scholarly journals Heritability is a poor, if not unhelpful, measure of complex human behavioral processes

2021 ◽  
Author(s):  
Agustin Fuentes ◽  
Kevin A Bird

Heritability is not a measure of the relative contribution of nature vis-à-vis nurture, nor is it the phenotypic variance explained by or due to genetic variance. Heritability is a correlative value. The evolutionary and developmental processes associated with human culture challenge the use of ‘heritability’ for understanding human behavior.

1996 ◽  
Vol 62 (1) ◽  
pp. 171-180 ◽  
Author(s):  
S. van der Beck ◽  
J. A. M. van Arendonk

AbstractThe value of using a marker for a quantitative trait locus (QTL) affecting a sex-limited trait in an outbred poultry breeding nucleus was studied. Marker and QTL were in linkage equilibrium in the base population. The recombination rate between marker and QTL was 0-05. A closed nucleus with 9000 chickens per generation was deterministically simulated. The genetic model contained polygenes and a QTL linked to a marker. Genetic effects explained proportionately 0·3 of the phenotypic variance before selection. Under selection, polygenic variance reached an equilibrium and QTL variance decreased continuously over time. Cocks were selected in two steps. First the best cocks of each full-sib family were selected (within-family selection) while final selection took place after information on fiill-sibs was available. Hens were selected after they had completed production. The effect of using marker information in estimating breeding values was studied in an ongoing breeding programme. Transmission of marker alleles was always traceable. Cumulative response over five generations increased proportionately by 0·06 to 0·13 if a marker linked to a QTL that explained 0·2 of the genetic variance was used. Cumulative response increased up to 0·28 if the QTL explained 0-8 of the genetic variance. Additional response due to the use of a marker increased with increasing intensity of within-family selection of cocks, increased with increasing variance explained by the QTL and was higher if within-family selection of cocks was carried out after rather than before their sibs had complete records.


2017 ◽  
Vol 40 ◽  
pp. 36481
Author(s):  
Fernando Amarilho-Silveira ◽  
Nelson José Laurino Dionello ◽  
Gilson De Mendonça ◽  
Jaqueline Freitas Motta ◽  
Tiago Albandes Fernandes ◽  
...  

This study aimed to estimate the components of (co)variance, genetic and phenotypic parameters and trends for birth weight. We used 783 birth weight records, between 2012 to 2016, of Texel sheep reared in extensive system. The components of (co)variance and the genetic parameters were estimated using six different animal models, using the restricted maximum likelihood method (REML). The model that best fit the data was Model 3, with estimates of direct additive genetic variance of 0.004, maternal permanent environment variance of 0.164, heritability coefficient of 0.011 and phenotypic variation attributed to the maternal permanent environment of 0.394. For the genetic trend, we observed a genetic gain of 0.413% and for the phenotypic trend, a phenotypic gain of 0.159 kg, between 2012 and 2016 were found. Estimates of direct heritability and proportion of the phenotypic variance explained by the maternal permanent environment presented lower and higher values, respectively, in comparison to other studies. For trends, both genetic and phenotypic, there were gains in birth weight between 2012 and 2016. 


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.


2001 ◽  
Vol 26 (1) ◽  
pp. 237-249 ◽  
Author(s):  
J.E. Pryce ◽  
R.F. Veerkamp

AbstractIn recent years there has been considerable genetic progress in milk production. Yet, increases in yield have been accompanied by an apparent lengthening of calving intervals, days open, days to first heat and a decline in conception rates, which appears to be both at the genetic and phenotypic level. Fertility has a high relative economic value compared to production traits such as protein, making it attractive to include in a breeding programme. To do this there needs to be genetic variance in fertility. Measures of fertility calculated from service dates have a small genetic compared to phenotypic variance, hence heritability estimates are small, typically less than 5%, although coefficients of genetic variance are comparable to those of production traits. Heritabilities of commencement of luteal activity determined using progesterone profiles are generally higher, and have been reported as being from 0.16 to 0.28, which could be because of a more precise quantification of genetic variance, as management influences such as delaying insemination and heat detection rates are excluded. However, it might not be the use of progesterone profiles alone, as days to first heat observed by farm staff has a heritability of 0.15. The most efficient way to breed for improved fertility is to construct a selection index using the genetic and phenotypic parameter estimates of all traits of interest in addition to their respective economic values. Index traits for fertility could include measures such as calving interval, days open, days to first service, or days to first heat but there may also be alternative measures. Examples include traits related to energy balance, such as live weight and condition score (change), both of which have higher heritabilities than fertility measures and have genetic correlations of sufficient magnitude to make genetic progress by using them feasible. To redress the balance between fertility and production, some countries already publish genetic evaluations of fertility including: Denmark, Finland, France, Germany, Israel, The Netherlands, Norway and Sweden.


