Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Brumpton, Ben; Sanderson, Eleanor; Hartwig, Fernando Pires; Harrison, Sean; Vie, Gunnhild Åberge; Cho, Yoonsu; Howe, Laura D; Hughes, Amanda; Boomsma, Dorret I.; Havdahl, Alexandra; Hopper, John L.; Neale, Michael C.; Nivard, Michel G.; Pedersen, Nancy L.; Reynolds, Chandra A.; Tucker‐Drob, Elliot M.; Grotzinger, Andrew D.; Morris, Tim; Li, Shuai; Chen, Weimin; Bjørngaard, Johan Håkon; Hveem, Kristian; Willer, Cristen J.; Evans, David M.; Kaprio, Jaakko; Smith, George Davey; Åsvold, Bjørn Olav; Hemani, Gibran; Davies, Neil M

doi:10.1101/602516

Cited by 78 publications

(112 citation statements)

References 72 publications

(96 reference statements)

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“…These biases can lead to social and family differences being masked as genetic differences between individuals, inflating associations between polygenic scores and educational attainment in between individual analyses. Family data are required to further investigate the impact of these baises 35 .…”

Section: Discussionmentioning

confidence: 99%

Can education be personalised using pupils’ genetic data?

Morris

Davies

Smith

2019

Preprint

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The predictive power of polygenic scores for some traits now rivals that of more classical phenotypic measures, and as such they have been promoted as a potential tool for genetically informed policy. However, how predictive polygenic scores are conditional on other easily available phenotypic data is not well understood. Using data from a UK cohort study, the Avon Longitudinal Study of Parents and Children, we investigated how well polygenic scores for education predict individuals' realised attainment over and above phenotypic data available to schools. Across our sample children's polygenic scores predicted their educational outcomes almost as well as parent's socioeconomic position or education. There was high overlap between the polygenic score and attainment distributions, leading to weak predictive accuracy at the individual level. Furthermore, conditional on prior attainment the polygenic score was not predictive of later attainment. Our results suggest that polygenic scores are informative for identifying group level differences, but they currently have limited use in predicting individual attainment.

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Section: Discussionmentioning

confidence: 99%

Can education be personalised using pupils’ genetic data?

Morris

Davies

Smith

2019

Preprint

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“…Siblings were identified in the UK Biobank by using data on kinship taken from the KING toolset and data on the proportion of loci shared between individuals. More details are available in Brumpton et al (80). We restricted analysis to the IVW estimator.…”

Section: Within-family Mendelian Randomizationmentioning

confidence: 99%

The causal effect of adiposity on hospital costs: Mendelian Randomization analysis of over 300,000 individuals from the UK Biobank

Dixon¹,

Hollingworth²,

Harrison³

et al. 2019

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Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Many existing estimates of this association are affected by endogeneity bias caused by simultaneity, measurement error and omitted variables. The contribution of this study is to avoid this bias by using a novel identification strategy -random germline genetic variation in an instrumental variable analysis -to identify the presence and magnitude of the causal effect of BMI on inpatient hospital costs. We also use data on genetic variants to undertake much richer testing of the sensitivity of results to potential violations of the instrumental variable assumptions than is possible with existing approaches. Using data on over 300,000 individuals, we found effect sizes for the marginal unit of BMI more than 50% larger than multivariable effect sizes. These effects attenuated under sensitivity analyses, but remained larger than multivariable estimates for all but one estimator. There was little evidence for non-linear effects of BMI on hospital costs. Within-family estimates, intended to address dynastic biases, were null but suffered from low power. This paper is the first to use genetic variants in a Mendelian Randomization framework to estimate the causal effect of BMI (or any other disease/trait) on healthcare costs. This type of analysis can be used to inform the cost-effectiveness of interventions and policies targeting the prevention and treatment of overweight and obesity, and for setting research priorities.

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“…The presence of dynastic effects can be tested with data on mother-father-offspring trios or siblings. 47 Using polygenic scores, the raw association between offspring genotype and phenotype can be compared with its association when adjusted for mother and father genotype. Attenuation of the raw association and direct (conditional) association between parental genotype and offspring phenotype supports an indirect effect of parental genotype on offspring phenotype and therefore the presence of dynastic effects.…”

Section: The Causes Of Genetic Associations: Mechanisms and Tools To mentioning

confidence: 99%

“…34,37,38 Awareness of these issues is also important for interpreting Mendelian randomization studies. 47 Social phenotypes such as educational attainment and SEP, which are complex, highly assortative and dynastic, appear to be particularly susceptible to these factors. It is therefore important that studies within the rapidly growing area of sociogenomic research 58 are account for these biases.…”

Section: Negative Confoundingmentioning

confidence: 99%

Why are education, socioeconomic position and intelligence genetically correlated?

Morris

Davies

Hemani

et al. 2019

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Genetic associations and correlations are perceived as confirmation that genotype influences one or more phenotypes respectively. However, genetic correlations can arise from non-biological or indirect mechanisms including population stratification, dynastic effects, and assortative mating. In this paper, we outline these mechanisms and demonstrate available tools and analytic methods that can be used to assess their presence in estimates of genetic correlations and genetic associations. Using educational attainment and parental socioeconomic position data as an exemplar, we demonstrate that both heritability and genetic correlation estimates may be inflated by these indirect mechanisms. The results highlight the limitations of between-individual estimates obtained from samples of unrelated individuals and the potential value of family-based studies. Use of the highlighted tools in combination with within-sibling or mother-father-offspring trio data may offer researchers greater opportunity to explore the underlying mechanisms behind genetic associations and correlations and identify the underlying causes of estimate inflation.

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Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Cited by 78 publications

References 72 publications

Can education be personalised using pupils’ genetic data?

Can education be personalised using pupils’ genetic data?

The causal effect of adiposity on hospital costs: Mendelian Randomization analysis of over 300,000 individuals from the UK Biobank

Why are education, socioeconomic position and intelligence genetically correlated?

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