scholarly journals Why are education, socioeconomic position and intelligence genetically correlated?

2019 ◽  
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.

scholarly journals Population phenomena inflate genetic associations of complex social traits

2020 ◽  
Vol 6 (16) ◽  
pp. eaay0328 ◽  
Author(s):  
Tim T. Morris ◽  
Neil M. Davies ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Genetic Correlation ◽  
Assortative Mating ◽  
Socioeconomic Position ◽  
Population Stratification ◽  
Educational Achievement ◽  
Genetic Associations ◽  
Social Traits ◽  
Family Based ◽  
Using Data ◽  
Biased Estimates

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.

Genetic parameters for calf mortality and correlated cow and calf traits in tropically adapted beef breeds managed in extensive Australian production systems

2014 ◽  
Vol 54 (1) ◽  
pp. 50 ◽  
Author(s):  
Kim L. Bunter ◽  
David J. Johnston
Keyword(s):  
Genetic Correlation ◽  
Maternal Effects ◽  
Production Systems ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Repeated Measure ◽  
Correlated Response ◽  
Genetic Associations ◽  
Calf Survival ◽  
Calf Mortality

The genetic associations between cow teat and udder traits with maternal contributions to calf mortality were studied in Brahman (BRAH) and Tropical Composite (TCOMP) cattle managed in extensive production systems of northern Australia. Data from 9286 purebred and crossbred calves, progeny of 2076 cows and 149 sires, were recorded from 2003 to 2011. Calf weights at birth (BWT) and weaning (WWT) were routinely recorded. The event of calf death before weaning (DWEAN) was analysed as a repeated-measure of the cow. Cows were also scored at each calving for front- and back-teat size and udder size (US) on an ascending five-point scale. Heritabilities for front-teat size, back-teat size and US were 0.38 ± 0.05, 0.31 ± 0.05 and 0.49 ± 0.01, and estimates were the same for BRAH and TCOMP. The heritability of DWEAN was higher in BRAH (0.09 ± 0.02) than in TCOMP (0.02 ± 0.01). Variance ratios for maternal genetic effects contributing to variation in BWT and WWT were 0.13 ± 0.02 and 0.18 ± 0.05, and tended to be larger in TCOMP than in BRAH. Teat and udder scores were moderately correlated phenotypically (0.37 ± 0.01) and genetically (0.53 ± 0.04) with each other. Both traits were uncorrelated genetically with calf birthweight but positively correlated with WWT and DWEAN. The genetic correlation between average teat score at calving and DWEAN was larger (0.54 ± 0.05) than that between US and DWEAN (0.33 ± 0.06), whereas the genetic correlation between US and maternal effects for WWT was larger (0.60 ± 0.08) than the corresponding value for average teat score with maternal WWT (0.37 ± 0.13). Correlations between BWT and WWT were high for both direct (0.63 ± 0.07) and maternal (0.50 ± 0.09) genetic effects. Genetic correlations between maternal effects for BWT or WWT with DWEAN were both negative (–0.23 ± 0.10 and –0.21 ± 0.04), while the correlation between BWT and WWT for maternal effects was positive (0.54 ± 0.09), showing that larger calves at birth are less likely to die before weaning and have heavier weaning weights from maternal genetic contributions to these traits. Selection on maternal components of BWT and WWT should be accompanied by recording for teat and udder characteristics to assist in preventing any undesired correlated response in teat or udder size, which can have detrimental outcomes for calf survival, despite expectations of higher milk yield.

Genetic and Environmental Influences on Executive Functions and Intelligence in the ABCD Study

2020 ◽  
Author(s):  
Samantha M Freis ◽  
Claire Morrison ◽  
Jeffrey M. Lessem ◽  
John K. Hewitt ◽  
Naomi P. Friedman
Keyword(s):  
Cognitive Development ◽  
Executive Functions ◽  
Genetic Correlation ◽  
Middle Childhood ◽  
Middle Age ◽  
Environmental Influences ◽  
Genetic Correlations ◽  
Twin Studies

Executive functions (EFs) and intelligence (IQ) are phenotypically correlated and heritable; however, they show variable genetic correlations in twin studies spanning childhood to middle age. We analyzed data from over 11,000 children (9-10-year-olds, including 749 twin pairs) in the Adolescent Brain Cognitive Development (ABCD) Study to examine the phenotypic and genetic relations between EFs and IQ in childhood. We identified two EF factors – Common EF and Updating-Specific, which were both related to IQ (rs = .64-.81). Common EF and IQ were heritable (53-67%), and their genetic correlation (rG = .86) was not significantly different than 1. These results suggest that EFs and IQ are phenotypically but not genetically separable in middle childhood.

