Quantitative Genetics - Science topic

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There are several software options available for conducting line x tester analysis that can handle multiple environments, including drought and control treatments. Here are a few options:

The Kruskal–Wallis test by ranks or Kruskal–Wallis H test (= one-way ANOVA on ranks) is a non-parametric method for testing whether samples originate from the same distribution (cited after: Kruskal–Wallis H Test using SPSS Statistics, Laerd Statistics).

It's not really evolution, but adaptation or exaptation, since you are not considering new species arising :)

Severine Roques did you ever get SPAGEDI to work on SNPS? I can't seem to find papers that did accomplish that although I also don't see why it shouldn't work...

And if yes, how did you make the SPAGEDI input file??

To me, big no, viral diseases are systemic so harvesting already infected pods could be a source of contamination for the new progenies.

Hi Do Ngoc duy

Thank you very much

Regards

R is free and has high flexibility in most genetic and statistical analyses.

Dear Yansen Chen

Wish you success,

The meaning of matlab is the matrix laboratory. So is sought originally for solve matrices. Please see the link: https://cimss.ssec.wisc.edu/wxwise/class/aos340/spr00/whatismatlab.htm#:~:text=The%20name%20MATLAB%20stands%20for,with%20input%20from%20many%20users.

Best wishes

Dear Luciana, thank you so much for the suggestion.

I will try it with your way.

thanks

can you tell us how you overcame this problem?

Oster Francis Nwachi

In addition to a MAF threshold you need to set an imputation quality or R2 threshold as well for post-imputation QC.

Benedikt

I used this software for all my analyses. The significant level here for genetic correlation is determined by standard error. So, your values are correct. I also suggest to contact Prof Utz. He is so friendly professor and he will answer you and give you more details. Try to google genetic correlation by standard error or just use incorporate keywords

In R:

If you put your allele frequencies into a matrix pmat (one row per marker, pad with zeros if needed)

e.g.

pmat <- rbind(

c(0.885, 0.115,rep(0,8)),

c(0.76, 0.14, 0.1,rep(0,7)),

c(0.59, 0.2, 0.1, 0.07, 0.04,rep(0,5)),

c(0.39, 0.15, 0.11, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05) )

(4 test marker systems from Waits et al, Mol Ecol 2001)

then use:

S2 <- rowSums(pmat^2)

PIsib <- prod((1+2*S2+2*S2*S2-rowSums(pmat^4))/4)

(result for these 4 markers is 0.098)

Thank you everyone for your answers, have been very useful.

Thanks for your input Salvador. 

Cheers, 

Geraldo

I strongly recommend to use BLUE, as you are doing a two-stage analysis. BLUE will allow you to have an 'adjusted mean' for each genotype according to design effects, and maybe other covariates in your model. And this is what you want, a more precise mean.This translates into using your model effects (for example replicate fixed and incomplete blocks and plots random), but your genotype (or clone) effect FIXED.

If you use BLUP then you are doing shrinkage of your genetic effects. This will mean that your genetic effects are moved towards the mean according to theri information, yes, they will be your best predictions of those random effects, but they will be adjusted by theri sample size and the vairance associated with theri data. This is what a random effects does, but the issue is that you eliminate part of the genetic signal if is random, and then often you will end up with more noise than what you want for your GWAS. Hence, you fit is as fixed, and then once you do your GWAS it will be a random effect, but it will not be double shrinkaged.

Good luck

What is the size of the F1 population? If you intend to do map based cloning of the gene, it would be essential to increase the size of the population substantially. If you had a size of below 200 individuals for example, it will be worthwhile to increase to over 1000 individuals. You would need to genotype this population with markers flanking the QTL to identify individuals showing recombination events. You might also need to develop more closely linked markers within the region. Good luck! 

@wim e crusio..Thanq sir for ur valuable suggestions...i have done both concordance nd correlation...but variables significant in correlation havent shown good concordance rate and negative heritability estimate ... is it ok if i stick to correlation alone...

Hi Sall, 

Yes your SStotal= SSE+ SSG+SSGXE+SSError.

Absolutely, yes I am wondering if there are good practices for the assembly/mapping and calling in handling nanopore data. You're right, the population genomics analysis I will be doing is downstream, but I wanted to mention it in case some of the inefficiencies of nanopore data have to be considered more strongly in the upstream processing in order for them not to affect the downstream population genomics analysis. If you have any advice, that would be much appreciated! Thank you!

