Science topic

GWAS - Science topic

Explore the latest questions and answers in GWAS, and find GWAS experts.
Questions related to GWAS
  • asked a question related to GWAS
Question
3 answers
Dear community,
I am planning on conducting a GWAS analysis with two groups of patients differing in binary characteristics. As this cohort naturally is very rare, our sample size is limited to a total of approximately 1500 participants (low number for GWAS). Therefore, we were thinking on studying associations between pre-selected genes that might be phenotypically relevant to our outcome. As there exist no pre-data/arrays that studied similiar outcomes in a different patient cohort, we need to identify regions of interest bioinformatically.
1) Do you know any tools that might help me harvest genetic information for known pathways involved in relevant cell-functions and allow me to downscale my number of SNPs whilst still preserving the exploratory character of the study design? e.g. overall thrombocyte function, endothelial cell function, immune function etc.
2) Alternatively: are there bioinformatic ways (AI etc.) that circumvent the problem of multiple testing in GWAS studies and would allow me to robustly explore my dataset for associations even at lower sample sizes (n < 1500 participants)?
Thank you very much in advance!
Kind regards,
Michael Eigenschink
Relevant answer
Answer
for the second part of your problem, you can try vcf2gwas pipeline, that is very easy to run as a docker image
  • asked a question related to GWAS
Question
1 answer
Is a GWAS study of LVH genetic predisposition of 500 samples be considered low powered study? If so how to justify a small sample size ? Is there research conducted with small n size with GWAS ?
Relevant answer
Answer
Ideally Power of any study directly linked to sample size . More sample size, more will be the power of study. But sample size calculation , importantly depend on
number of objective parameters ( primary ) of your study ,more no of objective parameters , more will be sample size. Definitely if you choose rare cardiovascular parameter, your sample size will be less. For eg you want to study on Hypertension even 1000 sample will be not even for GWAS
  • asked a question related to GWAS
Question
3 answers
I was wondering if there are any bioinformatic tools to search for species-specific genes across species' genomes. For example, in the Venn diagram attached below (Kim et al., 2011), there are overlapping gene families between these species. How would you go about finding these species-specific genes without simply checking every gene? Ensembl seems to be a good start, but I'm having trouble narrowing down the right tools. A big goal is to find genes with only a few existing orthologs (i.e. maybe genes only appear in certain species).
Thanks!
Relevant answer
Answer
So you want single copy orthologs that also are only present in a subset of species? Maybe just run something like OrthoFinder and parse what you want from the output (i.e., selecting single copy alignments w/ members only from your subset)?
  • asked a question related to GWAS
Question
3 answers
Can someone please explain me the logic behind identifying genes present within 50KB, 100KB and 500KB (both side) of a SNP locus ? How does the SNP affect the function of the genes present within the above mentioned windows?
Relevant answer
Answer
Hi
you can see this one:
all the best
fred
  • asked a question related to GWAS
Question
2 answers
I am performing a GWAS Analysis, while comparing my pre-imputation and post-imputation data, I observed that the most significant genetic variant (p<1x 10-16) from pre-imputation data is no more significant post imputation. Imputation performed using reference genome 1000genome phase3 v5 SAS population data in Michigan Imputation server. These variants were missed out while matching the target data and the ref data. How do I overcome this? What is should be reported in the manuscript (pre or post imputation data)? How do we justify such findings ?
Relevant answer
Answer
I agree with isan,please,test SNPs window effect it may help you in explaining variance by SNPs more effectively.
I hope this help.
  • asked a question related to GWAS
Question
2 answers
We have a GWAS population (genotyped) and we want to select the minimum (an optimal) set of genotype to phenotype in order to detect GWAS signals. Any soft or method available?
Relevant answer
Answer
To detect a genotype-to-phenotype signal in a non-bi-parental mapping population you need to first determine which genetic markers are useful. Usually you would want to have genetic markers genotyped for a large portion of the population (<10 missing rate). Also, as you can't detect the actual recombination events in a multi parental population you would want to estimate linkage disequilibrium among the genetic markers.
I think asking how to actually set this filters shouldn't be the question, but why this is important. I would say the technical stuff is trivial if you understand the actual biology behind the problem. That said, VCFtools, and some AWK or dplyr script should be more than enough to filter the useful markers. Did you generate the genotypes using a sequencing-based method or an array-based one? Sometimes genotypes need to be corrected if the sequencing coverage is poor.
