Biology subjects

Ritland, K.

Publications and source records attributed to Ritland, K..

3 recordsLinked to original sources

bioRxiv2022

Relatedness coefficients and their applications for triplets and quartets of genes

Relatedness coefficients which seek the identity-by-descent of genetic markers are described. The markers are in groups of two, three or four, and if four, can consist of two pairs. It is essential to use cumulants (not moments) for four-marker-gene probabilities, as the covariance of homozygosity, used in 4-marker applications, can only be described with cumulants. A covariance of homozygosity between pairs of markers arises when populations follow a mixture distribution. Also, the probability of four markers all identical-by-descent equals the normalized fourth cumulant. In this paper, a "genetic marker" generally represents either a gene locus or an allele at a locus. Applications of three marker coefficients mainly involve conditional regression, and applications of four marker coefficients can involve identity disequilibrium. Estimation of relatedness using genetic marker data is discussed. However, three- and four-marker estimators suffer from statistical and numerical problems, including higher statistical variance, complexity of estimation formula, and singularity at some intermediate allele frequencies.

genetics↗

bioRxiv2020

Performing Parentage Analysis for Polysomic Inheritances Based on Allelic Phenotypes

Polyploidy poses several problems for parentage analysis. We present a new polysomic inheritance model for parentage analysis based on genotypes or allelic phenotypes to solve these problems. The effects of five factors are simultaneously accommodated in this model: (i) double-reduction, (ii) null alleles, (iii) negative amplification, (iv) genotyping errors and (v) self-fertilization. To solve genotyping ambiguity (unknown allele dosage), we developed a new method to establish the likelihood formulas for allelic phenotype data and to simultaneously include the effects of our five chosen factors. We then evaluated and compared the performance of our new method with three established methods by using both simulated data and empirical data from the cultivated blueberry (Vaccinium corymbosum). We also developed and compared the performance of two additional estimators to estimate the geno-typing error rate and the sample rate. We make our new methods freely available in the software package O_SCPLOWPOLYGENEC_SCPLOW, at http://github.com/huangkang1987/polygene.

genetics↗

bioRxiv2018

Partial correlation analysis of transcriptomes helps detangle the growth and defense network in spruce

O_LIIn plants, there can be a trade-off between resource allocations to growth versus defense. Here, we use partial correlation analysis of gene expression to make inferences about the nature of this interaction.\nC_LIO_LIWe studied segregating progenies of Interior spruce subject to weevil attack. In a controlled experiment, we measured pre-attack plant growth and post-attack damage with several morphological measures, and profiled transcriptomes of 188 progeny.\nC_LIO_LIWe used partial correlations of individual transcripts (ESTs) with pairs of growth/defense traits to identify important nodes and edges in the inferred underlying gene network, e.g., those pairs of growth/defense traits with high mutual correlation with a single EST transcript. We give a method to identify such ESTs.\nC_LIO_LIA terpenoid ABC transporter gene showed strongest correlations (P=0.019); its transcript represented a hub within the compact 166-member gene-gene interaction network (P=0.004) of the negative genetic correlations between growth and subsequent pest attack. A small 21-member interaction network (P=0.004) represented the uncovered positive correlations.\nC_LIO_LIOur study demonstrates partial correlation analysis identifies important gene networks underlying growth and susceptibility to the weevil in spruce. In particular, we found transcripts that strongly modify the trade-off between growth and defense, and allow identification of networks more central to the trade-off.\nC_LI

plant biology↗