"OUT OF MEMORY" always shows up when the data input file does not meet the requirements, in all the cases it has happened to me - it was a space/gap left at the end of the row

Hello Vlatko, I do not like transformations in general, unless strictly necessary. If I understand correctly I'd rather use a model that take the variables that you mention and let the data tell me if this variables are really having an effect on the response.

Rahul this will depend on the model you are building. Is this a split plot design? When you build the model to estimate BLUES you are treating genotypes as fixed to estimate the BLUES and another model treating genotypes as random to estimates BLUPS. The reason you are getting negative variances is not because of REML it is because you have left the "unbounded variance components" this is useful in some cases. But I would recommend running REML. If they are negative and you leave them bounded they will be estimated as zero. Hope this helps.

With some software you can model this as two traits - males have a value for one of the traits and missing for the other and the other way round for females. Genetic relationships allow estimating the genetic covariance, but there is no information on residual covariance which can be fixed at a value which you then ignore. I don't know if MCMChmm can do this, but worth looking at.

 Dear Shaheed,  thank you for you answer, and I agree with you that selecting aolygenic trait is the best way to improve grain yield of a new line version.there was so many studying with me in KSU, USA, finished his PhP in corn breeding, his name Salim khan, please if you something about him let me know, thanks in advance.

You can use PPM (Plant Preservative Mixture) @ 1ml/litre of culture media. This is a American Product and 100% safe. No side effect on culture of explant.

There are many papers regarding the effect of SSR markers on economically important traits. Although their PIC are much more higher than SNPs but SNPs are much more abundant and amenable to throughput technologies. This is the reason we use SNPs for breeding. Here is a good paper: http://www.sciencedirect.com/science/article/pii/S2214024714000100

The commonly applied coefficient of relationship for the first generation eucalypt progeny of 1/2.5 appears to be quite suitable for correcting variance component and heritability estimates.  http://dx.doi.org/10.20886/ijfr.2016.3.2.119-127

QTL mapping give low resolution but give you high statistical power for detecting a QTL. The disadvantage is that you are limited to the genetic diversity present into the parents of your segregating population. You could think of using advanced intercrosses for increasing the resolution.

GWAS offers you very fine resolution, almost to the bp resolution, however, the power for detecting a QTL will be determined by the frequency of alleles, for instance, you may lose power for detecting rare alleles. Another disadvantage of GWAS is that is sensible to the population structure that may lead to many false positives. There are ways to correct for population structure, however, if your population structure contribute to the variation in your trait correcting for population structure may lead you to many false negatives.

Like you mention there are several methods that try to bridge the advantages of QTL mapping and GWAS. For examples, MAGIC and NAM populations they have a broader genetic diversity, and higher resolutions than bi-parental populations and the frequencies of alleles are quite well represented to reduce the problems of rare alleles. The development of these types of populations is labor and cost intensive

I think the decision between using one or the other depends on several factors:

So the question is what is the goal of your study:

1-To identify the causal gene or mutation (GWAS better than QTL mapping) or just looking for a linked marker (Probably a QTL mapping would do the job).

2- Are you interested in the whole genetic architecture of the trait, that is how many genes and their effect, (GWAS better over QTL mapping) or you are interested in major QTLs for practical purposes (QTL mapping  or GWAS would do the job).

3- Previous knowledge of the genetic architecture and heritability of the trait can help you to make a decision as well.

3.1 IF your trait or traits of interest is  underlined by few genes of large effect then GWAS will be suitable

3.2 If your trait is governed by rare alleles, then QTL mapping would be better or you would need large populations to detect them with GWAS.

Here I would like to suggest a few literature on the subject:

Hi,

Maybe using C programing. See the link below. 

Good luck. 

ANOVA test is useful for evaluation of variance

Hi,

See the PDFs and link bellow.

Good Luck.

I guess separate analyses need be done for men and women as men have XY chromosomes and would differ from the normal (MFA) analysis.

I think software PLINK should help you do the needful.

There is plenty of literature on GWAS in different organisms. The field is exploding. For fruitful guidance in your case, a journal like American Journal of Human Genetics would be very useful to search problems and solutions similar to yours.

Dear Dhananjay

It is a basic calculation for gene frequency.

Please, read the reference textbook:

Falconer D. S. and Trudy F.C. Mackay.1996. Introduction to quantitative genetics.Burnt Mill, England.

I hope will be helpful for you.

Good Luck

Hi

See the attached files.

Good luck

Hi,

See the attached file.

Good luck

Hi

See the attached file

Good luck

ASReml-R is a good package for classical quantitative genetics using AIREML. MCMCGLMM is also a good pacakge. rrBLUP, BGLR are also good packages after you convert your pedigree to inverse matrix of A.

