Genetic correlation

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In multivariate behavioral & quantitative genetics, a genetic correlation (denoted r_g or r_a) is the proportion of variance that two traits share due to genetic causes,[1][2] the correlation between the genetic influences on a trait and the genetic influences on a different trait[3][4][5][6][7][8][9] estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across >2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s-1980s.

Genetic correlations have applications in validation of GWAS results, breeding, prediction of traits, and discovering the etiology of traits & diseases.

They can be estimated using twin studies and molecular genetics. Genetic correlations have been found to be common in non-human genetics[10] and to be broadly similar to their respective phenotypic correlations,[11] but also in human traits.[12][13][14][15][16][17][18][19] This finding of widespread pleiotropy has implications for artificial selection in agriculture, interpretation of phenotypic correlations, social inequality,[20] attempts to use Mendelian randomization in causal inference,[21][22][23][24][25] the understanding of the biological origins of complex traits, and the design of GWASes.

A genetic correlations is to be contrasted with environment correlations between the environments affecting two traits (e.g. if poor nutrition in a household caused both lower IQ and height); a genetic correlation between two traits can contribute to the observed (phenotypic) correlation between two traits, but genetic correlations can also be opposite observed phenotypic correlations if the environment correlation is sufficiently strong in the other direction, perhaps due to tradeoffs or specialization.[26][27]

Interpretation

Genetic correlations are not the same as heritability, as it is about the overlap between the two sets of influences and not their absolute magnitude; two traits could be both highly heritable but not be genetically correlated or have small heritabilities and be completely correlated (as long as the heritabilities are non-zero).

For example, consider two traits - dark skin and black hair. These two traits may individually have a very high heritability (most of the population-level variation in the trait due to genetic differences, or in simpler terms, genetics contributes significantly to these two traits), however, they may still have a very low genetic correlation if, for instance, these two traits were being controlled by different, non-overlapping, non-linked genetic loci.

A genetic correlation between two traits will tend to produce phenotypic correlations - e.g. the genetic correlation between intelligence and SES[14] or education and family SES[28] implies that intelligence/SES will also correlate phenotypically. The phenotypic correlation will be limited by the degree of genetic correlation and also by the heritability of each trait. The expected phenotypic correlation is the bivariate heritability' and can be calculated as the square roots of the heritabilities multiplied by the genetic correlation. (Using a Plomin example,[29] for two traits with heritabilities of 0.60 & 0.23, r_g=0.75, and phenotypic correlation of r=0.45 the bivariate heritability would be \sqrt{0.60} \cdot 0.75 \cdot \sqrt{0.23} = 0.28, so of the observed phenotypic correlation, 0.28/0.45 = 62% of it is due to genetics.)

Cause

Genetic correlations can arise due to:[17]

  1. linkage disequilibrium (two neighboring genes tend to be inherited together, each affecting a different trait)
  2. biological pleiotropy (a single gene having multiple otherwise unrelated biological effects, or shared regulation of multiple genes[30])
  3. mediated pleiotropy (a gene causes trait X and trait X causes trait Y).
  4. biases: population stratification such as ancestry or assortative mating (sometimes called "gametic phase disequilibrium"), spurious stratification such as ascertainment bias/self-selection[31] or Berkson's paradox, or misclassification of diagnoses

Uses

Causes of changes in traits

Genetic correlations are scientifically useful because genetic correlations can be analyzed over time within an individual longitudinally[32] (e.g. intelligence is stable over a lifetime, due to the same genetic influences - childhood genetically correlates r_g=0.62 with old age[33]), or across studies or populations or ethnic groups/races, or across diagnoses, allowing discovery of whether different genes influence a trait over a lifetime (typically, they do not[3]), whether different genes influence a trait in different populations due to differing local environments, whether there is disease heterogeneity across times or places or sex (particularly in psychiatric diagnoses there is uncertainty whether 1 country's 'autism' or 'schizophrenia' is the same as another's or whether diagnostic categories have shifted over time/place leading to different levels of ascertainment bias), and to what degree traits like autoimmune or psychiatric disorders or cognitive functioning meaningfully cluster due sharing a biological basis and genetic architecture (for example, reading & mathematics disability genetically correlate, consistent with the Generalist Genes Hypothesis, and these genetic correlations explain the observed phenotypic correlations or 'co-morbidity';[34] IQ and specific measures of cognitive performance such as verbal, spatial, and memory tasks, reaction time, long-term memory, executive function etc. all show high genetic correlations as do neuroanatomical measurements, and the correlations may increase with age, with implications for the etiology & nature of intelligence). This can be an important constraint on conceptualizations of the two traits: traits which seem different phenotypically but which share a common genetic basis require an explanation for how these genes can influence both traits.

Boosting GWASes

Genetic correlations can be used in GWASes by using polygenic scores or genome-wide hits for one (often more easily measured) trait to increase the prior probability of variants for a second trait; for example, since intelligence and years of education are highly genetically correlated, a GWAS for education will inherently also be a GWAS for intelligence and be able to predict variance in intelligence as well[35] and the strongest SNP candidates can be used to increase the statistical power of a smaller GWAS,[36] or one could do a GWAS for multiple traits jointly.[37][38] Genetic correlations can also quantify the contribution of correlations <1 across datasets which might create a false "missing heritability", by estimating the extent to which differing measurement methods, racial influences, or environments create only partially overlapping sets of relevant genetic variants.[39]

Breeding

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Hairless dogs have imperfect teeth; long-haired and coarse-haired animals are apt to have, as is asserted, long or many horns; pigeons with feathered feet have skin between their outer toes; pigeons with short beaks have small feet, and those with long beaks large feet. Hence if man goes on selecting, and thus augmenting any peculiarity, he will almost certainly modify unintentionally other parts of the structure, owing to the mysterious laws of correlation.

— Charles Darwin, The Origin of Species, 1859

Genetic correlations are also useful in applied contexts such as plant/animal breeding by allowing substitution of more easily measured but highly genetically correlated characteristics (particularly in the case of sex-linked or binary traits under the liability-threshold model, where differences in the phenotype can rarely be observed but another highly correlated measure, perhaps an endophenotype, is available in all individuals), compensating for different environments than the breeding was carried out in, making more accurate predictions of breeding value using the multivariate breeder's equation as compared to predictions based on the univariate breeder's equation using only per-trait heritability & assuming independence of traits, and avoiding unexpected consequences by taking into consideration that artificial selection for/against trait X will also increase/decrease all traits which positively/negatively correlate with X.[40][41][42][43][44] The limits to selection set by the inter-correlation of traits, and the possibility for genetic correlations to change over long-term breeding programs, lead to Haldane's dilemma limiting the intensity of selection and thus progress.

