scholarly journals Within family Mendelian randomization studies

2019 ◽  
Vol 28 (R2) ◽  
pp. R170-R179 ◽  
Author(s):  
Neil M Davies ◽  
Laurence J Howe ◽  
Ben Brumpton ◽  
Alexandra Havdahl ◽  
David M Evans ◽  
...  

Abstract Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP–phenotype associations. We explain how studies of related individuals such as sibling pairs or parent–offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

2020 ◽  
Vol 49 (4) ◽  
pp. 1259-1269
Author(s):  
Shuai Li ◽  
Minh Bui ◽  
John L Hopper

Abstract Background We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable. Methods We describe ICE FALCON and present two empirical applications with simulations. Results We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON. Conclusions There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.


2006 ◽  
Vol 14 (3) ◽  
pp. 299-306 ◽  
Author(s):  
Nicola L Dean ◽  
J Concepción Loredo-Osti ◽  
T Mary Fujiwara ◽  
Kenneth Morgan ◽  
Seang Lin Tan ◽  
...  

Genetics ◽  
1996 ◽  
Vol 142 (4) ◽  
pp. 1299-1304
Author(s):  
F Pardo-Manuel de Villena ◽  
C Slamka ◽  
M Fonseca ◽  
A K Naumova ◽  
J Paquette ◽  
...  

Abstract We determined the genotypes of >200 offspring that are survivors of matings between female reciprocal F1 hybrids (between the DDK and C57BL/6J inbred mouse strains) and C57BL/6J males at markers linked to the Ovum mutant (Om) locus on chromosome 11. In contrast to the expectations of our previous genetic model to explain the “DDK syndrome,” the genotypes of these offspring do not reflect preferential survival of individuals that receive C57BL/6J alleles from the F1 females in the region of chromosome 11 to which the Om locus has been mapped. In fact, we observe significant transmission-ratio distortion in favor of DDK alleles in this region. These results are also in contrast to the expectations of Wakasugi's genetic model for the inheritance of Om, in which he proposed equal transmission of DDK and non-DDK alleles from F1 females. We propose that the results of these experiments may be explained by reduced expression of the maternal DDK Om allele or expression of the maternal DDK Om allele in only a portion of the ova of F1 females


2006 ◽  
Vol 17 (3) ◽  
pp. 239-247 ◽  
Author(s):  
Reiner Schulz ◽  
Lara A. Underkoffler ◽  
Joelle N. Collins ◽  
Rebecca J. Oakey

2021 ◽  
Author(s):  
Vivian Zayas ◽  
Randy T. Lee ◽  
Yuichi Shoda

People’s behavior is characterized by stable if…then… profiles, or if in x situation then behavior a, but if in y situation then behavior b. But how do researchers conceptualize and measure if…then… profiles? Drawing from Cognitive-Affective Processing System (CAPS) theory, we discuss recent developments in assessing if…then… profiles, and how such profiles can provide a window for elucidating key aspects of the underlying personality system. Specifically, the Highly-Repeated Within-Person (HRWP) approach assesses how a behavior varies as a function of key features in a situation, and operationalizes if…then… profiles as regression betas. We illustrate how the HRWP approach can be applied to data from often-used social cognitive tasks, wherein an individual is exposed to a large number of situations that differ on a dimension that has been experimentally-manipulated by the researcher, and their behaviors to the situations are tracked. The HRWP approach allows researchers to more precisely assess a given individual’s if…then… pattern, make stronger causal inferences about a given individual’s personality system, and empirically investigate, rather than simply assume, if there are meaningful differences between individuals in the causal processes.


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