The MLM always outperform GLM in controlling false positives, which could be seen by QQplot. To this end, MLM is better than GLM. However, the more stringent constrols of MLM sometimes introduce false negatives, which is believed to be one reason of the missing heritability. Empirically, we also met some cases that no significant sigals were identified in one population for the trait with sufficiently high heritability. If you come back to GLM results, you may see too many significances across the whole genome, that embarrass you to select ones in high credit. In this complex situation, I think GLM and MLM are both incapable to handle it. There are always another ways besides GLM and MLM method, for example, FarmCPU, ADGWAS, etc. 

Do not hesitate to try different methods, you will find the right one suitable to your specific data.

get in touch with ILRI scientists, Dr Ojango

Thanks’ a lot Dr. Filippo, I really appreciate your answer and guides.

Sincrely,

Djouher

Please consider that a fasta file does not include general/proper/standard annotations.

A fasta file is only a name and sequence. Some people encode annotations in the name in non-standard way sometimes, but as such, programs cannot rely on them. Normally a fasta file can be accompanied by GFF file that annotates somehow a fasta file. But this is an external file. 

Genbank files for example includes both the sequences and the annotations, on the other hand. 

I am not sure about what you are looking for. Can you provide a link or an image of an example of what you want to do? for example if you have seen what you want in some published article that we can see?

Best regards,

---sram

More modern mixed models also work to do the analysis ("cross-means analysis","generation means analysis"....just a different name for the same thing):

Piepho HP, Möhring J (2010) Generation means analysis using mixed models. Crop Science 50, 1674-1680.

This publication gave me a good overview:

Cavalli LL (1952) An analysis of linkage in quantitative inheritance. In: Quantitative Inheritance: Papers read at a colloquium held at the Institute of Animal Genetics, Edinburgh University under the auspices of the Agricultural Research Council, April 4th to 6th, 1950 (eds. Rieve ECR, Waddington CH), pp. 135-144. HMSO, London.

@Nimala

Thank you for your reply. I understand there are copy number variation between genes. What if we assume both gene of interest are single-copied, can we directly compare qPCR calculated copy number between 2 genes?

Yes, what you want is an alpha lattice.

I recommend the lecture by Jennifer Kling (Oregon State University), linked below.

The 'agricolae' R package can assist you with the design and analysis of your alpha lattice.

You will, of course, still want to replicate the experiment.

29 x 5 may not be the most efficient or effective design, however. You may consider adding some checks to make a better design.150 entries would give you more options to choose from. Alternately, if you can drop a genotype, 144 would make a nice square lattice. As Jennifer says, "Use as large a block size as possible while maintaining homogeneity of plots within blocks."

Thank you for your answer. The experiment is randomized complete block design

These are great steps, Sophie Limou.

At step 5, I would add:

C. Tian (2008) paper 'Accounting for ancestry: population substructure and genome-wide association studies' shows that HLA and large genomic inversion regions can affect your population stratification analysis. Therefore, I suggest excluding SNPs in these regions prior to your PCA analysis, but bring back the SNPs in these regions when you run the actual association analysis.

Build 37 base-pairs coordinates for these regions are:

## BP ranges for HLA, 8p23.1 and 17q21.31 regions ##

##remove 8p23.1 [1680460:19329000]

##remove 17q21.31 [40920300:41421700]

##remove HLA [25707300:33850900]

Hope all these steps will bring the inflation factor below 1.1 :)

P.S. It would be interesting to know your rule when defining the PCA outliers, but I would go with 2, 3 or 4 standard deviations from the mean of PC1 and PC2.

آقای دکتر سلام

در این زمینه توصیه می کنم از آقایان دکتر پویا زمانی و دکتر احمد احمدی در گروه علوم دامی دانشگاه بوعلی سینا سوال بفرمایید زیرا متخصص ژنتیک دام هستند و در کارهای آماری هم تبحر کافی دارند.

There are many functions (and libraries) in R that can help you sort your data in the proper order. The main things are: grandparents before parents, parents before offspring, however, sometimes this is not easy. The key is that for a given individual that has a sire and dam defined in the pedigree file, needs to define these parents previously. I recommend you use GenoMatrix for this. This package has a model (Pedigree Sort) that will help you reading and sorting your pedigree file. Check it out at:

This software has also other routines to clean your molecular data, and to generate your kingship matrix (additive, dominance and even epistatic matrices)

yes, strictly speaking you can not use normal assumptions for this type of data, and you will need to go with Generalized Linear Mixed Models (GLMM) or another type of methodology (Bayesian?) in order to properly model the distribution of your data.

