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PubMed1990

Genetic correlations in validating emotionality.

Sixty-six rats of seven inbred strains were observed in an open field (OF) and in a light-dark preference box (LDB). Each set of apparatus provides measures of locomotor activity and of the time spent in the "most protected" area (the corners in the OF, the dark compartment in the LDB). These measures are proposed to be indices of emotionality. In the present study, the reliability and validity of the OF and LDB measures were determined by genetic correlations. Genetic correlations (rGEN) within and between tests were used as they provide a more coherent picture of reliability and validity of the measures than product-moment correlations (rPM), since rGEN's are appropriate estimates of "true" individual scores. It appeared that the activity measure in the OF and the occupancies of the most protected parts of OF and LDB were valid indices of emotionality, while the activity measure in the LDB was not. We recommend that the occupancy measures in the OF and the LDB should be used in addition to the commonly used activity measure in the OF to assess emotionality. The results indicate that genetic correlations provide a useful tool to validate measures in animal research. The only experimental manipulation in this approach consists of using the variation between inbred strains.

Animals↗

PubMed2026

Consistent and idiosyncratic pleiotropy in shaping genetic correlations.

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

Genetic Pleiotropy↗

PubMed1999

[Improvement of methods for determining genetic correlations for selection indices].

The possibility of using partial correlation as a measure of genetic correlation in estimating selection indices was studied. The methods of estimation were analyzed, and the use of one of them was substantiated. The applicability of partial correlations in estimating genetic correlations was demonstrated theoretically and with an example from actual practice. The proposed algorithm was illustrated by an example.

Algorithms↗

PubMed2006

A meta-analysis of genetic correlations between plant resistances to multiple enemies.

Genetic correlations between plant resistances to multiple natural enemies are important because they have the potential to determine the mode of selection that natural enemies impose on a host plant, the structure of herbivore and pathogen communities, and the success of plant breeding for resistance to multiple diseases and pests. We conducted a meta-analysis of 29 published studies of 16 different plant species reporting a total of 467 genetic correlations between resistances to multiple herbivores or pathogens. In general, genetic associations between resistances to multiple natural enemies tended to be positive regardless of the breeding design, type of attacker, and type of host plant. Positive genetic correlations between resistances were stronger when both attackers were pathogens or generalist herbivores and when resistance to different enemies was tested independently, suggesting that generalists may be affected by the same plant resistance traits and that interactions among natural enemies are common. Although the mean associations between resistances were positive, indicating the prevalence of diffuse selection and generalized defenses against multiple enemies, the large variation in both the strength and the direction of the associations suggests a continuum between pairwise and diffuse selection.

Animals↗

PubMed1984

[Semantics of genetic correlation analysis].

A theory of genetic-correlation analysis has been put forward on the basis of notions concerning general and special codispersions . The complete set of genetic-correlation indexes is received and their genetic-statistical meaning revealed. The models for genetical component analysis of general and special codispersions of correlative connections are constructed.

Genetic Variation↗

PubMed2005

Principal components and factorial approaches for estimating genetic correlations in international sire evaluation.

The increasing number of participating countries and the lack of genetic links among some of them lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield. Reparameterization using principal components or factorial approaches is proposed to exploit patterns in the genetic correlation matrix in order to reduce the number of parameters to be estimated without much loss of information. A 2-step approach was used. First, the genetic matrix between 8 or 9 "base" countries was used to determine a reduced number of principal components or factors. Then, the contributions of the remaining countries to these principal components or factors were computed. The resulting genetic correlations for the 18 countries were compared with the "reference" genetic correlations obtained with a classical model. The impact of using reparameterized genetic correlation matrices on breeding value prediction was investigated for both approaches. A better agreement between predicted breeding values and stability of their rankings was found when an approximate factor analysis was used, whatever the number of factors considered. The estimation of genetic correlations among 18 countries using an approximate factorial approach with 5 factors taken into account led to a reduction of the number of parameters to estimate from 171 to 80. The average absolute deviation of the correlations estimated with an approximate factorial approach from the "reference" genetic correlations was 0.014, which is considered very satisfactory in light of the computational ease.

Analysis of Variance↗

PubMed2000

Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure.

