Population phenomena inflate genetic associations of complex social traits

Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates.

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Why are education, socioeconomic position and intelligence genetically correlated?

10.1101/630426 ◽

2019 ◽

Cited By ~ 5

Author(s):

Tim T Morris ◽

Neil M Davies ◽

Gibran Hemani ◽

George Davey Smith

Keyword(s):

Genetic Correlation ◽

Socioeconomic Position ◽

Population Stratification ◽

Genetic Correlations ◽

Genetic Associations ◽

Potential Value ◽

Position Data ◽

Underlying Causes ◽

Family Based ◽

Underlying Mechanisms

AbstractGenetic 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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Comparing within- and between-family polygenic score prediction

10.1101/605006 ◽

2019 ◽

Cited By ~ 4

Author(s):

Saskia Selzam ◽

Stuart J. Ritchie ◽

Jean-Baptiste Pingault ◽

Chandra A. Reynolds ◽

Paul F. O’Reilly ◽

...

Keyword(s):

Assortative Mating ◽

Complex Traits ◽

Population Stratification ◽

Educational Achievement ◽

Economic Status ◽

Life Outcomes ◽

Polygenic Score ◽

Family Effects ◽

Polygenic Scores ◽

Cognitive Traits

AbstractPolygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight life outcomes (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modelling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a source of between-family prediction through rGE mechanisms. These results provide novel insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

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

10.1101/602516 ◽

2019 ◽

Cited By ~ 30

Author(s):

Ben Brumpton ◽

Eleanor Sanderson ◽

Fernando Pires Hartwig ◽

Sean Harrison ◽

Gunnhild Åberge Vie ◽

...

Keyword(s):

Population Stratification ◽

Mendelian Randomization ◽

Genetic Data ◽

Population Based ◽

Health Study ◽

Uk Biobank ◽

Family Effects ◽

Family Based ◽

Using Data ◽

The Uk

AbstractMendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

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Personality and Genetic Associations With Military Service

Armed Forces & Society ◽

10.1177/0095327x18765449 ◽

2018 ◽

Vol 45 (4) ◽

pp. 637-658 ◽

Cited By ~ 2

Author(s):

Matthew R. Miles ◽

Donald P. Haider-Markel

Keyword(s):

Political Participation ◽

Personality Traits ◽

Genetic Correlation ◽

Military Service ◽

Genetic Associations ◽

Political Ambition ◽

Policy Makers ◽

Significant Heritability ◽

The Military

Existing literature connects military service to regional characteristics and family traditions, creating real distinctions between those who serve and those who do not. We engage this discussion by examining military service as a function of personality. In the second portion, we examine military service as predisposed by genetics. Our findings indicate there is a significant heritability component of serving in the military. We find a significant genetic correlation between personality traits associated with progressive political ambition and military service, suggesting that military service represents a different form of political participation to which individuals are genetically predisposed. We discuss the long-term implications of our findings for policy makers and recruiters.

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Cumulative Risk and Immigrant Youth’s Health and Educational Achievement: Mediating Effects of Inter- and Intra-Familial Social Capital

Youth & Society ◽

10.1177/0044118x17717501 ◽

2017 ◽

Vol 51 (6) ◽

pp. 793-813

Author(s):

Jing Zhang ◽

Jyoti Savla ◽

Hsiu-Lan Cheng

Keyword(s):

Social Capital ◽

Young Adulthood ◽

Family Cohesion ◽

Educational Achievement ◽

Cumulative Risk ◽

Immigrant Parents ◽

School Involvement ◽

Mediating Effects ◽

Parental School Involvement ◽

Using Data

Using data from Children of Immigrants Longitudinal Study, this study examined the longitudinal effects of cumulative risk of immigrant parents on immigrant youth’s health and educational achievement in young adulthood. The mediating effects of intra- (i.e., family cohesion) and inter-familial (i.e., parental school involvement) social capital were also examined. The findings showed that cumulative risk was negatively associated with youth’s health and educational achievement in young adulthood. In addition, parental school involvement mediated the association between cumulative risk and youth’s health and educational achievement. The findings suggest that inter-familial social connections may be a critical intervention target for immigrant youth preventive interventions.