Good luck!
  • asked a question related to GWAS
Question
5 answers
I have phenotype data and SNP data (in .map and .ped file) of 3k rice germplasms. How can I conduct GWAS and haplotype analysis?
Relevant answer
Answer
I used TASSEL in my thesis.
Title: Genetic Analysis of Leaf and Sprout Traits of Cabbage and Brussels Sprout
Page Number: 41
Here you would get the step by step procedure to conduct GWAS.
  • asked a question related to GWAS
Question
4 answers
I have a list of significant SNPs and would like to perform a Motif Discovery and Transcription Factor analysis.
Relevant answer
Answer
Weblink for ReMap:https://remap.univ-amu.fr/
  • asked a question related to GWAS
Question
1 answer
screening for herbicide resistance and identify genomic regions associated with it in maize
Relevant answer
Answer
I am not expert in this field but I expect that even experts would need to know considerably more about your plans to be able to answer meaningfully.
  • asked a question related to GWAS
Question
3 answers
Whether F2 derived from RIL population is appropriate for GWAS ?
Relevant answer
Answer
If you have two or more RILs populations derived from different parentages then can be conducted GWA study.
  • asked a question related to GWAS
Question
3 answers
How would one associate a single phenotype factor (binary presence / absence of trait) to SNPs in Tassel (v5.2.58) GUI?
I generated VCF file using GATK and am able to import the VCF and phenotype data into Tassel. The phenotype data is just two columns the 'Taxa' and the 'Factor' (as Y and N; where Y = has the phenotype and N = does not).
The desired end result is a Manhattan plot with any SNPs associated with the trait, and ideally, an output file which contains the SNP locations.
Relevant answer
Answer
I recommend you to use GAPIT, which is much more easier and also its possible to do multiple model comaprision in same time.
  • asked a question related to GWAS
Question
22 answers
I couldn't see much paper where plant breeders use biochemical such as proline content Malondialdehyde (MDA) and dyes such as NBT or DAB( for ROS detection) for screening stress-tolerant accession on a large scale (100-200 which I suppose is possible to do). Are not these methods better than phenotyping grain yield, biomass, plant height, NDVI, LAI, etc?
Relevant answer
Answer
The biochemical analysis are important, but if you have plant breeding programme should be more interesting and effective to make evaluation on the field. In those conditions you can evaluate and select the best plants for abiotic stress plants, and maybe to others topics like vigorous plants, also for production and quality. Then, you can select the most promissed material for your proposed objectivesz.
  • asked a question related to GWAS
Question
3 answers
Heritability in broad sense
Relevant answer
Answer
Agree to the answers above. However, for a GWAS panel, low narrow-sense heritability (estimated by SNPs or pedigree) typically warns a high risk of failure in discovery SNP.
  • asked a question related to GWAS
Question
2 answers
Comparative analysis
Relevant answer
Answer
GAPIT could use multiple methods to validate common significant markers. It also included single locus and multiple loci methods.
  • asked a question related to GWAS
Question
4 answers
Dear researchers
Please help me !!!. How to detect the candidate gene according to the GWAS marker? Thanks
Relevant answer
Answer
Thanks very much
  • asked a question related to GWAS
Question
1 answer
Hello people,
I did the SNP genotyping of two different plant species through TASSEL5-GBS pipeline. In one of the species, I am getting significantly high number of missing genotype information (N) (Attached figure 1) but in other species I am not getting high number of missing genotype (Figure 2) information.
What could be the reason of getting high number of missing genotype information (N), how to filter them before performing downstream analyses like GWAS, LD and so on.
Any suggestion?
Relevant answer
Answer
  • asked a question related to GWAS
Question
6 answers
Hi,
we obtained our data from SNP genotyping from external lab. We found out, that there are letters "'D" and "I" in some positions. Do you know what those means? It is also in the reference fields.
example:
1       13901895        chr1_13901894   D       I
1       13903334        chr1_13903333   I       D
1       13903422        chr1_13903421   I       .
Thank you very much for your help.
Relevant answer
Answer
Have you asked that "external lab" that gave you these data?
  • asked a question related to GWAS
Question
10 answers
What is the advantages of estimating BLUPs for GWAS studies
Relevant answer
Answer
Dear Gregor Steve , great question!