UNLABELLED: Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure. BACKGROUND: Levels of urinary albumin excretion (UAE) are used to define both the diagnosis of diabetic nephropathy and its progression. Predisposition to high blood pressure is also a risk factor for susceptibility to nephropathy, but its relationship with UAE in type 2 diabetes remains unclear. In this study, we have estimated heritabilities of UAE and blood pressure and their correlation attributable to genetic effects using 96 large families ascertained for type 2 diabetes. METHODS: In these families, 630 individuals with type 2 diabetes and 639 individuals with normoglycemia were examined. All of them had a determination of UAE as the urinary albumin creatinine ratio (ACR), a convenient index of UAE, together with blood pressure and other variables, such as age, sex, and body mass index. A variance components approach was used to estimate heritability and genetic correlations for ACR and systolic and diastolic blood pressures (SBP and DBP) after adjustment for relevant covariates. RESULTS: In the 96 pedigrees, 6481 usable pairs of relatives were identified. In the total collection of pairs, heritability for ACR (h2 = 0.27, P < 0.001) was similar to that for blood pressure. When only pairs of diabetic relatives were analyzed (N = 1732), the estimates of heritability increased slightly for ACR (h2 = 0.31), SBP (h2 = 0.33), and DBP (h2 = 0.23). A significant genetic correlation was found between ACR and SBP (rg 0.27) and DBP (rg 0.26) in all pairs of relatives (P < 0.001). In pairs of diabetic relatives, these values were higher for SBP and DBP, 0.38 and 0.52, respectively. CONCLUSION: In families with type 2 diabetes, UAE is a heritable trait, with a heritability similar to that for blood pressure. A significant genetic correlation between UAE and blood pressure, particularly in the presence of diabetes, indicates that these traits share common genetic determinants.

Albuminuria↗

PubMed1999

Estimating genetic correlations in natural populations.

Information on the genetic correlation between traits provides fundamental insight into the constraints on the evolutionary process. Estimates of such correlations are conventionally obtained by raising individuals of known relatedness in artificial environments. However, many species are not readily amenable to controlled breeding programmes, and considerable uncertainty exists over the extent to which estimates derived under benign laboratory conditions reflect the properties of populations in natural settings. Here, non-invasive methods that allow the estimation of genetic correlations from phenotypic measurements derived from individuals of unknown relatedness are introduced. Like the conventional approach, these methods demand large sample sizes in order to yield reasonably precise estimates, and special precautions need to be taken to eliminate bias from shared environmental effects. Provided the sample consists of at least 20% or so relatives, informative estimates of the genetic correlation are obtainable with sample sizes of several hundred individuals, particularly if supplemental information on relatedness is available from polymorphic molecular markers.

Animals↗

PubMed2004

Genetic correlations among production, body size, udder, and productive life traits over time in Holsteins.

Genetic correlations among milk, fat, and protein yields; body size composite (BSC); udder composite (UDC); and productive life (PL) in Holsteins were investigated over time. The data set contained 25,280 records of cows born in Wisconsin between 1979 and 1993. The multiple trait random regression (MT-RR) animal model included registration status, herd-year, age group, and stage of lactation as fixed effects; additive genetic effects with random regressions (RR) on year of birth using the first-order Legendre polynomial; and residual effects. Heterogeneous residual variances were considered in the model. Estimates of variance components and genetic correlations among traits from MT-RR were compared with those estimated with a multiple trait interval (MT-I) model, which assumed that every 3-yr interval was a separate trait and included the same effects as in the MT-RR model except for the RR. Genetic correlations estimated with MT-RR and MT-I models over time among all traits were compared with correlations among breeding values predicted with the single trait (ST) model without RR. Correlations among breeding values predicted with MT-RR, ST, and MT models were also calculated. Additive genetic and residual variances for all traits except PL increased over time; those for PL were constant. As a result, heritability estimates had no significant changes during the 15 yr. Genetic correlations of PL with milk, fat, protein, and BSC declined to zero or negative; those with UDC remained positive. Correlations among breeding values predicted with ST, MT, and MT-RR models were relatively high for all traits except PL. Genetic correlations between PL and other traits varied over time, with some correlations changing sign. For accurate indirect prediction of PL from other traits, the genetic correlations among the traits need to be re-estimated periodically.

Animals↗

PubMed1999

Hot and cold nociception are genetically correlated.

