I don't remember that statement in particular but I may have just meant with no reasonable assumptions. Perhaps I meant if one could assume that variation at each landmark could be analyzed separately - which it cannot because random variation at one landmark will change the alignment at other landmarks and thus the apparent variation and covariation at other landmarks. Other way would be to assume you knew the true alignment of the specimens and it was not influenced by random variation. __________________F. James Rohlf, Distinguished Prof. Emeritus Dept. Anthropology and Ecology & Evolution Stonybrook University -------- Original message --------From: "[email protected]" <[email protected]> Date: 12/22/22 4:30 AM (GMT-10:00) To: Morphmet <[email protected]> Subject: Re: [MORPHMET2] Re: Is it possible to calculate point-wise heritability based on Procrustes shape coordinates I happen to read this thread 3 years ago and got interested in Prof. Jim's statement that "
It was also asked about computing heritability “for each landmark”. That would be difficult without some strong assumptions."I understand there has been discussion in this forum about the inappropriateness of analysis of landmarks one at a time after GPA. However, I am curious to learn under what "strong assumption" could we perform analysis "one landmark at a time"?Shape by definition is the configuration of ALL landmarks after GPA. This means one should never analyze landmarks one at a time with the slightest hope that Procrustes shape coordinates for a single landmark represent local shape information. I think understanding the exceptional condition where one could analyze landmarks one at a time helps me gain deeper understanding of this issue.Thank you.KieranOn Wednesday, July 31, 2019 at 9:49:38 PM UTC+8 [email protected] wrote:Dear Prof. Houle,Thank you very much for your interest in my questions. My dataset contains 3D body shape data of people from peopole of two ethnicities. Based on such phenotypic data, I wish to estimate population differentiation and selection differential/gradient, and total predicted response to selection. I agree with you that estimation of these quantities requires phenotypic and additive genetic variance-covariance matrix rather than heritability. I have only phenotypic data at the moment, although I will obtain the genetic data sometime later.I understand that one approach to estimate the G matrix is to multiply the P matrix by an estimated value of heritability. Implicit in this approach is the assumption of proportionality. Am I right that it is impossible to estimate G in the absence of genetic data, apart from the approach above?Regards,PatrickOn Wednesday, 31 July 2019 03:10:31 UTC+8, dhoule wrote: First of all, quantitative genetic concepts are defined relative to a reference or base population. If you are mixing individuals from a wide geographic area, what is your population that you are making inferences about? Second, you can't do any quantitative genetics unless you have both 'unrelated' (relative to the defined reference population), and individuals with known degrees of relationship. Do you have genomic data on these individuals? If all you have are individuals of unknown relationship, all you can do is calculate the P matrix, then make some very strong assumption - like heritability is 0.3, and get a sort of proxy G matrix. This might be better than nothing. Why do you want to know the heritability? To predict the magnitude of changes, you need the genetic variance and covariances, rather than heritabilities. David Houle On 7/30/2019 11:59 AM, lv xiao wrote: A huge thank you to all who have contributed to my questions. It did took me a while to read the articles kindly suggested. I now have some basic understanding of heritability, selection differential, selection gradient, etc. I have 3D data on body shape of Europeans and Africans and I wish to obtain estimates of heritability and visualize shape changes associated with selection differential and response to selection. The first thing I would like to do is to estimate the additive genetic variace-covariance matrix G, and the phenotypic variance-covariance matrix P. Software such as ACE and Wombat have been used for such purpose. The essence of this seems to be a multivariate mixed-effect model where the dependent variable is the shape data in matrix and the independent variables consist of both fixed and random effects. However, my dataset is not related to full-sibling design and I would like to ask how could I derive matrix P and G from my phenotypic data? Once these two matrices are estimated, I will be able to performe further analyses on my own. Thank you. On Wednesday, 24 July 2019 21:12:13 UTC+8, lv xiao wrote: Dear all, Heritability is traditionally estimated based on genetic data. However, it has also been estimated from phenotypic data. In this case, twin comparison is the common study design. My question is if I have a sample of indenpendent individuals whose 3D shape data are available, is it possible to calculate heritability based on Procrustes shape coordinates for overall shape and for each landmark? Several studies have used the Sequential Oligogenic Linkage Analysis Routines (SOLAR) package for estimation of heritability of craniofacial trait based on independent samples (J. Anat.(2009) 214, pp19–35; THE ANATOMICAL RECORD 298:1535–1547 (2015)). However, these traits are linear distances, which are quite different from "shape" based on Procrustes shape coordinates. Thanks -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/6d5d4ad1-936f-4a2a-a33b-3445a59a6373%40googlegroups.com. -- David Houle Department of Biological Science Florida State University 319 Stadium Drive Tallahassee, FL 32306-4295 850-339-5670 http://bio.fsu.edu/~dhoule/ -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/aab303e0-2433-41c1-9175-b3351fdb2c04n%40googlegroups.com. -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/63bbce37.170a0220.9fd95.7625%40mx.google.com.