There are different ways of using BLUPs for and from GWAS. However, there are also limitations!
Generally speaking, we want to use phenotypic records in all statistical analyses. But, sometimes this is not possible (for several reasons) and we may use BLUPs instead. Here are three common reasons for using BLUPs instead of phenotypes:
Example 1: Complex models
- Some software have limitations in performing some type of analyses, such as, but not limited to: including random effects other than the residual, repeated records (related to the previous one), multivariate analysis (aka multiple-trait analysis), etc.
- In this case, one could use BLUPs that are already adjusted for these effects when performing GWAS
Example 2: Limited phenotypes
- Given a genetic relationship matrix (e.g., A matrix, Genomic Relationship Matrix) that measures the genetic similarities (i.e., covariance) among individuals, the Mixed Model Equations (MME; Henderson, 1963) allows every single individual in the relationship matrix to have a BLUP, regardless of the individual having or not phenotypic records.
- Hence, when the genotypic data include individuals with and WITHOUT phenotypic records, a larger dataset used for analysis could be obtained by using BLUPs instead of phenotypic records.
Example 3: Individuals with large progeny records
- In general terms, when an individual in the relationship matrix has lots (i.e., hundreds to thousands) of progeny records, the BLUP of this individual should be highly accurate.
- Hence, in a large number of genotyped individuals have a large number of (non-genotyped/limited-genotyped) progeny with phenotypic records, the use of BLUPs in place of its phenotypic records could provide with better/more accurate GWAS results.
However, as I mentioned, there are also limitations about this approach:
Example 4: BLUPs have different accuracies
- When talking about real data, we see a large variation on the number and degree of relationships among individuals, the number of phenotypic records, and more.
- Therefore, some BLUPs should be more accurate than others. HENCE, the statistical analysis using BLUPs should be properly weighted by the level of uncertainty of these BLUPs.
- Such weighing procedure could be complex or impossible to be implemented (depending on the software and dataset)
Example 5: BLUPs are only part of story
- BLUPs are estimates of the additive values of the individuals. Thus, if your goal in your GWAS is to identify non-additive effects, such as dominance and epistasis, it is not expected to identify associations for SNPs with non-additive estimates.
- Therefore, BLUPs, unless specifically calculated to include those effects*, should not provide you with these associations.
*By the way, if non-additive effects are included, we shouldn't call them BLUPs anymore.
There are additional thoughts about this, but I think this could give you some clarity.
Please let me know if you have any other questions. Thanks, Nick
  • asked a question related to GWAS
Question
4 answers
I am doing my research on Cancer biology.
I need your suggestions in order to perform GWAS of autoghapy related gene in homo sapiens.
Kindly please suggest me some information about tools for the same.
Thank you all in advance.
Relevant answer
Answer
Maybe the following website will help you! http://www.autophagy.lu/
  • asked a question related to GWAS
Question
4 answers
We have SSR marker (150) based genotypic data of 190 rice landraces. Want to prepare a research article. Which kind of analysis may be performed with these data? Kindly give your opinion and suggestions.
Regards
Parmeshwar
Relevant answer
Answer
hello,
Primarily we should get to know that for what purpose we are carrying out molecular studies and based on that analysis can be done. like,
  1. for genotypic characterization:- Basic genetic parameter analysis like hetertozygotes level, allelic frerquency, PIC value etc.,
  2. for genetic variability studies:- AMoVA
  3. for ancestry studies: - phylogenetic analysis OR dendrogram studies.
  4. for genetic distance studies:- PCA, Genetic distance matrices.
  5. for population studies: - STRUCTURE.
Stat tools:- ArleQin, GenAlEx, Molkiv, Power marker, NTSYS, STRUCTURE, MEGA and Darwin.
*If you are using genic SSRs-
  1. trait identification studies
  2. QTL analysis.
Stat Tools: - Windows QTL Cartographer, TASEL.
all the best
  • asked a question related to GWAS
Question
4 answers
Why is the term covariate used for the independent variable in GWAS? It seems there is a sense of ambiguity about the term "covariate" in GWAS.
In " Statistics" , in analysis of covariance (ANCOVA), the auxiliary variable is called covariate. So "Covariate " has a separate meaning from the independent variable. But in "Genomic analysis", all variables are called covariates. Whether it is really an auxiliary variable or an independent variable.