Recent experiments in our laboratory have revealed a genetic correlation of the sensitivity of inbred mouse strains to different assays of nociception using noxious heat stimuli. An open question is whether the property of the noxious stimulus underlying the genetic correlation is heat specifically or simply a temperature (hot or cold) in the noxious range. The existing electrophysiological, psychophysical, neurochemical, and functional imaging literatures regarding the relationship of heat pain and cold pain are quite contentious, with a number of similarities and dissociations being documented. In the present study, we tested 12 inbred mouse strains (129/J, A/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, C57BL/10J, C58/J, CBA/J, DBA/2J, RIIIS/J, and SM/J) for their reflexive withdrawal sensitivity to immersion of the tail in hot (47. 5 degrees C) and cold (-15 degrees C) water and compared the observed latencies with those obtained previously, using a 49 degrees C stimulus. All three traits displayed substantial heritabilities, ranging from 0.41 to 0.50. Strain means on these nociceptive traits displayed a high degree of cross-correlation (r = 0.49-0.77). Genetic correlation of hot and cold nociception implies that similar genes underlie interindividual variability in both traits in mice and further suggests that these nociceptive types share physiological mediation. This finding is discussed in relation to existing data in other mammals including humans.

Animals↗

PubMed2001

Estimating genetic correlations in natural populations in the absence of pedigree information: accuracy and precision of the Lynch method.

Usually, genetic correlations are estimated from breeding designs in the laboratory or greenhouse. However, estimates of the genetic correlation for natural populations are lacking, mostly because pedigrees of wild individuals are rarely known. Recently Lynch (1999) proposed a formula to estimate the genetic correlation in the absence of data on pedigree. This method has been shown to be particularly accurate provided a large sample size and a minimum (20%) proportion of relatives. Lynch (1999) proposed the use of the bootstrap to estimate standard errors associated with genetic correlations, but did not test the reliability of such a method. We tested the bootstrap and showed the jackknife can provide valid estimates of the genetic correlation calculated with the Lynch formula. The occurrence of undefined estimates, combined with the high number of replicates involved in the bootstrap, means there is a high probability of obtaining a biased upward, incomplete bootstrap, even when there is a high fraction of related pairs in a sample. It is easier to obtain complete jackknife estimates for which all the pseudovalues have been defined. We therefore recommend the use of the jackknife to estimate the genetic correlation with the Lynch formula. Provided data can be collected for more than two individuals at each location, we propose a group sampling method that produces low standard errors associated with the jackknife, even when there is a low fraction of relatives in a sample.

Analysis of Variance↗

PubMed2004

Heritabilities and genetic correlations of laying performance in Muscovy ducks selected in Taiwan.

1. Genetic parameters in the base population of a closed experimental strain of Muscovy ducks, selected for body weight at 10 weeks of age, were estimated from data in 8 successive generations, for the following traits: age at first egg (AGE1EGG), total number of eggs laid at 40 and 52 weeks of age (NEGG40 and NEGG52), number of eggs laid during 15 and 22 weeks in the first laying cycle (NEGG15W and NEGG22W), and their Box-Cox transformed data. 2. The method of multi-trait restricted maximum likelihood with an animal model was used to estimate genetic parameters. Only the results obtained with non-transformed data are shown. 3. Heritability estimates for laying performance showed moderate values, increasing little with age: 0.20+/-0.03 (AGE1EGG), 0.23+/-0.03 (NEGG40), 0.27+/-0.03 (NEGG52), 0.20+/-0.03 (NEGG15W), and 0.22+/-0.03 (NEGG22W). 4. Genetic correlations between laying traits were high. Genetic correlation between AGE1EGG and egg number was negative, it was positive between total numbers of eggs at 40 and 52 weeks and egg numbers in the first laying cycle. 5. Body weight at 10 weeks of age exhibited positive genetic correlations (0.46+/-0.06) with age at first egg and negative with egg production traits (-0.28+/-0.06 to -0.41+/-0.06). 6. The cumulated predicted genetic gains, after 7 generations of selection, expressed per genetic standard deviation unit (sigma(g)) were 0.06 sigma(g), 0.07 sigma(g), 0.17 sigma(g), 0.23 sigma(g), and 0.25 sigma(g) for AGE1EGG, NEGG40, NEGG52, NEGG15W, and NEGG22W, respectively. 7. Selecting Muscovy ducks to improve laying in Taiwanese climatic conditions would be possible using the number of eggs laid up to 52 weeks of age as the selection criterion. Because unintended selection effects for laying traits were present, the selection experiment for body weight at 10 weeks of age was not antagonistic with laying traits.

Animals↗

PubMed2004

Genetic correlations and the coevolutionary dynamics of three-species systems.