Breeding experiments on genetically correlated traits can measure the extent to which correlated traits are inherently developmentally linked & response is constrained, and which can be dissociated.[45] Some traits, such as the size of eyespots on the butterfly Bicyclus anynana can be dissociated in breeding,[46] but other pairs, such as eyespot colors, have resisted efforts.[47]

Computing the genetic correlation

Genetic correlations require a genetically informative sample. They can be estimated by using breeding experiments on two traits of known heritability and selecting on one trait to measure the change in the other trait (allowing inferring the genetic correlation), family/adoption/twin studies (analyzed using SEMs or DeFries-Fulker extremes analysis), molecular estimation of relatedness such as GCTA,[48] methods employing polygenic scores like LD score regression,[15][49] BOLT-REML,[50] CPBayes,[51] or HESS,[52] comparison of genome-wide SNP hits in GWASes (as a loose lower bound), and phenotypic correlations of populations with at least some related individuals.[53] (As with estimating SNP heritability, the better computational scaling & the ability to estimate only using public polygenic scores is a particular advantage for LD score regression over competing methods, and combined with the increasing availability of polygenic scores from datasets like the UK Biobank has led to an explosion of genetic correlation research in the 2010s.) The methods are related to Haseman-Elston regression & PCGC regression.[54] Such methods are typically genome-wide, but it is also possible to estimate genetic correlations for specific variants or genome regions.[55]

One way to consider it is using trait X in twin 1 to predict trait Y in twin 2 for monozygotic and dizygotic twins (i.e. using twin 1's IQ to predict twin 2's brain volume); if this cross-correlation is larger for the more genetically-similar monozygotic twins than for the dizygotic twins, the similarity indicates that the traits are not genetically independent and there is some common genetics influencing both IQ and brain volume. (Statistical power can be boosted by using siblings as well.[56])

Genetic correlations are affected by methodological concerns; underestimation of heritability, such as due to assortative mating, will lead to overestimates of longitudinal genetic correlation,[57] and moderate levels of misdiagnoses can create pseudo correlations.[58] As they are affected by heritabilities of both traits, genetic correlations have low statistical power, especially in the presence of measurement errors biasing heritability downwards, because "estimates of genetic correlations are usually subject to rather large sampling errors and therefore seldom very precise": the standard error of an estimate r_g is \sigma(r_g) = \frac{1 - r_g^2}{\sqrt{2}} \cdot \sqrt{\frac{\sigma(h^2_x) \cdot \sigma(h^2_y)}{h^2_x \cdot h^2_y}}.[59] (Larger genetic correlations & heritabilities will be estimated more precisely.[60]) However, inclusion of genetic correlations in an analysis of a pleiotropic trait can boost power for the same reason that multivariate regressions are more powerful than separate univariate regressions.[61]

Twin methods have the advantage of being usable without detailed biological data, with human genetic correlations calculated as far back as the 1970s and animal/plant genetic correlations calculated in the 1930s, and require sample sizes in the hundreds for being well-powered, but they have the disadvantage of making assumptions which have been criticized, and in the case of rare traits like anorexia nervosa it may be difficult to find enough twins with a diagnosis to make meaningful cross-twin comparisons, and can only be estimated with access to the twin data; molecular genetic methods like GCTA or LD score regression have the advantage of not requiring specific degrees of relatedness and so can easily study rare traits using case-control designs, which also reduces the number of assumptions they rely on, but those methods could not be run until recently, require large sample sizes in the thousands or hundreds of thousands (to obtain precise SNP heritability estimates, see the standard error formula), may require individual-level genetic data (in the case of GCTA but not LD score regression)

Given a genetic covariance matrix, the genetic correlation is computed by standardizing this, i.e., by converting the covariance matrix to a correlation matrix. For example, if two traits, say height and weight have the following additive genetic variance-covariance matrix:

Height Weight
Height 36 36
Weight 36 117

Then the genetic correlation is .55, as seen is the standardized matrix below:

Height Weight
Height 1
Weight .55 1

In practice, structural equation modeling applications such as Mx or OpenMx (and before that, historically, LISREL[62]) are used to calculate both the genetic covariance matrix and its standardized form. In R, cov2cor() will standardize the matrix.

Typically, published reports will provide genetic variance components that have been standardized as a proportion of total variance (for instance in an ACE twin study model standardised as a proportion of V-total = A+C+E). In this case, the metric for computing the genetic covariance (the variance within the genetic covariance matrix) is lost (because of the standardizing process), so you cannot readily estimate the genetic correlation of two traits from such published models. Multivariate models (such as the Cholesky decomposition[better source needed]) will, however, allow the viewer to see shared genetic effects (as opposed to the genetic correlation) by following path rules. It is important therefore to provide the unstandardised path coefficients in publications.

Correlations

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Human correlations

Genetic correlations, positive & negative, have been measured for a wide variety of human traits using primarily molecular genetic methods but also, historically, twins.