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High Resolution Haplotype Analyses of Classical HLA Genes in Families With Multiple Sclerosis Highlights the Role of HLA-DP Alleles in Disease Susceptibility

Frontiers in Immunology ◽

10.3389/fimmu.2021.644838 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kazutoyo Osoegawa ◽

Lisa E. Creary ◽

Gonzalo Montero-Martín ◽

Kalyan C. Mallempati ◽

Sridevi Gangavarapu ◽

...

Keyword(s):

Multiple Sclerosis ◽

Statistical Significance ◽

Hla Class I ◽

Protective Effects ◽

Genetic Associations ◽

Hla Alleles ◽

Hla Genes ◽

Haplotype Allele ◽

Family Based ◽

Hla Haplotypes

Multiple sclerosis (MS) susceptibility shows strong genetic associations with HLA alleles and haplotypes. We genotyped 11 HLA genes in 477 non-Hispanic European MS patients and their 954 unaffected parents using a validated next-generation sequencing (NGS) methodology. HLA haplotypes were assigned unequivocally by tracing HLA allele transmissions. We explored HLA haplotype/allele associations with MS using the genotypic transmission disequilibrium test (gTDT) and multiallelic TDT (mTDT). We also conducted a case-control (CC) study with all patients and 2029 healthy unrelated ethnically matched controls. We performed separate analyses of 54 extended multi-case families by reviewing transmission of haplotype blocks. The haplotype fragment including DRB5*01:01:01~DRB1*15:01:01:01 was significantly associated with predisposition (gTDT: p < 2.20e-16; mTDT: p =1.61e-07; CC: p < 2.22e-16) as reported previously. A second risk allele, DPB1*104:01 (gTDT: p = 3.69e-03; mTDT: p = 2.99e-03; CC: p = 1.00e-02), independent from the haplotype bearing DRB1*15:01 was newly identified. The allele DRB1*01:01:01 showed significant protection (gTDT: p = 8.68e-06; mTDT: p = 4.50e-03; CC: p = 1.96e-06). Two DQB1 alleles, DQB1*03:01 (gTDT: p = 2.86e-03; mTDT: p = 5.56e-02; CC: p = 4.08e-05) and DQB1*03:03 (gTDT: p = 1.17e-02; mTDT: p = 1.16e-02; CC: p = 1.21e-02), defined at two-field level also showed protective effects. The HLA class I block, A*02:01:01:01~C*03:04:01:01~B*40:01:02 (gTDT: p = 5.86e-03; mTDT: p = 3.65e-02; CC: p = 9.69e-03) and the alleles B*27:05 (gTDT: p = 6.28e-04; mTDT: p = 2.15e-03; CC: p = 1.47e-02) and B*38:01 (gTDT: p = 3.20e-03; mTDT: p = 6.14e-03; CC: p = 1.70e-02) showed moderately protective effects independently from each other and from the class II associated factors. By comparing statistical significance of 11 HLA loci and 19 haplotype segments with both untruncated and two-field allele names, we precisely mapped MS candidate alleles/haplotypes while eliminating false signals resulting from ‘hitchhiking’ alleles. We assessed genetic burden for the HLA allele/haplotype identified in this study. This family-based study including the highest-resolution of HLA alleles proved to be powerful and efficient for precise identification of HLA genotypes associated with both, susceptibility and protection to development of MS.