The majority of species interact with at least several others. We develop simple genetic models of coevolution between three species where interactions are mediated by quantitative traits. We assume that one of the species has two quantitative traits, each of which governs its interaction with one of the other two species. We use this model to explore how genetic correlations between the two traits in the multivariate species shape the evolutionary dynamics and outcomes of three species interactions. Our results suggest that genetic correlations are most important when at least one of the interactions is between a predator and prey or parasite and host. In these cases, genetic correlations between traits lead to a wide variety of novel coevolutionary outcomes and dynamics. In particular, genetic correlations can affect the existence and stability of coevolutionary equilibrium points, and they can lead to recurrent or permanent maladaptation. When the three species interact only as competitors or mutualists, however, genetic correlations have no effect on the outcome of coevolution. In all cases, our results reveal the surprising conclusion that both positive and negative genetic correlations between traits have qualitatively identical effects on coevolutionary dynamics.

Alleles↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMed1991

Genetic correlations among reproductive traits and uterine dimensions in mice.

The objective of this experiment was to identify relationships among reproductive and uterine traits in mice having normal or crowded uterine conditions. Littermate females were randomly assigned to be either unilaterally ovariectomized (ULO) or to remain intact (C) and to be killed either 3 d after mating (PM) or 4 d after parturition (PP) in a 2 x 2 factorial design. Measurements taken were ovulation rate (OR) and uterine length (UL), wet weight (UWW), dry weight (UDW) and displacement (UDP) in PM females and number born (NB) and implantation rate (IMP) in PP females. Heritability estimates from full-sib correlations were .18, .01, .33, .04, .14, .47 and .06 for OR, IMP, NB, UL, UWW, UDW and UDP, respectively. Phenotypic correlations among uterine measurements were moderate to high and positive. Genetic correlations for C and ULO females for OR with NB were .62 and .73, respectively. Genetic correlations between C and ULO females were .53 for NB and 1.05 for OR. Genetic correlations of UL and UWW with NB were high for C (.70 and .59, respectively) and moderate for ULO (.47 and .36, respectively). Genetic correlations between NB and other uterine dimensions were lower.

Animals↗

PubMed2005

Genetic correlations do not constrain the evolution of sexual dimorphism in the moss Ceratodon purpureus.

The trajectory of phenotypic evolution is constrained in the short term by genetic correlations among traits. However, the extent to which genetic correlations impose a lasting constraint is generally unknown. Here, I examine the genetic architecture of life-history variation in male and female gametophytes from two populations of the moss Ceratodon purpureus, focusing on genetic correlations within and between the sexes. A significant negative correlation between allocation to vegetative and reproductive tissue was evident in males of both populations, but not females. All traits showed between-sex correlations of significantly less than one, indicating additive genetic variance for sexual dimorphism. The degree of dimorphism for traits was significantly negatively associated with the strength of the between-sex correlation. The structure of genetic correlations among life-history traits was more divergent between the two populations in females than in males. Collectively, these results suggest that genetic correlations do not impose a lasting constraint on the evolution of life-history variation in the species.

Biological Evolution↗

PubMed1990

Estimation of genetic correlation: interpretation of experiments using selectively bred and inbred animals.

There is increasing interest in determining the extent to which multiple characters related to drug sensitivity are influenced by common genes. The principal method for testing for the existence of such genetic correlations has been examination of pairs of mouse or rat lines selectively bred for sensitivity or resistance to a single behavioral effect of a drug. When a pair of selected lines is found to differ significantly on some trait other than the one on which they were selected, it is commonly concluded that significant genetic correlation between the traits exists, implying the action of a common set of genes on the two responses. In addition, results from comparisons of lines of animals selected for trait X and tested for trait Y may be compared with results from lines selected for trait Y and tested for trait X. As the number of correlated responses in selected lines increases, it becomes more important to adhere to sensible, consensual guidelines for interpreting such line differences. The principles underlying phenotypic and genotypic correlational analyses with selected lines are discussed. A scheme is presented to allow standardization across laboratories of inferences about the relative strength of genetic association from experiments with selected lines. Statistical and practical experimental issues are addressed. Estimates of genetic correlations may also be derived from the correlation of mean trait values across a panel of inbred strains. Existing data have sometimes found estimates of genetic correlations made with one approach to be inconsistent with those estimated in other ways. Possible reasons for this are discussed. Finally, the relationship between phenotypic correlations and genetic correlations is discussed. Phenotypic and genetic correlations for a pair of traits may differ widely, and may even be opposite in sign. Both are characteristic of the population from which they are sampled. Phenotypic correlations estimated within selected lines may change over time, as the additive genetic variance in the selected trait is exhausted. A specific example of this phenomenon is given.

Alcoholic Intoxication↗

PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