Genetika ◽  
2016 ◽  
Vol 48 (2) ◽  
pp. 643-652 ◽  
Author(s):  
Baoyan Jia ◽  
Xinhua Zhao ◽  
Yang Qin ◽  
Muhammad Irfan ◽  
Tae-Heon Kim ◽  
...  

A recombinant inbred lines (RILs) population of 90 lines were developed from a subspecies cross between an indica type cultivar, ?Cheongcheong?, and a japonica rice cultivar, ?Nagdong? was evaluated for leaf traits in 2009. A genetic linkage map consisting of 154 simple sequence repeat (SSR) markers was constructed, covering 1973.6 cM of 12 chromosomes with an average map distance of 13.9 cM between markers. By composite interval mapping method a total of 19 QTLs were identified for the leaf traits on 5 chromosomes (Chr.1, Chr.3, Chr.6, Chr.8 and Chr.11). The percentage of phenotypic variance explained by each QTL varied from 8.1% to 29.4%. Five pleiotropic effects loci were identified on chromosomes 1,6.


2020 ◽  
Vol 10 (7) ◽  
pp. 2297-2315 ◽  
Author(s):  
Carolina Chavarro ◽  
Ye Chu ◽  
Corley Holbrook ◽  
Thomas Isleib ◽  
David Bertioli ◽  
...  

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.


2020 ◽  
Vol 30 (4) ◽  
pp. 2307-2320
Author(s):  
Anne Biton ◽  
Nicolas Traut ◽  
Jean-Baptiste Poline ◽  
Benjamin S Aribisala ◽  
Mark E Bastin ◽  
...  

Abstract We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.


2011 ◽  
Vol 3 (4) ◽  
pp. 129-133
Author(s):  
Supriyo CHAKRABORTY ◽  
Sheng-Chu WANG ◽  
Zhao-Bang ZENG

Polygenes (QTLs) for grain yield were mapped on rice chromosomes under two moisture stress environments by multiple interval mapping (MIM) method in a double haploid (DH) population derived from a cross between a deep-rooted japonica and a shallow-rooted indica genotype. In environment 1 (E1), the MIM detected a total of six QTLs for grain yield on chromosomes-two QTLs on chromosome 1 and four QTLs on chromosome 5 along with one additive x additive epistasis. But in environment 2 (E2), the MIM detected five QTLs for grain yield on two chromosomes-three QTLs on chromosome 1 and two QTLs on chromosome 7. One common QTL on chromosome 1 flanked by the markers RG109-ME1014 was detected in both the environments, although the other detected QTLs differed between environments. The magnitude of QTL effect, percent genetic variance and percent phenotypic variance explained by each QTL was also estimated in both environments. The common QTL explained about 26.05 and 13.93% of genetic variance in E1 and E2, respectively. Estimated broad sense heritability for grain yield was 48.01 in E1 and 25.27% in E2.


1999 ◽  
Vol 73 (2) ◽  
pp. 165-176 ◽  
Author(s):  
J. MERILÄ ◽  
R. PRZYBYLO ◽  
B. C. SHELDON

An increasing amount of evidence indicates that different forms of environmental stress influence the expression of genetic variance in quantitative traits and, consequently, their evolvability. We investigated the causal components of phenotypic variance and natural selection on the body condition index (a trait often related to fitness in wild bird populations) of blue tit (Parus caeruleus) nestlings under contrasting environmental conditions. In three different study years, nestlings grown under a poor feeding regime attained lower body condition than their full-sibs grown under a good feeding regime. Genetic influences on condition were large and significant in both feeding regimes, and in all three study years. However, although estimates of additive genetic variance were consistently higher in the poor than in the good environment, heritability estimates for body condition index were very similar in both environments due to higher levels of environmental variance in the poor environment. Evidence for weak genotype×environment interactions was obtained, but these contributed little to variance in nestling condition. Directional natural selection on fledging condition of nestlings was detected, and there were no indications of year or environmental effects on the form and intensity of selection observed, in a sample of 3659 nestlings over four years. However, selection on fledging condition was very weak (standardized selection gradient, β=0·027±0·016 SE), suggesting that, in the current population, the large additive genetic component to fledging condition is not particularly surprising. The results of these analyses are contrasted with those obtained for other populations and species with similar life-histories.


2020 ◽  
Author(s):  
Zenab Tamimy ◽  
Sofieke T. Kevenaar ◽  
Jouke Jan Hottenga ◽  
Michael D. Hunter ◽  
Eveline L. de Zeeuw ◽  
...  

AbstractThe classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height.


Sign in / Sign up

Export Citation Format

Share Document