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Bias in retrospective analyses of biomarker effect using data from an outcome-adaptive randomized trial

Clinical Trials ◽

10.1177/1740774519875969 ◽

2019 ◽

Vol 16 (6) ◽

pp. 599-609 ◽

Cited By ~ 1

Author(s):

Lingyun Ji ◽

Lisa M McShane ◽

Mark Krailo ◽

Richard Sposto

Keyword(s):

Simulation Studies ◽

Adaptive Randomization ◽

Prognostic Effect ◽

Variance Estimators ◽

Oncology Research ◽

Previously Treated Patients ◽

Previously Treated ◽

Effect Of Treatment ◽

Using Data ◽

Biased Estimates

Background/Aims Biomarker-stratified outcome-adaptive randomization trials, in which randomization probabilities depend on both biomarker value and outcomes of previously treated patients, are receiving increased attention in oncology research. Data from these trials can also form the basis of investigation of additional biomarkers that may not have been incorporated into the original trial design. In this article, we investigate the validity of a standard analytical method that utilizes data from a biomarker-stratified outcome-adaptive randomization trial to assess the effect of a newly identified biomarker on patient outcomes. Methods In the context of an ancillary biomarker study for a two-arm phase II trial with a response endpoint, we conduct analytic and simulation studies to investigate bias in estimated biomarker effects under outcome-adaptive randomization. Conditions under which bias arises and magnitude of the bias are examined in several settings. We then propose unbiased estimators of biomarker effects with appropriate variance estimators. Results We demonstrate that use of biomarker-stratified outcome-adaptive randomization perturbs the patient population and treatment assignments. Consequently, application of standard analysis methods to data from an outcome-adaptive randomization trial either to estimate prognostic effect of a new biomarker in uniformly treated patients or to estimate effect of treatment in relation to the new biomarker can lead to substantially biased estimates. The proposed adjusted estimators are asymptotically unbiased, and the proposed variance estimators correctly reflect the sample variability in the estimators. Conclusion This article demonstrates existence of bias when standard, naïve statistical methods are utilized to assess biomarker effects using data from a biomarker-stratified outcome-adaptive randomization trial, and hence that results from naïve analyses must be interpreted with great caution. These findings highlight that, in an era where data and specimens are increasingly being shared for biomarker studies, care must be taken to document and understand implications of the study design under which specimens or data have been obtained.

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Estimation of direct and maternal heritability and genetic correlation for backfat and growth rate in swine using data from centrally tested Yorkshire boars

Journal of Animal Science ◽

10.2527/1992.7061755x ◽

1992 ◽

Vol 70 (6) ◽

pp. 1755-1759 ◽

Cited By ~ 19

Author(s):

S. M. Bryner ◽

J. W. Mabry ◽

J. K. Bertrand ◽

L. L. Benyshek ◽

L. A. Kriese

Keyword(s):

Growth Rate ◽

Genetic Correlation ◽

Using Data ◽

Maternal Heritability

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Children of the Post-Communist Transition: Age at the Time of the Parents' Job Loss and Dropping Out of Secondary School

The B E Journal of Economic Analysis & Policy ◽

10.2202/1935-1682.1824 ◽

2008 ◽

Vol 7 (2) ◽

Cited By ~ 1

Author(s):

Gabor Kertesi ◽

Gabor Kezdi

Keyword(s):

Secondary School ◽

Capital Investment ◽

Job Loss ◽

Educational Achievement ◽

Causal Effect ◽

Substantial Effect ◽

Preschool Age ◽

Dropping Out ◽

Using Data ◽

Employed Parents

Abstract Using data on children whose parents lost their jobs during the post-communist transition of Hungary, we address the causal effect of unexpected long-term unemployment of parents on their children's educational achievement. We estimate the effect of the children's age at the time of their parents' job loss on their probability of dropping out of secondary school (an event that follows the parents' job loss by many years). The treatment is an additional year reared in a family with regularly employed parents, which can be interpreted as additional human capital investment. We provide bounding estimates to the causal effect. The estimated bounds are tight, they show a substantial effect, and the effect is significantly stronger for preschool age children than for older ones.

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