Testing for genetic assimilation with phylogenetic comparative analysis: Conceptual, methodological, and statistical considerations

Genetic assimilation is a process that leads to reduced phenotypic plasticity during adaptation to novel conditions, a potentially important phenomenon under global environmental change. Null expectations when testing for genetic assimilation, however, are not always clear. For instance, the statistical artifact of regression to the mean could bias us towards detecting genetic assimilation when it has not occurred. Likewise, the specific mechanism underlying plasticity expression may affect null expectations under neutral evolution. We used macroevolutionary numerical simulations to examine both of these important issues and their interaction, varying whether or not plasticity evolves, the evolutionary mechanism, trait measurement error, and experimental design. We also modified an existing reaction norm correction method to account for phylogenetic non-independence. We found: 1) regression to the mean is pervasive and can generate spurious support for genetic assimilation; 2) experimental design and post-hoc correction can minimize this spurious effect; and 3) neutral evolution can produce patterns consistent with genetic assimilation without constraint or selection, depending on the mechanism of plasticity expression. Additionally, we re-analyzed published macroevolutionary data supporting genetic assimilation, and found that support was lost after proper correction. Considerable caution is thus required whenever investigating genetic assimilation and reaction norm evolution at macroevolutionary scales.

PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

PSXII-23 Identification and evaluation of novel fertility traits using automated activity monitor data from commercial dairy herds

The reproductive performance of dairy herds is a determinant factor in their production efficiency and long-term profitability. While the genetic improvement of fertility in cows has been a top breeding objective, its success has been constrained by the low heritability of current traits and the negative impact of management practices on trait measurement accuracy. The recent development and adoption of automated sensor technologies, such as activity monitors, within dairy production systems have provided new opportunities to identify relevant and cost-effective phenotypes of fertility. To date, several activity-based measurements of estrus expression have been associated with fertility outcomes and may, therefore, be suitable predictors of female fertility. The objective of this study was to identify and evaluate novel estrus-related fertility traits using automated activity monitor (AAM) data from commercial dairy herds. A total of 8,730,987 records consisting of 10,529 estrus events were collected between January 2019 and March 2020 from 2,434 cows and 689 heifers of two U.S. commercial herds, where cows were fitted with collar-mounted accelerometers (Heatime, SCR Engineers Ltd., Netanya, Israel). The AAM dataset contained individual bi-hourly activity and rumination patterns, as well as computed fertility index scores, which were used to derive fertility traits related to estrus duration and intensity. The mean number of estrus events per cow and heifer was 4.06 ± 0.04 and 2.63 ± 0.07, respectively, with 1,408 of estruses resulting in pregnancy. Traditional fertility traits were calculated from the breeding record information of 3,255 animals. An estimation of the heritabilities along with genetic and phenotypic correlations between the novel estrus-related fertility traits and traditional fertility traits was undertaken using the program AIREML. Outputs from this project are expected to provide further insight on how to best use AAM measurements to improve selection for reproductive performance in commercial dairy herds.

Fine-scale population structure confounds genetic risk scores in the ascertainment population

Genetic risk scores (GRS), also known as polygenic risk scores, are a tool to estimate individuals’ liabilities to a disease or trait measurement based solely on genetic information. They have value in clinical applications [1] as well as for assessing relationships between traits and discovering causal determinants of complex disease [2, 3]. However, it has been shown that these scores are not robust to differences across continental populations [4, 5] and may not be portable within them either [6]. Even within a single population, they may have variable predictive ability across sexes and socioeconomic strata [7], raising questions about their potential biases. In this paper, we investigated the accuracy of two different GRS across population strata of the UK Biobank [8], separated along principal component (PC) axes, considering different approaches to account for social and environmental confounders. We found that these scores did not predict the real differences in phenotypes observed along the first principal component, with evidence of discrepancies on axes as high as PC45. These results demonstrate that the measures currently taken for correcting for population structure are not sufficient, and the need for social and environmental confounders to be factored into the creation of GRS.

Seeing spots: quantifying mother-offspring similarity and assessing fitness consequences of coat pattern traits in a wild population of giraffes (Giraffa camelopardalis)

Polymorphic phenotypes of mammalian coat coloration have been important to the study of genetics and evolution, but less is known about the inheritance and fitness consequences of individual variation in complex coat pattern traits such as spots and stripes. Giraffe coat markings are highly complex and variable and it has been hypothesized that variation in coat patterns most likely affects fitness by camouflaging neonates against visually hunting predators. We quantified complex coat pattern traits of wild Masai giraffes using image analysis software, determined the similarity of spot pattern traits between mother and offspring, and assessed whether variation in spot pattern traits was related to fitness as measured by juvenile survival. The methods we described could comprise a framework for objective quantification of complex mammal coat pattern traits based on photographic coat pattern data. We demonstrated that some characteristics of giraffe coat spot shape were likely to be heritable, as measured by mother-offspring regression. We found significant variation in juvenile survival among phenotypic groups of neonates defined by multivariate clustering based on spot trait measurement variables. We also found significant variation in neonatal survival associated with spot size and shape covariates. Larger spots (smaller number of spots) and irregularly shaped or rounder spots (smaller aspect ratio) were correlated with increased survival. These findings will inform investigations into developmental and genetic architecture of complex mammal coat patterns and their adaptive value.

Determine Entrepreneurial Characteristics Using Mobile Android Game Freezer

<h1>Psychometric Test has been used as an individual trait measurement for a long time for both experienced entrepreneurs as well as young generation who are looking for their potential in entrepreneurship. The assessment and measurement of strength and weaknesses of key entrepreneurial traits provides the entrepreneurial level and personal development planning for entrepreneur or those who want to venture into business. However, the traditional psychometric test lacks of fun element which is less enjoyable activity during answering the test. Furthermore, it requires basic understanding of business jargon that difficult for certain respondents to provide accurate respond. The purpose of this study is to propose a gamification approach which is mobile game application called ‘Freezer’ that can measured entrepreneurial traits of the player. In this sense, ‘Freezer’ creates a simulated scenario for respondents to play as an ice cream business owner and to win as a successful business person, each action done will be measured as entrepreneurial traits criteria. At the end of the game, the result will be presented that can describe the entrepreneurial characteristics of the individuals based on their planning and activities during playing the game.</h1><p> </p><p> </p>

Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture

Our capacity to measure performance- and efficiency-related phenotypes in grazing livestock in a timely manner, ideally in real-time without human interference, has been severely limited. Future demands and constraints on grazing livestock production will require a step change beyond our current approaches to obtaining phenotypic data. Animal phenomics is a relatively new term that describes the next generation of animal trait measurement, including methodologies and equipment used to acquire data on traits, and computational approaches required to turn data into phenotypic information. Phenomics offers a range of emerging opportunities to define new traits specific to grazing livestock, including intake and efficiency at pasture, and to measure many traits simultaneously or at a level of detail previously unachievable in the grazing environment. Application of this approach to phenotyping can improve the precision with which nutritional and other management strategies are applied, enable development of predictive biological traits, and accelerate the rate at which genetic gain is achieved for existing and new traits. In the present paper, we briefly outline the potential for livestock phenomics and describe (1) on-animal sensory-based approaches to develop traits diagnostic of productivity and efficiency, as well as resilience, health and welfare and (2) on-farm methods for data collection that drive management solutions to reduce input costs and accelerate genetic gain. The technological and analytical challenges associated with these objectives are also briefly considered, along with a brief overview of a promising field of work in which phenomics will affect animal agriculture, namely efficiency at pasture.