Anthropometric

  • facial averageness/attractiveness[63]
  • baldness[64]
  • tiredness/forced expiratory volume[65]
  • tiredness/grip strength[65]
  • tiredness/self-rated health[65]
  • tiredness/longevity[65]
  • self-reported health/longevity[66]
  • life satisfaction/positive affect[67]
  • exercise/longevity[68]
  • self-reported health/forced expiratory volume in one second (FEV1)[66]
  • tiredness/obesity[65]
  • tiredness/BMI[65]
  • baseline BMI/increase in BMI[69]
  • chronotype/BMI[70]
  • undersleeping/BMI[70]
  • oversleeping/BMI[70]
  • morning chronotype/BMI[71]
  • difficulty sleeping/BMI[71]
  • difficulty sleeping/waist circumference[71]
  • difficulty sleeping/waist-hip ratio[71]
  • difficulty sleeping/neck bone mineral density[71]
  • daytime sleepiness/BMI[71]
  • daytime sleepiness/self-reported sleep duration[71]
  • daytime sleepiness/difficulty sleeping[71]
  • daytime sleepiness/waist circumference[71]
  • daytime sleepiness/waist-hip ratio[71]
  • sex/sleep pattern[72]
  • sex/sleep quality[72]
  • self-reported sleep duration/birth weight[71]
  • self-reported sleep duration/difficulty sleeping[71]
  • self-reported sleep duration/neck bone mineral density[71]
  • difficulty sleeping/birth weight[71]
  • gestational (pregnancy) weight gain / BMI[73]
  • gestational (pregnancy) weight gain / fasting glucose[73]
  • gestational (pregnancy) weight gain / type 2 diabetes[73]
  • breast size/BMI[74]
  • sweet taste perception/BMI[75]
  • ADHD/BMI[76]
  • monetary delay temporal discounting/BMI[77]
  • ADHD/extreme BMI[76]
  • ADHD/obesity class 1[76]
  • ADHD/obesity class 2[76]
  • ADHD/obesity class 3[76]
  • ADHD/overweight[76]
  • ADHD/waist-to-hip ratio[76]
  • ADHD/childhood obesity[76]
  • ADHD/type 2 diabetes[76]
  • tiredness/waist-hip ratio[65]
  • major depressive disorder/waist-hip ratio (WHR)[78]
  • self-reported health/BMI[66]
  • early puberty/BMI[79]
  • age at first sex/BMI[80]
  • exercise/BMI[81]
  • exercise/waist circumference[81]
  • body fat percentage/cardiorespiratory fitness (VO2max)[82]
  • BMI/waist circumference[81]
  • waist-hip ratio/BMI[83]
  • fasting insulin/BMI[83]
  • fasting glucose/waist-hip ratio[83]
  • heart rate recovery/vagal rebound[84]
  • longevity/resting heart rate[85]
  • exercise/resting heart rate[84][86]
  • exercise/heart rate recovery[84]
  • exercise/respiratory sinus arrhythmia[86]
  • ambulatory heart period/respiratory sinus arrhythmia[87]
  • respiration rate/respiratory sinus arrhythmia[87][88]
  • weight/height[89]
  • spousal height[90]
  • birth weight//infant head circumference[91]
  • birth weight/birth length[91]
  • birth weight/childhood height[91]
  • birth weight/adult height[91]
  • birth weight/waist circumference[91]
  • birth weight/hip circumference[91]
  • birth weight/BMI[91]
  • spousal BMI[90]
  • birth weight/childhood obesity[91]
  • birth weight/adult obesity[91]
  • birth weight/pubertal growth[91]
  • age at first sex/height[80]
  • age at first sex/birth weight[80]
  • age at first sex/birth length[80]
  • early puberty/age at first sex[80]
  • early puberty/age at first birth[80]
  • age at first sex/age at first birth[80]
  • age at first sex/age at menarche[80]
  • age at first sex/age at voice breaking[80]
  • age at first sex/age at menopause[80]
  • age at first sex/number of sexual partners[80]
  • age at first sex/number of children ever born[80]
  • age at first sex/childless[80]
  • age at first sex/happiness[80]
  • age at first birth/age at menarche[92]
  • age at first birth/age at menopause[92]
  • menopause/ADHD[76]
  • age at first birth/voice breaking[92]
  • age at first birth/polycystic ovary syndrome[92]
  • age at first birth/age at first sexual intercourse[92]
  • age at first birth/birth weight[92]
  • age at first birth/years of education[92]
  • age at first birth/cigarettes per day[92]
  • age at first birth/ever smoked[92]
  • age at first birth/age onset of smoking[92]
  • age at first birth/triglycerides[92]
  • age at first birth/type 2 diabetes[92]
  • age at first birth/fasting insulin level[92]
  • age at first birth/waist-hip ratio[92]
  • spousal waist-to-hip ratio[90]
  • age at first birth/BMI[92]
  • age at first birth/height[92]
  • age at first birth/major depressive disorder[93]
  • age at first birth/ADHD[76]
  • number of children ever born (fertility)/age at first sexual intercourse[92]
  • number of children ever born/years of education[92]
  • number of children ever born/number of grandchildren ever born[94]
  • anthropometric measurements made on the 6 major sub-populations of the Solomon Islands (Harvard Solomon Islands Project)[95]
    • weight
    • sitting height
    • height
    • biacromial diameter
    • bicristal diameter (biiliac breadth)
    • chest breadth
    • foot length
    • total facial height
    • upper facial height
    • nose length
    • nose breadth
    • bicondylar humerus diameter
    • wrist breadth
    • hand breadth
    • hand length
    • bicondylar femur diameter
    • foot breadth
    • head length
    • head breadth
    • minimum frontal diameter
    • bizygomatic diameter
    • bigonial diameter
    • head circumference
    • upper arm circumference
    • calf circumference
    • triceps skinfold thickness
    • subscapular skinfold thickness
  • intracranial volume/height[96]
  • intracranial volume/child head circumference[96]
  • intracranial volume/birth length[96]
  • intracranial volume/birth weight[96]
  • birth length/intelligence[97]
  • birth weight/intelligence[97]
  • head motion/BMI[98]
  • head motion/waist circumference[98]
  • head motion/hypertension[98]
  • head motion/monetary delay temporal discounting[98]
  • parents' ages at death[76]
    • father's age at death[76]
    • mother's age at death[76]

Neuroanatomical

Intelligence

  • intelligence longitudinal stability and consistency [33][57][135][136][137][138][139][140][97][126]
  • cognitive performance & neuroanatomical properties[99][102][105][106][108][113][115][125]
  • behavioral/emotional problems in children[141]
  • intelligence/reading[142][143]
  • intelligence/longevity[144]
  • tiredness/verbal-numerical reasoning [65]
  • self-reported health/intelligence[66]
  • age at first sex/intelligence[80]
  • intelligence/Openness[97][145]
  • intelligence/Conscientiousness[145][146]
  • intelligence/Neuroticism[20][126]
  • intelligence/anxiety[97]
  • intelligence/socioeconomic status (SES)[14][20][147]
  • intelligence/verbal-numerical reasoning/processing speed/short-term memory/Trail Making test[148]
  • spatial reasoning[149]
  • Coronary artery disease/verbal-numerical reasoning[13]
  • Stroke: ischaemic/verbal-numerical reasoning[13]
  • Stroke: cardioembolic/verbal-numerical reasoning[13]
  • Stroke: large vessel disease/verbal-numerical reasoning[13]
  • Stroke: small vessel disease/verbal-numerical reasoning[13]
  • Type 2 diabetes/verbal-numerical reasoning[13]
  • ADHD/verbal-numerical reasoning[13][150]
  • ADHD/intelligence[97][151]
  • ADHD/childhood IQ[76]
  • Alzheimer's disease/verbal-numerical reasoning[13][126]
  • Autism/verbal-numerical reasoning[13][152]
  • autism/intelligence[97][126]
  • Bipolar disorder/verbal-numerical reasoning[13]
  • Major depressive disorder/verbal-numerical reasoning[13][126]
  • schizophrenia/intelligence[153][154][155][156][157][158][159][97][160][126]
    • Schizophrenia/verbal-numerical reasoning[13]
    • Schizophrenia/memory[13]
    • Schizophrenia/reaction time[13]
    • Schizophrenia/educational attainment[13][161]
  • intelligence/temperament[162][163]
  • Hippocampal volume/verbal-numerical reasoning[13]
  • Intracranial volume (ICV)/verbal-numerical reasoning[13]
  • intracranial volume/childhood cognitive function[96]
  • intracranial volume/adult cognitive function[96]
  • Infant head circumference/verbal-numerical reasoning[13]
  • Infant head circumference/intelligence[97][126]
  • Blood pressure: diastolic/verbal-numerical reasoning[13]
  • Blood pressure: systolic/verbal-numerical reasoning[13]
  • BMI/verbal-numerical reasoning[13][126]
  • intelligence/weight[89]
  • Height/verbal-numerical reasoning[13]
  • intelligence/height[20][89][164][165][166][167][168][126][169]
  • Longevity/verbal-numerical reasoning[13]
  • Forced expiratory volume in 1 s (FEV1)/verbal-numerical reasoning[13]
  • Childhood cognitive ability/verbal-numerical reasoning[13]
  • Coronary artery disease/reaction time[13]
  • Stroke: ischaemic/reaction time[13]
  • Stroke: cardioembolic/reaction time[13]
  • Stroke: large vessel disease/reaction time[13]
  • Stroke: small vessel disease/reaction time[13]
  • Type 2 diabetes/reaction time[13]
  • ADHD/reaction time[13][150]
  • Alzheimer's disease/reaction time[13]
  • Autism/reaction time[13]
  • Bipolar disorder/reaction time[13]
  • Major depressive disorder/reaction time[13]
  • Hippocampal volume/reaction time[13]
  • Intracranial volume/reaction time[13]
  • Infant head circumference/reaction time[13]
  • Blood pressure: diastolic/reaction time[13]
  • Blood pressure: systolic/reaction time[13]
  • BMI/reaction time[13]
  • Height/reaction time[13]
  • Longevity/reaction time[13]
  • Forced expiratory volume in 1 s (FEV1)/reaction time[13]
  • Childhood cognitive ability/reaction time[13]
  • Coronary artery disease/memory[13]
  • Stroke: ischaemic/memory[13]
  • Stroke: cardioembolic/memory[13]
  • Stroke: large vessel disease/memory[13]
  • Stroke: small vessel disease/memory[13]
  • Type 2 diabetes/memory[13]
  • ADHD/memory[13]
  • Alzheimer's disease/memory[13]
  • Autism/memory[13]
  • Bipolar disorder/memory[13]
  • Major depressive disorder/memory[13]
  • Hippocampal volume/memory[13]
  • Intracranial volume/memory[13]
  • Infant head circumference/memory[13]
  • Blood pressure: diastolic/memory[13]
  • Blood pressure: systolic/memory[13]
  • BMI/memory[13]
  • Height/memory[13]
  • Longevity/memory[13]
  • Forced expiratory volume in 1 s (FEV1)/memory[13]
  • Childhood cognitive ability/memory[13]
  • go-no-go/fast task reaction time[150]
  • intelligence/reaction-time variability[150]
  • intelligence/reaction-time[150]
  • reaction-time variability/ADHD[150][170][171][172][173][174]
  • reaction-time/reaction-time variability[150]
  • frequency of talking or texting on smartphone/intelligence[175]
  • intelligence/waist-hip ratio[126]
  • intelligence/waist circumference[126]
  • monetary delay temporal discounting/childhood IQ[77]
Education