Relevant answer
Answer
It's because regression underlies all of this as R A Fisher points out in.his first paper on ancova. For an easy description see the attached.. Best wishes David Booth
  • asked a question related to GWAS
Question
5 answers
Hi,
I want to do a meta analysis from GWAS and publications. For the GWAS studies, I only have the beta value, p value and total number of samples while for the publications I have sufficient data that could be used to calculate effect size.
Is there a way to compute effect size from only the beta, pval and N?
If not, is there any way to calculate beta using the mean, SEM and N of cases and controls from publications?
Thanks in advance!
Relevant answer
Answer
Thank you all very much for your valuable insights!
  • asked a question related to GWAS
Question
4 answers
Hi, I am trying to understand quality control in GWAS for individuals and for SNPs. I don't understand what is missing rate (--missing) for individuals and MAF (--freq) for SNPs. Can anyone explain it in plain language?
Relevant answer
Answer
While agreeing with all the above scientists, my hat off 🎩 , the simplest way to know if the association is worth working on is to look for two values:
1.) P-Value, and
2.) OR or Beta values
The rest is just your judgment :) + some statistical formulas.
  • asked a question related to GWAS
Question
12 answers
What are the basic steps in GWAS?
Relevant answer
Answer
you may follow the research article
This is a very nice piece of work and thoroughly written research article.
Hope this may help.
I am also looking for GWAS and its details but in APPLE.
  • asked a question related to GWAS
Question
3 answers
I have a big list of significant SNPs (>30K) from a GWAS/meta-analysis. Can you please suggest what are some best ways to find the respective gene names and further classify them as already reported and novel ones?
Thanks in advance
Relevant answer
Answer
Thanks, Fredric and Diana ~ let me check both the options!
  • asked a question related to GWAS
Question
5 answers
Phylogenetic, diversity, Synteny mapping, PCA, etc
Relevant answer
Answer
Dear Ajay,
Please read this article and it will help you more.
  • asked a question related to GWAS
Question
5 answers
I am using metal to perform metaanalysis of GWAS.
Is there anyway to keep chromosome number and position in the output file ?
Thanks.
Relevant answer
Answer
I suggest you use Biomercator v3 for meta analysis of QTL.
  • asked a question related to GWAS
Question
5 answers
HI, nowdays i use illumina beadchip for GWAS analysis.
I have 2 type chip. It' s bovinesnp50_v1, bovinesnp50_v2.
But it has different chromosome, maker position in the SNP_map file.
So, How can i use different versions.
Can i imputation from bovinesnp50_v1 to bovinesnp50_v2.
If I can imputation it. After imputation, how can i use different version of maker position?
i wanna know answers. Have a nice day : )
Relevant answer
Answer
We have been faced the same issues. One suggestion we have used is to use common SNP (by names) from the two versions for simplicity. This approach, however, will lead to loss of some of unmatched SNPs. Thus, the another approach mentioned from other peers may be considered.
In our recent genomic prediction article, we lost less than 10% of the combined SNPs from BovineSNP50 BeadChip version 2 and 3, but loss of benefits of genomic prediction was small.
In my opinion, selecting which approaches should depend on factors such as ultimate purpose, cost-benefit, and convenience.
  • asked a question related to GWAS
Question
6 answers
Including these steps: 1) raw data format transformation for five companies 2) update positions for all SNPs to hg37 version 3) Quality control within companies 4) Pre-phasing (SHAPEIT2) and imputation (IMPUTE2) for all SNPs of each company 5) Perform GWAS using two logistic models for 27 phenotypes 6) Statistic and downstream bioinformatic analysis. 7) Estimation of genetic parameters (rg and hg). 8) PRS analysis. However. the size of my dataset only consist more than 1000 people. With no background knowledge, how long would this take as a bioinformatics master student?
Relevant answer
Answer
more than 1000? please tell the exact number of samples and size of the data?
  • asked a question related to GWAS
Question
4 answers
There is very little publication where functional characterization(cloning, overexpression, silencing, etc.) of genes identified through GWAS has been performed. However, most of the publications on functional characterization are on genes identified through transcriptome. Why is this? I doubt whether there is any usefulness of GWAS on crop improvement or not? if yes then give me some successful publication examples?