Shared genetic architecture across psychiatric disorders

2021 ◽  
pp. 1-7
Author(s):  
Andrew D. Grotzinger
Keyword(s):  
Risk Factors ◽  
Psychiatric Disorders ◽  
Genetic Correlations ◽  
Fine Grained ◽  
Level Data ◽  
Symptom Level ◽  
Genetic Level ◽  
Genetic Signal ◽  
Family Based ◽  
Shared Genetic

Abstract Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

Breeding for improved growth and juvenile corewood stiffness in slash pine

2007 ◽  
Vol 37 (10) ◽  
pp. 1886-1893 ◽  
Author(s):  
Xiaobo Li ◽  
Dudley A. Huber ◽  
Gregory L. Powell ◽  
Timothy L. White ◽  
Gary F. Peter
Keyword(s):  
Genetic Correlation ◽  
Growth Traits ◽  
Low Cost ◽  
Genetic Correlations ◽  
Volume Growth ◽  
Pinus Elliottii ◽  
Tree Improvement ◽  
Slash Pine ◽  
Narrow Sense Heritability ◽  
Improvement Programs

The importance of integrating measures of juvenile corewood mechanical properties, modulus of elasticity in particular, with growth and disease resistance in tree improvement programs has increased. We investigated the utility of in-tree velocity stiffness measurements to estimate the genetic control of corewood stiffness and to select for trees with superior growth and stiffness in a progeny trial of 139 families of slash pine, Pinus elliottii Engelm. grown on six sites. Narrow-sense heritability estimates across all six sites for in-tree acoustic velocity stiffness at 8 years (0.42) were higher than observed for height (0.36) and diameter at breast height (DBH) (0.28) at 5 years. The overall type B genetic correlation across sites for velocity stiffness was 0.68, comparable to those found for DBH and volume growth, indicating that family rankings were moderately repeatable across all sites for these traits. No significant genetic correlations were observed between velocity stiffness, DBH, and volume growth. In contrast, a significant, but small, favorable genetic correlation was found between height and velocity stiffness. Twenty percent of the families had positive breeding values for both velocity stiffness and growth. The low cost, high heritability and nearly independent segregation of the genes involved with in-tree velocity stiffness and growth traits indicate that acoustic methods can be integrated into tree improvement programs to breed for improved corewood stiffness along with growth in slash pine.

A Proteome-Wide Association Study Identified Candidate Human Brain Proteins Associated with Coffee Consumption

2021 ◽  
Author(s):  
Chun'e Li ◽  
Xiao Liang ◽  
Yumeng Jia ◽  
Yan Wen ◽  
Huijie Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
Association Study ◽  
Plasma Proteins ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Coffee Consumption ◽  
Genetic Associations ◽  
Brain Proteins ◽  
Genome Wide ◽  
Brain Proteome

Abstract Background Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites. Methods First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites. Results For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216). Conclusions We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.

Quantitative study of genetic gain for growth, carcass, and morphological traits of Nelore cattle

2019 ◽  
Vol 99 (2) ◽  
pp. 296-306
Author(s):  
Daniel Duarte da Silveira ◽  
Lucas De Vargas ◽  
Rodrigo Junqueira Pereira ◽  
Gabriel Soares Campos ◽  
Ricardo Zambarda Vaz ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Carcass Traits ◽  
Genetic Correlations ◽  
Correlated Response ◽  
Genetic Associations ◽  
Muscle Area ◽  
Genetic Gains ◽  
Fat Thickness ◽  
Nelore Cattle ◽  
Genetic Trends