Psychological

  • food fussiness/food neophobia[238]
  • dental fear/fear of pain[239]
  • carry a tune/clap to a beat[233]
  • carry a tune/perfect pitch[233]
  • carry a tune/sing back musical notes[233]
  • phonological coding/pig Latin task[240]
  • phonological coding/rhyme-generation[240]
  • longitudinal stability of planning/behavioral disinhibition (Porteus Maze Test)[241]
  • longitudinal stability of personality[140]
  • risk-taking behavior of various kinds (natural/physical, moral, financial, reproductive, competitive, safety, gambling)[242]

Psychiatric

  • personality traits[243]
  • personality traits/psychiatric disorders[244]
  • personality traits/borderline personality disorder[245]
  • intellectual disabilities[246][247][248][249][250][251]
  • psychiatric illnesses/property crime[257]
  • schizophrenia/epilepsy[258]
  • schizophrenia/bipolar disorder[37][244][259][260][261][262]
    • bipolar subtype 1/bipolar subtype 2/schizophrenia[263]
  • schizophrenia/childhood-adolescent depression[264]
  • schizophrenia/oppositional defiant disorder+conduct disorder (ODD/CD)[264]
  • ADHD/conduct disorder[265]
  • schizophrenia/trauma[266]
  • PTSD/schizophrenia[267]
  • PTSD/bipolar disorder[267]
  • schizophrenia/family history of psychological disorders[266]
  • schizophrenia/ADHD[264][268]
  • schizophrenia/brain-volumes[269][270]
  • schizophrenia/major depressive disorder[261][271]
    • earlier-onset major depressive disorder (MDD)/adult-onset MDD/schizophrenia/bipolar disorder[272]
    • 10 subgroups of major depressive disorder[271]
  • mood instability/major depressive disorder[273]
  • major depressive disorder/Alzheimer's disease[271]
  • major depressive disorder/HDL cholesterol[271][78]
  • major depressive disorder/LDL cholesterol[271][78]
  • major depressive disorder/alcohol consumption[271]
  • major depressive disorder/diastolic blood pressure[271]
  • major depressive disorder/eczema[271]
  • major depressive disorder/migraine[271]
  • major depressive disorder/morning chronotype[271]
  • major depressive disorder/pulse blood pressure[271]
  • major depressive disorder/systolic blood pressure[271]
  • major depressive disorder/triglycerides[271][78]
  • mood instability/schizophrenia[273]
  • Openness/schizophrenia[244]
  • schizophrenia/autism[261]
  • sleep/schizophrenia[274]
  • tiredness/schizophrenia[65]
  • self-reported health/schizophrenia[66]
  • creativity/schizophrenia[275]
  • age at first sex/schizophrenia[80]
  • schizophrenia/HIV infection/risky sexual behavior[276]
  • Empathy Quotient score/schizophrenia[234]
  • self-reported sleep duration/schizophrenia[277]
  • daytime sleepiness/schizophrenia[71]
  • bipolar disorder/rheumatoid arthritis[48]
  • ADHD/rheumatoid arthritis[76]
  • bipolar disorder/type 2 diabetes[48]
  • bipolar disorder/major depressive disorder[244][261]
  • bipolar disorder/creativity[275]
  • Conscientiousness/bipolar disorder[244]
  • Openness/bipolar disorder[244]
  • Openness/major depressive disorder[244]
  • self-reported sleep duration/bipolar disorder[71]
  • age at first sex/bipolar disorder[80]
  • Empathy Quotient score/bipolar disorder[234]
  • Systemizing Quotient-Revised (SQ-R)/bipolar disorder[234]
  • brain region activation/bipolar disorder[278]
  • ADHD: inattention/hyperactivity-impulsivity symptoms[279][280] [281]
  • tiredness/ADHD[65]
  • major depressive disorder/ADHD[261][76]
  • depressive symptoms/ADHD[76]
  • self-reported health/ADHD[66]
  • autism/ADHD[282][283]
  • PGC mental illness factor/ADHD[76]
  • Extraversion/ADHD[244]
  • Neuroticism/ADHD[76]
  • monetary delay temporal discounting/attention-deficit/hyperactivity disorder (ADHD)[77]
  • monetary delay temporal discounting/schizophrenia[77]
  • monetary delay temporal discounting/major depressive disorder[77]
  • monetary delay temporal discounting/Neuroticism[77]
  • age at first sex/autism[80]
  • Empathy Quotient score/autism[234]
  • Systemizing Quotient-Revised (SQ-R)/autism[234]
  • age at first sex/ADHD[80]
  • ADHD/tics[284]
  • ADHD/obsessive-compulsive[284][285]
  • intracranial volume/ADHD[270]
  • ADHD/bipolar disorder[286]
  • longitudinal stability of ADHD[283]
  • ADHD/affective problems[287]
  • tiredness/bipolar disorder[65]
  • tiredness/major depressive disorder[65]
  • daytime sleepiness/major depressive disorder[71]
  • difficulty sleeping/major depressive disorder[71]
  • self-reported health/major depressive disorder[66]
  • schizophrenia/major depressive disorder[244][288]
  • bipolar disorder/major depressive disorder[288]
  • age at first sex/major depressive disorder[80]
  • migraine/Neuroticism[289]
  • tiredness/Neuroticism[65]
  • self-reported health/Neuroticism[66]
  • Grit/Conscientiousness[207]
  • Conscientiousness/Extraversion[244][290]
  • Neuroticism: 2 subgroups[271]
  • Neuroticism/Extraversion[208][244][290]
  • Openness/Extraversion[208][290]
  • Extraversion: sociability and impulsiveness facets[291]
  • Social Anxiety Score/Extraversion[292]
  • frequency of talking or texting on smartphone/Extraversion[175][244]
  • Neuroticism/Conscientiousness[208][244][290]
  • Openness/Conscientiousness[244][290]
  • Openness/Neuroticism[244][290]
  • Agreeableness/Conscientiousness[244]
  • Agreeableness/Extraversion[244]
  • Agreeableness/Neuroticism[244]
  • Agreeableness/major depressive disorder[244]
  • Conscientiousness/major depressive disorder[244]
  • Conscientiousness/autism spectrum disorder[244]
  • Agreeableness/autism spectrum disorder[244]
  • Eyes Test empathy score/Openness[133]
  • Eyes Test empathy score/self-reported empathy (Empathy Quotient)[133]
  • Neuroticism, Extraversion, Psychoticism, social attitudes scale, Wilson conservatism scale longitudinal stability[6]
  • Neuroticism/loneliness[293]
  • Extraversion/loneliness[293]
  • Extraversion/bipolar disorder[244]
  • depression symptoms/loneliness[293]
  • behavioral/emotional problems in children[141][294][295][296][297]
    • longitudinal stability of childhood aggression[298]
  • antisocial behavior/Extraversion[299]
  • antisocial behavior/Agreeableness[299]
  • antisocial behavior/Conscientiousness[299]
  • antisocial behavior/sensation seeking[299]
  • antisocial behavior/urgency[299]
  • antisocial behavior/lack of premeditation[299]
  • antisocial behavior/lack of perseverance[299]
  • antisocial behavior/Psychoticism[299]
  • antisocial behavior/novelty seeking[299]
  • antisocial behavior/impulsivity[299]
  • antisocial behavior/reward dependence[299]
  • antisocial behavior/depressive symptoms[300]
  • longitudinal stability of antisocial symptoms[300]
  • longitudinal stability of depressive symptoms[300]
  • unipolar/bipolar depression[301]
  • age at first sex/Alzheimers[80]
  • age at first sex/risk-taking propensity[80]
  • age at first sex/suffer from nerves[80]
  • age at first sex/irritable personality[80]
