correlated traits
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2021 ◽  
Author(s):  
Gulnara R. Svishcheva ◽  
Evgeny S. Tiys ◽  
Elizaveta E. Elgaeva ◽  
Sofia G. Feoktistova ◽  
Paul R. H. J. Timmers ◽  
...  

We propose a novel effective framework for analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be to effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows incorporation of different GWAS models (Cox, linear and logistic) and is computationally fast.


2021 ◽  
Author(s):  
Jenna Lee Ballard ◽  
Luke Jen O'Connor

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed Pleiotropic Decomposition Regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of 5-6 traits genetically correlated with coronary disease, asthma, and type II diabetes respectively, producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes compared with the original summary statistics: by 94% and 70% for asthma and T2D out of sample, and by a predicted 300% for CAD.


2021 ◽  
Author(s):  
Lance F Merrick ◽  
Adrienne B Burke ◽  
Zhiwu Zhang ◽  
Arron H Carter

Traits with an unknown genetic architecture make it difficult to create a useful bi-parental mapping population to characterize the genetic basis of the trait due to a combination of complex and pleiotropic effects. Seedling emergence of wheat (Triticum aestivum L.) from deep planting is a vital factor affecting stand establishment and grain yield, has a poorly understood genetic architecture, and is historically correlated with coleoptile length. The creation of bi-parental mapping populations can be overcome by using genome-wide association studies (GWAS). This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait GWAS model (MT-GWAS) and three single-trait GWAS models (ST-GWAS) with the inclusion of covariates for correlated traits. The ST-GWAS models included one single locus model (MLM), and two multiple loci models (FarmCPU and BLINK). We conducted the GWAS using two populations, the first consisting of 473 varieties from a diverse association mapping panel (DP) phenotyped from 2015-2019, and the other population used as a validation population consisting of 279 breeding lines (BL) phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models for the DP. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. MT-GWAS models were able to identify large-effect markers on chromosome 5A. FarmCPU and BLINK models were able to identify many small effect markers, and the inclusion of covariates helped to identify the large effect markers on chromosome 5A. Therefore, by using multi-locus models combined with pleiotropic covariates, breeding programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence of deep-sown winter wheat.


2021 ◽  
Vol 12 ◽  
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deukhwan Lee ◽  
Noelia Ibáñez-Escriche

There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.


Author(s):  
Muhammad Irfan Ullah ◽  
Shahzadi Mahpara ◽  
Rehana Bibi ◽  
Rahmat Ullah Shah ◽  
Rehmat Ullah ◽  
...  

2021 ◽  
Vol 8 ◽  
Author(s):  
Tanja C. Zerulla ◽  
Philip K. Stoddard

Melanin-based color patterns are an emerging model for studying molecular and evolutionary mechanisms driving phenotypic correlations. Extensive literature exists on color patterns and their correlated traits in the family Poeciliidae, indicating that these fishes are tractable models. We review the biology of polymorphic melanic side-spotting patterns characterized by macromelanophores forming irregular spotted patterns across fishes’ flanks. These patterns are present in the genera Gambusia, Limia, Phalloceros, Poecilia, and Xiphophorus. Their presence is controlled by dominant genes on autosomes or sex chromosomes. Variation in expression is under polygenic control; however, these genes’ identities are still largely unknown. In some Gambusia holbrooki and Poecilia latipinna, expression is dependent on low temperature exposure, but underlying molecular mechanisms are unknown. Spotted fish develop melanoma in rare cases and are a well-developed model for melanoma research. Little is known about other physiological correlates except that spotted G. holbrooki males exhibit higher basal cortisol levels than unspotted males and that metabolic rate does not differ between morphs in some Xiphophorus species. Behavioral differences between morphs are widespread, but specific to population, species, and social context. Spotted G. holbrooki males appear to be more social and more dominant. Juvenile spotted G. holbrooki have lower behavioral flexibility, and spotted X. variatus exhibit greater stress resistance. Findings conflict on whether morphs differ in sexual behavior and in sexual selection by females. Melanic side-spotting patterns are uncommon (<30%) in populations, although extreme high-frequency populations exist. This low frequency is surprising for dominant genes, indicating that a variety of selective pressures influence both these patterns and their correlated traits. Little is known about reproductive life history traits. Spotted G. holbrooki are larger and have higher survival when uncommon, but underlying mechanisms remain unknown. Spotted morphs appear to have a strong selective advantage during predation. Predators prefer to attack and consume unspotted morphs; however, this preference disappears when spotted G. holbrooki males are common, indicating negative frequency-dependent selection. Spotted morphs are preferred socially under turbid conditions, but other environmental factors that shape phenotypic correlations and morph fitness have not been studied. Finally, we present questions for future studies on melanic side-spotting patterns.


2020 ◽  
Vol 11 ◽  
Author(s):  
Nancy L. Rodríguez-Castañeda ◽  
Pedro L. Ortiz ◽  
Montserrat Arista ◽  
Eduardo Narbona ◽  
Mª Luisa Buide

Flower color, as other floral traits, may suffer conflicting selective pressures mediated by both mutualists and antagonists. The maintenance of intraspecific flower color variability has been usually explained as a result of direct selection by biotic agents. However, flower color might also be under indirect selection through correlated traits, since correlations among flower traits are frequent. In this study, we aimed to find out how flower color variability is maintained in two nearby populations of Silene littorea that consistently differ in the proportions of white-flowered plants. To do that, we assessed natural selection on floral color and correlated traits by means of phenotypic selection analysis and path analysis. Strong directional selection on floral display and flower production was found in both populations through either male or female fitness. Flower color had a negative indirect effect on the total male and female fitness in Melide population, as plants with lighter corollas produced more flowers. In contrast, in Barra population, plants with darker corollas produced more flowers and have darker calices, which in turn were selected. Our results suggest that the prevalence of white-flowered plants in Melide and pink-flowered plants in Barra is a result of indirect selection through correlated flower traits and not a result of direct selection of either pollinators or herbivores on color.


Evolution ◽  
2020 ◽  
Vol 74 (6) ◽  
pp. 1112-1123
Author(s):  
Reuven Dukas ◽  
Janice L. Yan ◽  
Andrew M. Scott ◽  
Surabhi Sivaratnam ◽  
Carling M. Baxter

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