However, in practical terms, we do use LMM with normality assumptions for this type of variable in a regular basis. For example, sometimes we have a score from 1 to 5 for straightness of pine trees, where 0 is completely straight and 5 is very curved. This variable is discrete and ordinal, but if we check for some properties it can work quite well. First, you need to have equal spacing between your classes, this means that 1 to 2 is the same score change than 4 to 5, and this needs to be clearly defined in your measuring protocols. The other thing is that you ideally need to have several class values in your variable (ideally 10 or more), the reason is that this will give you a closer approximation to normality (central limit theorem) and therefore less issues. In any case, you will always have some 'doubts' about your heritability and BLUP estimates as you are 'approximating' your variable (e.g. true level of departure from straigthness)  with something different (e.g. a score).

Hi, you can try SMARTer Universal Low Input RNA Kit—cDNA Synthesis for NGS from Degraded Samples. http://www.clontech.com/US/Products/cDNA_Synthesis_and_Library_Construction/Next_Gen_Sequencing_Kits/Total_RNA-Seq/Universal_RNA_Seq_Random_Primed

Good luck,

Lesya

Assuming that you randomized each block, it sounds like you have a slightly unusual augmented design (unusual because the checks are generally replicated, rather than the new treatments). I'd recommend that you read about augmented designs before continuing with your analysis (links below).

You should be able to make pairwise comparisons, but not with a thing called "check". Since your checks are not the same variety in all blocks, you should not estimate a combined mean for them. The augmented design analysis will allow you to compare each treatment variety with each control variety.

I agree with Isain: he probably refers to human data, whilst you Folake seem to work with plants. I work with animals, and 3k-7k are low , 54 k medium and 800k high density panels.

I personally found this resourceful:

Best

Sayak

Your question should be framed differently. Each QTL is important, whether it has an overall negative or positive effect. You have to select the desirable allele of each QTL, whether it comes from parent A (poor) or B (superior). The poor parent may also carry a desirable allele for a QTL. It is not the QTL, but its desirable allele, which needs to be selected. Each QTL will have one desirable and the other undesirable allele. Sometimes the desirable allele of a QTL may be present in poor parent. I hope I have answered your question. 

The fitness is just how well an insertion mutant grows relative to the "wildtype" (parent). i.e if the growth rate was halved, the insertion mutant would have a fitness of 0.5. If it grew 10% faster, it would have a fitness of 1.1.

Since the list appear to be quite active, I'll add our details:

MS-Omics is a service provider of metabolomics and data analysis. 

I also suggest using MIGRATE, which gives more reliable estimations of gene flow than POPGENE and GENALEx, and also allows you to test different migration models.

Hope this helps

I think Juan's idea of building your own transcript assembly and then mapping to that is a good one. Trinity and Bridger are nice software for that. If you can get access to a large server/cluster you could probably run transcriptome assemblies in a matter of a few days. If not, I would suggest taking the full length genes (with introns) and map with a splicing-aware mapper such as Tophat2 or GSNAP. You could use Bowtie if you're not interested in the splicing. A program like HTseq lets you get raw read counts for your genes of interest, and this can be used to calculate FPKM or be put into differential expression analysis software (eg DESEq). Cufflinks can also be used for quick gene expression analysis.

Good luck

Thank you all for your suggestions.

Sorry for the late reply.

It sounds to me like you need to be searching for long-term studies on your species of interest, and that there might be more of what you're looking for in the gray literature than in the primary literature.  Have you tried direct queries to field stations and NGOs in your region of interest?  How about banding/ringing databases?  Folks at the North American Banding Council (http://www.nabanding.net/) might have insights, as might people you can access via the Ornithology Exchange (http://ornithologyexchange.org/).  You can find references for life history information of birds worldwide here: http://www.hbw.com/.  Lastly, this reference was just made public, but it's North American birds: http://www.vitalratesofnorthamericanlandbirds.org/.

Good luck!

~tim

Hi  Raghunath

My apologies, I missed the haplotype part of your question.  The calculation above was for standard case-control association. You will need at least 2 polymorphisms within an appropriate distance from one another e.g. within the same gene to perform haplotype analysis.  Haploview is quite a easy program to use. Alternatively, if you're familiar with scripting you can use haplo.stats in the R programming environment.

Thank you Dr. Nusret Zencirci,

Is there a way to combine augmented design study with alpha-lattice design for a population size of 100 entries? Some people consider each environment as a block  and analyze it as an RCBD for breeding purpose but I think block size is too big to address field variability. Is there a better way to do combined analysis under this circumstances

I have written a macro SAS program for Hayman approach and I have send it for TAG journal, I hope to be accepted. As I get acceptance I  will put free for other researchers.