  • self-reported health/anorexia nervosa[66]
  • age at first sex/anorexia nervosa[80]
  • obsessive-compulsive disorder/anorexia nervosa[302]
  • Bipolar disorder/anorexia nervosa[244]
  • Eyes Test empathy score/anorexia nervosa[133]
  • Empathy Quotient score/anorexia nervosa[234]
  • Systemizing Quotient-Revised (SQ-R)/anorexia nervosa[234]
  • Tourette syndrome/obsessive-compulsive disorder[285][303][304]
  • hoarding/obsessive-compulsive syndrome[305][306]
  • obsessive-compulsive disorder inventory/inventory subscales (checking, hoarding, obsessing, ordering, and washing)[307][308]
  • excessive object acquisition/difficulties discarding possessions[309]
  • compulsive hoarding/tics[305]
  • obsessive-compulsive syndrome/tics[284][305]
  • panic disorder/generalised anxiety disorder/phobias/obsessive-compulsive disorder/post-traumatic stress disorder[310][311][312]
  • Neuroticism/phobia[266]
  • Neuroticism/panic[266]
  • bulimia/anorexia nervosa[313]
  • Right temporal pole surface area/anorexia nervosa[270]
  • weight and shape concerns and behaviors/binge eating[314]
  • obesity/binge eating[315]
  • purging/binge eating[316]
  • body dissatisfaction / weight preoccupation / binge eating[317]
  • major depressive disorder/anorexia nervosa[318]
  • Neuroticism/anorexia nervosa[244][319]
  • Schizophrenia/anorexia nervosa[244][319]
  • Conscientiousness/schizophrenia[244]
  • Psychiatric factor/anorexia nervosa[319]
  • Years of education/anorexia nervosa[319]
  • College attendance/anorexia nervosa[319]
  • Extreme BMI/anorexia nervosa[319]
  • Body fat percentage/anorexia nervosa[319]
  • major depressive disorder/body fat percentage[78]
  • Overweight/anorexia nervosa[319]
  • Hip circumference/anorexia nervosa[319]
  • HDL cholesterol/anorexia nervosa[319]
  • Phospholipids in large HDL/anorexia nervosa[319]
  • Concentration of large HDL particles/anorexia nervosa[319]
  • Total lipids in large HDL/anorexia nervosa[319]
  • Cholesterol esters in large HDL/anorexia nervosa[319]
  • Free cholesterol in large HDL/anorexia nervosa[319]
  • Total cholesterol in large HDL/anorexia nervosa[319]
  • Fasting glucose/anorexia nervosa[319]
  • HOMA-Beta/anorexia nervosa[319]
  • Fasting insulin/anorexia nervosa[319]
  • HOMA-IR/anorexia nervosa[319]
  • major depressive disorder/bulimia[320]
  • Right temporal pole surface area/major depressive disorder[270]
  • dementia with Lewy bodies/Alzheimers[321]
  • dementia with Lewy bodies/Parkinson's[321]
  • Alzheimers/Parkinson's[321]
  • difficulty sleeping/Alzheimer's disease[71]
  • intracranial volume/Parkinson's disease[96]
  • hippocampal volume/Alzheimer's disease[322]
  • autism/Social and Communication Disorders Checklist[152]
  • childhood sexual abuse/age at first sex[323]
  • stressful life-events/psychotic events[324]
  • Subjective well-being / depressive symptoms[67][93]
  • Subjective well-being / Neuroticism[67]
  • Subjective well-being/ADHD[76]
  • Neuroticism/major depressive disorder[244][93][271]
  • Depressive symptoms / Neuroticism[67][266][93]
  • menstrual symptoms/anxiety/depression/Neuroticism[325]
  • life satisfaction/Neuroticism[326]
  • positive affect/Neuroticism[326]
  • Anxiety disorders / subjective well-being[67]
  • Anxiety disorders / Neuroticism[67][266]
  • Anxiety disorders / depressive symptoms[67]
  • Autism spectrum disorder / subjective well-being[67]
  • Autism spectrum disorder / Neuroticism[67]
  • intracranial volume/autism spectrum disorder[270]
  • Bipolar disorder / subjective well-being[67]
  • Bipolar disorder / Neuroticism[67]
  • Bipolar disorder / depressive symptoms[67]
  • Schizophrenia / subjective well-being[67]
  • Schizophrenia / Neuroticism[67]
  • Schizophrenia / depressive symptoms[67][266]
  • PGC psychiatric disorders factor/major depressive disorder[93]
  • schizophrenia/anxiety[266]
  • BMI / depressive symptoms[67][78]
  • Coronary artery disease / depressive symptoms[67][78]
  • Coronary artery disease/Neuroticism[67][266]
  • general anxiety disorder/major depressive disorder[327][328][329][330][331][332][333]
  • burnout/major depressive order[332]
  • burnout/general anxiety disorder[332]
  • major depressive disorder/phobias (agoraphobia / social / animal / situational phobia)[334]
  • phobias/panic disorder/bulimia nervosa[333][335][336][337][338][339][340]
  • longitudinal tinnitus[341]
  • tinnitus/poorer hearing threshold[341]
  • independent living/subjective well-being[342]
  • independent living/intelligence[342]
  • independent living/schizophrenia[342]
  • independent living/bipolar disorder[342]
  • independent living/major depressive disorder[342]
Drug use
  • lifetime use of marijuana/smoking[343]
  • tiredness/smoking status[65]
  • sugar consumption/drug use[344]
  • age at first sex/ever-smoker[80]
  • age at first sex/cigarettes per day[80]
  • monetary delay temporal discounting/lifetime smoking[77]
  • monetary delay temporal discounting/former smoker[77]
  • self-reported sleep duration/cigarettes per day[71]
  • difficulty sleeping/cigarettes per day[71]
  • daytime sleepiness/cigarettes per day[71]
  • age at first sex/smoking at onset[80]
  • alcohol consumption/alcohol dependence[345][346][347] (sex-varying correlations)[348]
  • age at first sex/alcohol consumption[80]
  • alcohol consumption/ventral striatum activity/ADHD[349]
  • alcohol consumption/HDL cholesterol[350]
  • alcohol consumption/overweightness (obesity class 1/2)[350]
  • alcohol consumption/lung cancer[350]
  • ADHD/lung cancer[76]
  • ADHD/ever smoker[76]
  • ADHD/former smoker[76]
  • ADHD/lung cancer[76]
  • ADHD/cigarettes per day[76]
  • alcohol consumption/childhood height[350]
  • alcohol consumption/severe obesity[350]
  • alcohol consumption/age at menarche[350]
  • alcohol consumption/chronotype[350]
  • alcohol consumption/body mass index[350]
  • alcohol consumption/hip circumference[350]
  • alcohol consumption/fasting insulin[350]
  • female alcohol consumption/LDL cholesterol[350]
  • female alcohol consumption/triglycerides[350]
  • Ever-smoker / subjective well-being[67]
  • Ever-smoker / neuroticism[67]
  • Ever-smoker / depressive symptoms[67]
  • Smoking/education[351]
  • Smoking/intelligence[97][126]
  • smoking/alcohol drinking[352][350]
  • smoking/BMI[352]
  • alcohol drinking/education[351]
    • female alcohol consumption/college completion[350]
    • female alcohol consumption/years of schooling[350]
  • alcoholism/divorce[353]
  • alcohol effects on arithmetic performance / alcohol effects on motor coordination[354]
  • willingness to drive drunk/Eysenck personality scales[355]
  • executive function/substance abuse/substance dependency, longitudinal[356]
  • schizophrenia
    • alcohol consumption/schizophrenia[350]
    • schizophrenia/marijuana use[357]
    • schizophrenia/nicotine or alcohol or cocaine[358][266]
  • longitudinal stability of gambling[359]

