osteosarcoma patients and 1879 controls of European ancestry. Results: Each standard deviation increase in the polygenic score for adult height, corresponding to a 1.7-cm increase in stature, was associated with a 1.10-fold increase in risk of osteosarcoma (95% CI: 1.01–1.19, P= 0.027). Each standard deviation increase in the polygenic score for childhood height, corresponding to a 0.5-cm increase in stature, was associated with a 1.10-fold increase in risk of osteosarcoma (95% CI: 1.01–1.20, P= 0.023). The polygenic score for birth length was not associated with osteosarcoma risk (P=0.11). When adult and childhood height scores were modeled together, they were independently associated with osteosarcoma risk (P=0.037 and 0.043, respectively). An eQTL for CILP2 (cartilage intermediate layer protein 2), rs8103992, was significantly associated with osteosarcoma risk after adjustment for multiple comparisons (OR=1.35, 95% CI: 1.16–1.56, P=7.93×10−5, Padjusted=0.034). Conclusions: Genetic propensity to taller adult and childhood height attainments contributed independently to osteosarcoma risk in our data. These results suggest that the biological pathways affecting normal bone growth may be involved in osteosarcoma etiology.

show abstract

Section: Discussionmentioning

confidence: 99%

Genetic determinants of childhood and adult height associated with osteosarcoma risk

Zhang

Morimoto

Smith

et al. 2018

Cancer

View full text Add to dashboard Cite

show abstract

“…However, Thompson et al [21] showed that 2nd order weights produce causal estimates which are generally more biased than using 1st order weights. The reason for this apparent paradox is that 2nd order weights can be highly correlated with the ratio estimates themselves.…”

Section: Heterogeneity Assessmentmentioning

confidence: 99%

Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

Bowden

Minelli

et al. 2017

Preprint

Self Cite

148

View full text Add to dashboard Cite

Background: Two-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.

show abstract

“…

\overset{β}{true} = \frac{\sum_{j = 1}^{M} {\overset{β}{true}}_{j} false/ V_{j}}{\sum_{j = 1}^{M} 1 false/ V_{j}} .

When s j , the standard error of

{\overset{γ}{true}}_{j}

, is negligible, the variance, V j , of

{\overset{β}{true}}_{j}

can be estimated by

S_{j}^{2} false/ {\overset{γ}{true}}_{j}^{2}

. The delta method can be used to obtain estimates of V j that allow for the uncertainty in

{\overset{γ}{true}}_{j}

, but these have to be used with care as such estimates are correlated with the estimate of

{\overset{β}{true}}_{j}

and this correlation can introduce its own bias into

\overset{β}{true}

…”

Section: Meta‐analysis Of Variant‐specific Estimatesmentioning

confidence: 99%

“…To harness these data, researchers have started to adopt a two‐sample design in which the variant‐exposure information comes from one GWAS and the variant‐outcome information comes from a second GWAS. () Such data provide valid MR estimates provided that the variant‐exposure relationship is the same in both study populations and, of course, that the other MR assumptions hold for all of the variants. This means that we need to be confident that none of the variants has a pleiotropic effect.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mendelian randomization incorporating uncertainty about pleiotropy

Thompson

Minelli

Bowden

et al. 2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

Mendelian randomization (MR) requires strong assumptions about the genetic instruments, of which the most difficult to justify relate to pleiotropy. In a two-sample MR different methods of analysis are available if we are able to assume, M 1 : no pleiotropy (fixed effects meta-analysis), M 2 : that there may be pleiotropy but that the average pleiotropic effect is zero (random effects meta-analysis), M 3 : that the average pleiotropic effect is non-zero (MR-Egger). In the latter two cases we also require that the size of the pleiotropy is independent of the size of the effect on the exposure. Selecting one of these models without good reason would run the risk of misrepresenting the evidence for causality. The most conservative strategy would be to use M 3 in all analyses as this makes the weakest assumptions, but such an analysis gives much less precise estimates and so should be avoided whenever stronger assumptions are credible. We consider the situation of a two-sample design when we are unsure which of these three pleiotropy models is appropriate. The analysis is placed within a Bayesian framework and Bayesian model averaging (BMA) is used. We demonstrate that even large samples of the scale used in genome-wide meta-analysis may be insufficient to distinguish the pleiotropy models based on the data alone. Our simulations show that BMA provides a reasonable trade-off between bias and precision. BMA is recommended whenever there is uncertainty about the nature of the pleiotropy.

show abstract

Mendelian Randomization using Public Data from Genetic Consortia

Cited by 79 publications

References 25 publications

Genetic determinants of childhood and adult height associated with osteosarcoma risk

Genetic determinants of childhood and adult height associated with osteosarcoma risk

Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

Mendelian randomization incorporating uncertainty about pleiotropy

Contact Info

Product

Resources

About