Genetic evidence of assortative mating in humans

Robinson, Matthew R.; Kleinman, Aaron; Graff, Mariaelisa; Vinkhuyzen, Anna A. E.; Couper, David; Miller, Michael B.; Peyrot, Wouter J.; Abdellaoui, Abdel; Zietsch, Brendan P.; Nolte, Ilja M.; Vliet-Ostaptchouk, Jana V. van; Snieder, Harold; Medland, Sarah E.; Martin, Nicholas G.; Magnusson, Patrik K. E.; Iacono, William G.; McGue, Matt; North, Kari E.; Yang, Jian; Visscher, Peter M.

doi:10.1038/s41562-016-0016

Cited by 293 publications

(305 citation statements)

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“…The tendency for individuals of a similar height and weight to mate more frequently than expected by chance has been described in the literature18, and may account for some of the observed variation; however, this factor is highly unlikely to account for the 34% increase in the BMI SD. An important hypothesis to explain the increasing individual-level inequalities is the contribution of genetic factors to explain the increasing individual-level inequalities.…”

Section: Discussionmentioning

confidence: 85%

Change in the distribution of body mass index in Brazil: analysing the interindividual inequality between 1974 and 2013

Wagner

Boing

Cembranel

et al. 2019

J Epidemiol Community Health

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BackgroundBrazil is among the 10 countries with the highest prevalence of overweight in the world. The aim was to analyse the changes in the dispersion of body mass index (BMI) in the Brazilian population from the 1970s to 2013 in specific population groups.MethodsData were extracted from five national household surveys between 1974–1975 and 2013, including adults aged 20 to 64. We calculated SD, median, fifth and 95th percentiles of BMI for each sociodemographic category (sex, age, schooling) and survey year in order to explore whether changes in mean BMI are followed by changes in dispersion.ResultsDuring the period the mean BMI ranged from 22.7 kg/m2 to 26.6 kg/m2, with a much higher variation in the 95th percentile (+6.1 kg/m2) when compared with the fifth percentile (+1.8 kg/m2). The within-group differences increased over time. The SD increased in all categories analysed and was higher among women, lower schooling groups and the oldest group. An increase of 1.0 kg/m2 in the BMI mean was associated with an increase of 0.32 kg/m2 in the SD, 0.45 kg/m2 in the fifth percentile and 1.50 kg/m2 in the 95th percentile of BMI.ConclusionsPopulation changes occurred in BMI are more complex than the simple increase of its average. Concomitant to the increase in BMI, there was an increase in the within-group differences, showing that growing inequalities are not driven solely by sociodemographic factors.

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Section: Discussionmentioning

confidence: 85%

Change in the distribution of body mass index in Brazil: analysing the interindividual inequality between 1974 and 2013

Wagner

Boing

Cembranel

et al. 2019

J Epidemiol Community Health

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“…Such indirect genetic effects, termed ‘genetic nurture’ or ‘environmental bias’, have recently been demonstrated to upwardly bias GCTA heritability estimates of educational attainment, because the methods used here are unable to distinguish between direct and indirect genetic effects (Kong et al ., ; Young et al ., ). Our heritability estimates for educational attainment point scores will also be susceptible to bias by assortative mating, whereby parents non‐randomly select partners based upon level of education, as demonstrated by previous work (Robinson et al ., ). There may also be unobserved differences between individuals (residual population structure) biasing our results; we attempted to account for this by using the first 20 principle components of population structure, however we cannot be certain that these will correct for all differences.…”

Section: Discussionmentioning

confidence: 99%

Testing the validity of value‐added measures of educational progress with genetic data

Morris

Davies

Dorling

et al. 2018

British Educational Res J

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Value‐added measures of educational progress have been used by education researchers and policy‐makers to assess the performance of teachers and schools, contributing to performance‐related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value‐added measures control for genetic differences between pupils. We use raw value‐added, contextual value‐added (which additionally controls for background characteristics) and teacher‐reported value‐added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value‐added measures but explain up to 20% of the variation in contextual value‐added measures (95% CI = 6.06% to 35.71%). Value‐added measures built from teacher‐rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross‐sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value‐added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.

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“…Another strategy would be to use data of genetic variant(s) known to associate with a given phenotype and test their association with a second phenotype between spouses. This strategy detected a positive association between a height allele score in women and education of their male spouses (Carslake D et al., ), as well as provided evidence for assortative mating involving height, educational attainment, and other phenotypes (Robinson et al., ). However, this strategy may be prone to other biases.…”

Section: Discussionmentioning

confidence: 99%

Bias in Mendelian randomization due to assortative mating

Hartwig

Davies

Smith

2018

Genetic Epidemiology

115

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Mendelian randomization (MR) has been increasingly used to strengthen causal inference in observational epidemiology. Methodological developments in the field allow detecting and/or adjusting for different potential sources of bias, mainly bias due to horizontal pleiotropy (or “off‐target” genetic effects). Another potential source of bias is nonrandom matching between spouses (i.e., assortative mating). In this study, we performed simulations to investigate the bias caused in MR by assortative mating. We found that bias can arise due to either cross‐trait assortative mating (i.e., assortment on two phenotypes, such as highly educated women selecting taller men) or single‐trait assortative mating (i.e., assortment on a single phenotype), even if the exposure and outcome phenotypes are not the phenotypes under assortment. The simulations also indicate that bias due to assortative mating accumulates over generations and that MR methods robust to horizontal pleiotropy are also affected by this bias. Finally, we show that genetic data from mother–father–offspring trios can be used to detect and correct for this bias.

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Genetic evidence of assortative mating in humans

Cited by 293 publications

References 50 publications

Change in the distribution of body mass index in Brazil: analysing the interindividual inequality between 1974 and 2013

Change in the distribution of body mass index in Brazil: analysing the interindividual inequality between 1974 and 2013

Testing the validity of value‐added measures of educational progress with genetic data

Bias in Mendelian randomization due to assortative mating

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