Biological

  • tiredness/HDL cholesterol[65]
  • tiredness/HbA1c[65]
  • tiredness/triglycerides[65]
  • self-reported health/systolic and diastolic blood pressure[66]
  • birth weight/diastolic blood pressure[91]
  • birth weight/systolic blood pressure[91]
  • birth weight/total cholesterol[91]
  • birth weight/HDL cholesterol[91]
  • birth weight/LDL cholesterol[91]
  • ADHD/HDL cholesterol[76]
  • ADHD/triglycerides[76]
  • Triglycerides / subjective well-being[67]
  • Triglycerides / neuroticism[67]
  • Triglycerides / depressive symptoms[67]
  • fasting insulin/triglycerides[83]
  • fasting glucose/fasting triglycerides[83]
  • waist-hip ratio/fasting triglycerides[83]
  • fasting triglycerides/BMI[83]
  • HDL/triglycerides[83]
  • early puberty/fasting insulin[79]
  • early puberty/lipid profiles[79]
  • early puberty/bone mineral density[79]
  • age at first sex/femoral neck bone mineral density[80]
  • age at first sex/lumbar spine bone mineral density[80]
  • age at first sex/fasting glucose[80]
  • age at first sex/HDL cholesterol[80]
  • age at first sex/LDL cholesterol[80]
  • age at first sex/total cholesterol[80]
  • age at first sex/triglycerides[80]
  • age at first sex/fasting insulin[80]
  • age at first sex/HbA1C[80]
  • age at first sex/diastolic blood pressure[80]
  • age at first sex/systolic blood pressure[80]
  • fasting HDL/BMI[83]
  • systolic blood pressure/BMI[83]
  • spousal systolic blood pressure[90]
  • waist-hip ratio/fasting glucose[83]
  • waist-hip ratio/fasting insulin[83]
  • waist-hip ratio/HDL[83]
  • waist-hip ratio/systolic blood pressure[83]
  • fasting glucose/fasting insulin[83]
  • fasting glucose/HDL[83]
  • fasting glucose/systolic blood pressure[83]
  • fasting insulin/HDL[83]
  • fasting insulin/systolic blood pressure[83]
  • HDL/systolic blood pressure[83]
  • liver enzymes/total cholesterol[19]
  • liver enzymes/LDL cholesterol[19]
  • liver enzymes/HDL cholesterol[19]
  • liver enzymes/triglycerides[19]
  • liver enzymes/glucose[19]
  • liver enzymes/insulin[19]
  • liver enzymes/HOMA-B[19]
  • liver enzymes/HOMA-IR[19]
  • liver enzymes/c-reactive protein[19]
  • liver enzymes/alcohol consumption[19]
  • cortisol/cortisone levels in child/adolescent hair[360]

Disease

Transfer

Trans-population
  • African/European schizophrenia[384]
  • East Asian/European schizophrenia[383]
  • lifetime, recurrent, & female major depressive disorder: East Asian/European[385]
  • gene expression[386]
  • rheumatoid arthritis[386]
  • type 2 diabetes[386][387]
Trans-cohort
  • depressive symptoms: GERA/PGC,[67] GERA/UKB,[67] UKB/PGC[67][271]
    • major depressive disorder+recurrent major depressive disorder+female major depressive disorder/major depressive disorder[93]
    • female/male / major depressive disorder+recurrent major depressive disorder[93]
  • neuroticism: UK Biobank (UKB) / Genetics of Personality Consortium[67]
  • autism: PGC (USA)/iPSYCH (Denmark)[152]
  • male/female Empathy Quotient score[234]
  • male/female Systemizing Quotient-Revised (SQ-R)[234]
  • male/female Eyes Test empathy score[133]
  • ARIC/EHR phenotype consistencies[388]
  • male/female alcohol consumption[350]
  • between-cohort IQ measurements[126]
  • ADHD: PGC/iPSYCH cohorts[76]; PGC case-controls / trios[76]; meta-analysis/EAGLE general population[76]; meta-analysis/23andMe self-reported diagnoses[76]
Trans-method
  • custom immunochip (iChip) SNP array/standard SNP array (comparing their estimates for the Crohn's disease/ulcerative colitis genetic correlation)[373]
  • major depressive order: Psychiatric Genomics Consortium (PGC) clinical depression diagnosis/23andMe self-report depression[288]
  • major depressive disorder/self-declared depression[389]
  • Big Five personality traits in Genetics of Personality Consortium/23andMe cohorts[244]
  • self-reported height/clinically measured height[390]

