The heritability of human personality is well-established. Recent research indicates that nonadditive genetic effects, such as dominance and epistasis, play a large role in personality variation. One possible explanation for the latter finding is that there has been recent selection on human personality. To test this possibility, we estimated additive and nonadditive genetic variance in personality and subjective well-being of zoo-housed orangutans. More than half of the genetic variance in these traits could be attributed to nonadditive genetic effects, modeled as dominance. Subjective well-being had genetic overlap with personality, though less so than has been found in humans or chimpanzees. Since a large portion of nonadditive genetic variance in personality is not unique to humans, the nonadditivity of human personality is not sufficient evidence for recent selection of personality in humans. Nonadditive genetic variance may be a general feature of the genetic structure of personality in primates and other animals.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10519-012-9537-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Complex genetic interactions affect susceptibility to Alzheimer's disease risk in the BIN1 and MS4A6A loci.
Geroscience
January 2025
Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St, Durham, NC, 27705, USA.
Genetics is the second strongest risk factor for Alzheimer's disease (AD) after age. More than 70 loci have been implicated in AD susceptibility so far, and the genetic architecture of AD entails both additive and nonadditive contributions from these loci. To better understand nonadditive impact of single-nucleotide polymorphisms (SNPs) on AD risk, we examined individual, joint, and interacting (SNPxSNP) effects of 139 and 66 SNPs mapped to the BIN1 and MS4A6A AD-associated loci, respectively.
View Article and Find Full Text PDFKPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.
Brief Bioinform
November 2024
State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road, Beijing, 100193, China.
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects.
View Article and Find Full Text PDFGenetic analysis and association detection of agronomic traits in maize genotypes.
Sci Rep
January 2025
Department of Horticulture, Karaj Branch, Islamic Azad University, Karaj, Iran.
In maize breeding, enhancing yield through genetic insights is crucial yet challenged by the complex interplay of agronomic traits. This study utilized a diallel mating design involving nine advanced early maize lines to dissect the genetic architecture underlying key agronomic traits and their impact on yield. Over two consecutive years (2018-2019 and 2019-2020), 36 hybrids derived from these lines were grown across two locations, Karaj, Alborz, Iran and Kermanshah (2019-2020), Iran, in a randomized complete block design with three replications.
View Article and Find Full Text PDFThe highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR.
Nat Commun
December 2024
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, CH-8057, Switzerland.
Transcription factor binding sites (TFBSs) are important sources of evolutionary innovations. Understanding how evolution navigates the sequence space of such sites can be achieved by mapping TFBS adaptive landscapes. In such a landscape, an individual location corresponds to a TFBS bound by a transcription factor.
View Article and Find Full Text PDFDifferentiation status determines the effects of IFNγ on the expression of PD-L1 and immunomodulatory genes in melanoma.
Cell Commun Signal
December 2024
Department of Biomedical Science, Faculty of Medicine, BioMedical Center, University of Iceland, Reykjavík, Iceland.
Background: Melanoma cells frequently dedifferentiate in response to inflammation which can increase responses to certain cytokines. Interferon-γ (IFNγ) is an integral part of the anti-tumor immune response and can directly induce both differentiational changes and expression of immunosuppressive proteins in melanoma cells. How the differentiation status of melanoma cells affects IFNγ responses remains unclear.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!