Showing data that is normalized to a control AND showing the control (normalized to itself !!) with error bars is at best mixing different concepts of variation (what is not a sound idea) and at worst simply unneccesary and silly.

The information of the controls is used up in the calculation of the ddCt values of the other groups. It makes no sense to explicitely normalize the control group itself to itself (there is no information provided, so it is an absolutely pointless thing to do).

The information that is left over is:

"all other (ddCt-)values are refere to 0 as reference"

This means it makes perfect sense here (with ddCt) to draw the horizontal axis at y=0, and one immediately sees that positive values indicate an induction (relative to the controls) and negative values indicate a repression (relative to the controls). If data is normalized to a control, then there is no control group to be shown in the plot. Instead, the reference line should be drawn to indicate the (constant!) reference value. So there is actually no questions like "what is the error bar of the control group?" - it makes no sense.

This is different for dCt values. When dCt values are shown, then there is either NO reference line at all or the mean dCt of the control group can serve as the reference line. Either the horizontal axis as the reference line should then be placed at y=mean(dCt[controls]) or all the dCt values should be shifted by -mean(dCt[controls])) and the horizontal axis be plasec at y=0. This way, all the groups (including the controls) are represented on the plot, but the actual change in expression must be inferred implicitely by visually comparing the different dCt values - there is no information in the plot about the uncertainty with the estimates of these differences (i.e. ddCt values).

NB1: if dCt values are to be shown I'd prever to show all individual points rather than summary statistics like mean and SE. Only if there are really many groups or genes or a hell lot of individual values, so that such a plot would become "too busy" I would use a boxplot instead.

NB2: if ddCt values are shown (that are available only as means), then I'd prefer to show the confidence interval rather than the SE as a measure of uncertainty or precision of this estimate.

By 'negative covariance' you probably meant to say 'negative variance'? A commonly used approach to deal with this is to treat it as zero. You may refit the model without the corresponding random effect in the model.

Thank you sir,

Spoke to many scientists on campus they think it's not possible. It will be great if you can help me. If you have time I can call you or we can communicate by email. Please let me know your I'd. My Id: breddy1121@gmail.com 

Thank you in advance 

Regards,

Bharath 

when your data consist of many unrelated sires and their offspring ( paired data ) you can calculate heritability by regression offspring on sire ( heritability = 2b )

Dear Ping,

the differnce between observed and expected is not really high:

      obs exp

AA 38% 36%

Aa 43% 48%

aa 19% 16%

Sum 100% 100%

    

BB 57% 60%

Bb 41% 35%

bb 2% 5%

Sum 100% 100%

Could be only by chance given the small sample numbers????

Kind regards

Burkhard

 Lea-Coulson Model? (Bear in mind the assumptions however)

If you still need the data from such a run, 0.1 manual dRn threshold could be used to apprehend reasonable Cq values here.  If your FAM is being counter-balanced by TAMRA on the 3' end - you may have higher initial unquenched signal (TAMRA doesn't totally FRET-quench FAM).  MGB-NFQ on the 3' end is much quieter. BHQ is often better as well.  If this is not a calibration issue, it seems like you should not be afraid to use manual threshold settings (memorize them for each different target from same-tissue-same-isolation-method-same-master-mix protocols/work-flows). In your image, the machine is being fooled by the orange line intersecting the thresh at ~7 cycles.  The thresh line needs to be gently nudged out of its confusion by the user here ... to about 0.1 dRn.

Let p^ = population proportion of class of interest, here p^ = 0.52; Z_a/2 = population distribution for one sided test; and E = maximum error allow, say 0.03. The population proportion sample size is given by:

n = Z_a/2²p^(1 - p^) / e²

If Z_a/2(0.95) = 1.96; p^ = 0.52 and e = 0.03, then the sample size is:

n = (1.96)²(0.52)(0.48) / (0.03)²

n = 3.84(0.2496) / 0.0009

n = 0.9585 / 0.0009

n = 1065

And odds ratio is an estimated parameter like a mean or a slope on a regression line.  A p-value is not a parameter but is a probabilty of obtaining your results under the null hypothesis. If the p-value is very small then you can feel confident rejecting the null hypothesis. I the case of an odds ration a small p-value means that it is very unlikely that the odds ratio = 1 (no difference between the two odds). Mathematically equivalent is the confidence interval which is really more informative than the p-value as it gives you an upper and lower limit on the odds ration estimate. If the odds ratio CI covers 1 then the odds are the same.