Animal/plant

See also

References

  1. ↑ Falconer 1960
  2. ↑ Neale & Maes 1996, Methodology for genetics studies of twins and families (6th ed.). Dordrecht, The Netherlands: Kluwer.
  3. ↑ 3.0 3.1 pg 123 of Plomin 2012
  4. ↑ pg194-195 of Jensen 1980, Bias in Mental Testing
  5. ↑ Martin & Eaves 1977, "The Genetical Analysis of Covariance Structure"
  6. ↑ 6.0 6.1 Eaves et al 1978, "Model-fitting approaches to the analysis of human behaviour"
  7. ↑ Loehlin & Vandenberg 1968, "Genetic and environmental components in the covariation of cognitive abilities: An additive model", in Progress in Human Behaviour Genetics, ed. S. G. Vandenberg, pp. 261278. Johns Hopkins, Baltimore.
  8. ↑ Purcell 2002, "Variance components models for gene-environment interaction in twin analysis"
  9. ↑ 9.0 9.1 Kohler et al 2011, "Social Science Methods for Twins Data: Integrating Causality, Endowments and Heritability"
  10. ↑ Wagner & Zhang 2011, "The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms"
  11. ↑ 11.0 11.1 11.2 Cheverud 1988, "A comparison of genetic and phenotypic correlations"
  12. ↑ Krapohl et al 2015, "Phenome-wide analysis of genome-wide polygenic scores"
  13. ↑ 13.00 13.01 13.02 13.03 13.04 13.05 13.06 13.07 13.08 13.09 13.10 13.11 13.12 13.13 13.14 13.15 13.16 13.17 13.18 13.19 13.20 13.21 13.22 13.23 13.24 13.25 13.26 13.27 13.28 13.29 13.30 13.31 13.32 13.33 13.34 13.35 13.36 13.37 13.38 13.39 13.40 13.41 13.42 13.43 13.44 13.45 13.46 13.47 13.48 13.49 13.50 13.51 13.52 13.53 13.54 13.55 13.56 13.57 13.58 13.59 13.60 13.61 13.62 13.63 13.64 13.65 13.66 13.67 13.68 13.69 13.70 13.71 13.72 13.73 13.74 13.75 13.76 13.77 13.78 13.79 13.80 13.81 13.82 13.83 13.84 13.85 13.86 13.87 13.88 13.89 13.90 13.91 13.92 13.93 13.94 13.95 13.96 Hagenaars et al 2016, "Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia"
  14. ↑ 14.0 14.1 14.2 Hill et al 2016, "Molecular genetic contributions to social deprivation and household income in UK Biobank (n=112,151)"
  15. ↑ 15.0 15.1 "LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis", Zheng et al 2016
  16. ↑ Sivakumaran et al 2011, "Abundant pleiotropy in human complex diseases and traits"
  17. ↑ 17.0 17.1 Solovieff et al 2013, "Pleiotropy in complex traits: challenges and strategies"
  18. ↑ 18.0 18.1 Cotsapas et al 2011, "Pervasive sharing of genetic effects in autoimmune disease"
  19. ↑ 19.00 19.01 19.02 19.03 19.04 19.05 19.06 19.07 19.08 19.09 19.10 Chambers et al 2011, "Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma"
  20. ↑ 20.0 20.1 20.2 20.3 20.4 Mottus et al 2017, "Markers of Psychological Differences and Social and Health Inequalities: Possible Genetic and Phenotypic Overlaps"
  21. ↑ Pickrell 2015, "Fulfilling the promise of Mendelian randomization"
  22. ↑ Smith 2015, "Mendelian randomization: a premature burial?"; "Understanding Mendelian Randomization"
  23. ↑ Burgess et al 2016, "Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors"
  24. ↑ Hagenaars et al 2016b, "Cognitive ability and physical health: a Mendelian randomization study"
  25. ↑ Bowden et al 2015, "Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression"
  26. ↑ eg Falconer cites the example of chicken size and egg laying: chickens grown large for genetic reasons lay later, fewer, and larger eggs, while chickens grown large for environmental reasons lay quicker and more but normal sized eggs (pg315 of Falconer 1960); Falconer in Table 19.1 on pg316 also provides examples of opposite-signed phenotypic & genetic correlations: fleece-weight/length-of-wool & fleece weight/body-weight in sheep, and body-weight/egg-timing & body-weight/egg-production in chicken. One consequence of the negative chicken correlations was that, despite moderate heritabilities and a positive phenotypic correlation, selection had begun to fail to yield any improvements (pg329) according to "Genetic slippage in response to selection for multiple objectives", Dickerson 1955.
  27. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  28. ↑ 28.0 28.1 Krapohl & Plomin 2016, "Genetic link between family socioeconomic status and children's educational achievement estimated from genome-wide SNPs"
  29. ↑ pg 397 of Plomin et al 2012
  30. ↑ Tong et al 2017, "Shared regulatory sites are abundant in the human genome and shed light on genome evolution and disease pleiotropy"
  31. ↑ Munafo et al 2016, "Collider Scope: How selection bias can induce spurious associations"
  32. ↑ Hewitt et al 1988, "Resolving the causes of developmental continuity or 'tracking.' I. Longitudinal twin studies during growth"
  33. ↑ 33.0 33.1 Deary et al 2012, "Genetic contributions to stability and change in intelligence from childhood to old age"
  34. ↑ "The substantial comorbidity between specific cognitive disabilities is largely due to genetic factors, meaning that the same genes affect different learning disabilities although there are also disability-specific genes." pg184-185 of Plomin et al 2012
  35. ↑ Rietveld et al 2013, "GWAS of 126,559 individuals identifies genetic variants associated with educational attainment"
  36. ↑ 36.0 36.1 Rietveld et al 2014, "Common genetic variants associated with cognitive performance identified using the proxy-phenotype method"
  37. ↑ 37.0 37.1 Andreassen et al 2013, "Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate"
  38. ↑ Porter & O'Reilly, "Multivariate simulation framework reveals performance of multi-trait GWAS methods"
  39. ↑ et al 2017, "Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies"
  40. ↑ Hazel 1943, "The Genetic Basis for Constructing Selection Indexes"
  41. ↑ Rae 1951, "The Importance of Genetic Correlations in Selection"
  42. ↑ Hazel & Lush 1943, "The efficiency of three methods of selection"
  43. ↑ Lerner 1950, Population genetics and animal improvement: as illustrated by the inheritance of egg production