The aim of this study was to evaluate the genetic variability, genetic and phenotypic associations, and genetic gains of birth (BW), weaning (WW), and yearling (YW) weights, loin muscle area (LMA), backfat thickness (BF), rump fat thickness (RF), scores of body structure (BS), finishing precocity (FS), and muscling (MS) in Nelore cattle. Genetic parameters were obtained through Bayesian inference using BLUPF90 programs. All studied traits showed genetic variability, with heritability ranging from 0.29 to 0.47. In all studied ages, weights presented positive genetic correlations with LMA (ranging from 0.13 to 0.53), being generally stronger in comparison with the other carcass traits analyzed (BF and RF). Similarly, weights were higher genetic associated with BS (0.47–0.92) than with FS (0.18–0.62) and MS (0.22–0.65), respectively. The BF and RF showed positive and moderate genetic associations with FS and MS (0.31–0.36). Genetic trends were significant (P < 0.05) and favorable for WW, YW, and visual scores. Selection for increasing BW, WW, YW, and LMA will result in modest or no change in BF and RF (correlated response ranging from −0.04 to 0.07 mm per generation). In this population, carcass traits must be included in the selection indexes to obtain genetic gains in carcass quality, if desired.

An analysis of the variation in the lean tissue distribution of sheep

1982 ◽  
Vol 34 (3) ◽  
pp. 257-264 ◽  
Author(s):  
B. T. Wolf
Keyword(s):  
Tissue Distribution ◽  
Genetic Correlation ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Maximum Difference ◽  
Lean Tissue ◽  
Heritability Estimates ◽  
The Relationship ◽  
Daily Gain ◽  
Sire Breed

ABSTRACTThe distribution of lean tissue between eight standard joints was examined in 956 crossbred lambs slaughtered at constant live weights of either 35 or 40 kg. The sire breeds used were the Dorset Down, Ile-de-France, Oldenburg, Oxford, Suffolk and Texel. Sire breed did not have a significant effect on the proportion of total carcass lean found in the higher-priced joints but did show significant differences in the proportion of total carcass lean found in individual joints, with a maximum difference of 7·7 g total lean per kg joint being recorded. Similarly, small but significant effects due to ewe age (1 to 3 years), rearing type (single, twin, triplet), sex (male castrate, female) and weight of total lean were reported for the proportion of total carcass lean found in different joints.Heritability estimates ranged from 0·07 (s.e. 008) to 0·65 (s.e. 0·16) for the proportion of total lean in the best-end neck and higher-priced joints respectively. Phenotypic standard deviations of 5·8g/kg and 17·9g/kg were reported for the proportion of total lean found in the best-end neck and the higher-priced joints respectively. The genetic correlations between the proportion of total lean in each of the higher-priced joints and the proportion of total lean in the higher-priced joints combined were positive. A genetic correlation of 017 (s.e. 0·20) was found for the relationship between average daily gain from birth to slaughter and the proportion of total lean in the higher-priced joints.

HERITABILITIES AND GENETIC CORRELATIONS FOR ULTRASONIC BACKFAT MEASUREMENTS, GROWTH AND CARCASS TRAITS IN SWINE

1982 ◽  
Vol 62 (3) ◽  
pp. 665-670 ◽  
Author(s):  
D. C. JEFFRIES ◽  
R. G. PETERSON
Keyword(s):  
Key Words ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Ultrasonic Measurements ◽  
Yorkshire Pigs ◽  
Daily Gain ◽  
Season Interaction

Genetic parameters were estimated for 2403 purebred Yorkshire pigs over a 2-yr period, representing 21 sires. The traits studied included average daily gain, age adjusted to 90 kg, ultrasonic measurements of backfat at the mid-back and loin positions, total and adjusted total ultrasonic backfat and corresponding carcass backfat measurements. Least squares analyses were used to estimate and adjust for the effects of sex, year-season and sex by year-season interaction. Heritabilities and genetic correlations were calculated for all traits using both half- and full-sib estimates. Adjusted age and adjusted total ultrasonic backfat measurements were found to have the highest heritabilities of the live traits in this study. Estimates of heritability for adjusted age and adjusted total ultrasonic backfat were 0.24 ± 0.10 and 0.26 ± 0.10 based on half-sib and 0.56 ± 0.07 and 0.41 ± 0.06 from full-sib analyses. The genetic correlation between these two traits was −0.07 ± 0.28 based on the half-sib method. The total phenotypic correlation was −0.01 ± 0.02. Key words: Swine, ultrasonic backfat, heritabilities, genetic correlations

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