  44. ↑ Falconer 1960, pg324-329
  45. ↑ Conner 2012, "Quantitative genetic approaches to evolutionary constraint: how useful?"
  46. ↑ Beldade et al 2002, "Developmental constraints versus flexibility in morphological evolution"
  47. ↑ Allen et al 2008, "Differences in the selection response of serially repeated color pattern characters: standing variation, development, and evolution"
  48. ↑ 48.0 48.1 48.2 48.3 48.4 Lee et al 2012, "Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood"
  49. ↑ "LD Score regression distinguishes confounding from polygenicity in genome-wide association studies", Bulik-Sullivan et al 2015 (see also Shi et al 2016); LDSC
  50. ↑ 50.00 50.01 50.02 50.03 50.04 50.05 50.06 50.07 50.08 50.09 50.10 50.11 50.12 50.13 50.14 50.15 50.16 Loh et al 2015, "Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis"
  51. ↑ Majumdar et al 2017, "An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations"
  52. ↑ Shi et al 2016, "Contrasting the genetic architecture of 30 complex traits from summary association data"
  53. ↑ Lynch 2000, "Estimating genetic correlations in natural populations"
  54. ↑ Golan et al 2014, "Measuring missing heritability: Inferring the contribution of common variants"
  55. ↑ Shi et al 2016, "Local genetic correlation gives insights into the shared genetic architecture of complex traits"
  56. ↑ Posthuma & Boomsma 2000, "A note on the statistical power in extended twin designs"
  57. ↑ 57.0 57.1 DeFries et al 1987, "Genetic Stability of Cognitive Development From Childhood to Adulthood"
  58. ↑ Wray et al 2012, "Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes"
  59. ↑ pg317-318 of Falconer 1960
  60. ↑ 60.0 60.1 Schmitt et al 2007b, "Review of twin and family studies on neuroanatomic phenotypes and typical neurodevelopment"
  61. ↑ Almasy et al 1997, "Bivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages"
  62. ↑ Heath et al 1989, "Testing structural equation models for twin data using LISREL"
  63. ↑ Lee et al 2016, "Facial averageness and genetic quality: testing heritability, genetic correlation with attractiveness, and the paternal age effect"
  64. ↑ Hagenaars et al 2016b, "Genetic Prediction of Male Pattern Baldness"
  65. ↑ 65.00 65.01 65.02 65.03 65.04 65.05 65.06 65.07 65.08 65.09 65.10 65.11 65.12 65.13 65.14 65.15 65.16 65.17 Deary et al 2016, "Genetic contributions to self-reported tiredness"
  66. ↑ 66.00 66.01 66.02 66.03 66.04 66.05 66.06 66.07 66.08 66.09 66.10 66.11 66.12 66.13 Harris et al 2015, "Molecular genetic contributions to self-rated health"
  67. ↑ 67.00 67.01 67.02 67.03 67.04 67.05 67.06 67.07 67.08 67.09 67.10 67.11 67.12 67.13 67.14 67.15 67.16 67.17 67.18 67.19 67.20 67.21 67.22 67.23 67.24 67.25 67.26 67.27 Okbay et al 2016, "Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses"
  68. ↑ Karvinen et al 2015, "Physical activity in adulthood: genes and mortality"
  69. ↑ Hjelmborg et al 2008, "Genetic Influences on Growth Traits of BMI: A Longitudinal Study of Adult Twins"
  70. ↑ 70.0 70.1 70.2 70.3 Jones et al 2016, "Genome‐Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci"
  71. ↑ 71.00 71.01 71.02 71.03 71.04 71.05 71.06 71.07 71.08 71.09 71.10 71.11 71.12 71.13 71.14 71.15 71.16 71.17 71.18 71.19 71.20 71.21 71.22 71.23 71.24 71.25 71.26 71.27 71.28 71.29 71.30 71.31 71.32 71.33 Lane et al 2016, "Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank"
  72. ↑ 72.0 72.1 Heath et al 1990, "Evidence for Genetic Influences on Sleep Disturbance and Sleep Pattern in Twins"
  73. ↑ 73.0 73.1 73.2 Warrington et al 2017, "Maternal and fetal genetic contribution to gestational weight gain"
  74. ↑ Wade et 2010, "Body mass index and breast size in women: same or different genes?"
  75. ↑ Hwang et al 2016, "Sweet Taste Perception is Associated with Body Mass Index at the Phenotypic and Genotypic Level"
  76. ↑ 76.00 76.01 76.02 76.03 76.04 76.05 76.06 76.07 76.08 76.09 76.10 76.11 76.12 76.13 76.14 76.15 76.16 76.17 76.18 76.19 76.20 76.21 76.22 76.23 76.24 76.25 76.26 76.27 76.28 76.29 76.30 76.31 76.32 76.33 Demontis et al 2017, "Discovery Of The First Genome-Wide Significant Risk Loci For ADHD" (table 5)
  77. ↑ 77.0 77.1 77.2 77.3 77.4 77.5 77.6 77.7 77.8 77.9 Sanchez-Roige et al 2017, "Genetics of the Research Domain Criteria (RDoC): genome-wide association study of delay discounting"
  78. ↑ 78.0 78.1 78.2 78.3 78.4 78.5 78.6 Wong et al 2017, "Genetic Overlap Of Depression With Cardiometabolic Diseases And Implications For Drug Repurposing For Comorbidities"
  79. ↑ 79.0 79.1 79.2 79.3 79.4 79.5 79.6 Day et al 2015, "Genetic determinants of puberty timing in men and women: shared genetic aetiology between sexes and with health-related outcomes"
  80. ↑ 80.00 80.01 80.02 80.03 80.04 80.05 80.06 80.07 80.08 80.09 80.10 80.11 80.12 80.13 80.14 80.15 80.16 80.17 80.18 80.19 80.20 80.21 80.22 80.23 80.24 80.25 80.26 80.27 80.28 80.29 80.30 80.31 80.32 80.33 80.34 80.35 80.36 80.37 80.38 80.39 80.40 80.41 80.42 80.43 80.44 80.45 Day et al 2016, "Physical and neurobehavioral determinants of reproductive onset and success"
  81. ↑ 81.0 81.1 81.2 Mustelin et al 2009, "Physical activity reduces the influence of genetic effects on BMI and waist circumference: a study in young adult twins"
  82. ↑ Schnurr et al 2016, "Genetic Correlation between Body Fat Percentage and Cardiorespiratory Fitness Suggests Common Genetic Etiology
  83. ↑ 83.00 83.01 83.02 83.03 83.04 83.05 83.06 83.07 83.08 83.09 83.10 83.11 83.12 83.13 83.14 83.15 83.16 83.17 83.18 83.19 Vattikuti et al 2012, "Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits"
  84. ↑ 84.0 84.1 84.2 Nederend et al 2016, "Heritability of heart rate recovery and vagal rebound after exercise"
  85. ↑ Eppinga et al 2016, "Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality"
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