Relevant answer
Answer
The reason is that there are very few publications where functional characterization (cloning, overexpression, silencing, etc.) of the genes identified through GWAS has been performed. However, most of the publications on functional characterization revolve around genes identified through transcription because these are quantitative traits and are controlled by many genes and the influence of the environment is very high and the effect of each gene is weak (Minor genes) and they have small-effect genes rather than Major genes that have large -effects
  • asked a question related to GWAS
Question
3 answers
Hello!
I recently received many *.CEL files from a recent UK biobank genotyping. According to the SNPolisher guide, I have to conduct certain metrics on these SNPs to keep just the ones fulfilling essential criteria. It is not clear (at least for me, first time using it) how these CEL files are using in the SNPolisher inputs. The first input is: Ps_Metrics(posteriorFile, callFile, output.metricsFile, pidFile).
Is there a previous step where these cell files are converted into these posterior or cal files? Both should be in *.txt format but all I have are *.CEL files.
Hope for some guidance from any expert!
Kind regards,
B
Relevant answer
Answer
Thanks a lot, Anna! Are the Axiom Analysis Suite or Axiom Power Tools equivalents? I'm planning to use the command line prompt for my work and I found Axiom Analysis Suite is a Windows-based tool. Which are you commonly using?
  • asked a question related to GWAS
Question
3 answers
Hello fellow researchers,
I am currently dealing with very large data sets of SNPs (more than 2 million) to investigate whether GWAS significant SNPs are more frequently located within certain genomic regions than non-significant SNPs. I have a 2x2 table stating the absolute number of SNPs in the significant vs. non-significant group that are either located within this specific region or not. Now, I obviously need to check my results for statistical significance, which initially I have done with the Chi-square test. But because I have so high numbers, every investigation is (putatively) statistically significant. I know that some publications just state the Cramer's V as an additional indicator, but I would rather have something alternative to use (if it exists). So do any of you know good alternative tests or methods to deal with these high numbers without this large sample size bias? How do you normally deal with these huge samples sizes?
I would be grateful for any tip or advice.
Thank you!
Relevant answer
Answer
Luisa -
You noted that "But because I have so high numbers, every investigation is (putatively) statistically significant."  That is the problem with the one-size-fits-all p-value "significance" levels (say 0.05 or 0.01) that were originally considered for industrial experiments as an indicator (by Fisher) of whether or not an investigation should proceed.  It was flawed from the beginning because any measure that changes with sample size (not just estimated better with a larger sample size, but changes) has to be interpretable in some context.  You need an idea here of "effect size."  I do not know your subject matter, but often a p-value is just a step too far for a very practical usage.  It is often considered good for decision making, but I argue that.  An automatic yes/no decision made for you may seem attractive, but you really need as much information as possible, often enhanced greatly by graphics, to make a more informed decision.  A standard error, although it also changes with sample size, is more easily interpretable, especially if you can generate confidence intervals.  And graphics can convey a variety of information and stimulate further analysis. 
The real question is How much of a difference makes a real, practical difference to your application?
I have worked with very small samples, and urged people not to just look at an isolated p-value, where they tend to be large, just because of small sample sizes.  I've seen industry try to claim a standard was met, just because there was too little information to show that it wasn't.  I know that large data sets have the opposite problem.  A lone p-value is rather meaningless, ​in either case.  
Also, in favor of 'measurement' rather than hypothesis testing at all, we often do not really want to know "Is something present, yes or no?" But rather "How much is present?" which could include little or none.  The example I have in mind for that is the hypothesis tests for heteroscedasticity in regression.  Why even do one?  You really want to know the impact, especially on variance.  If you do an hypothesis test, then what?  If you decide "yes" it is present, then you need to do something about it.  If "no," then it can still impact results and you won't know it.  But if you just estimate the coefficient of heteroscedasticity and use that in the model to model the heteroscedasticity, then you don't have to guess how much difference it would have made (and different size predicted values should be associated with different size sigma for residuals, which impact prediction intervals). 
OK, that was a long aside, but whatever your application, I think making good decisions is actually aided by studying sigmas not p-values.   A sigma can be compared to the parameter.  Some hypotheses, such as multiple sample comparisons may not be so easily restructured, but you still just do not get good information from a single p-value in isolation.  Perhaps you could research "size effect."
Cheers - Jim
  • asked a question related to GWAS
Question
10 answers
We have some good projects on bioinformatics. Interested researches having expertise in virtual screening/in silico drug discovery, GWAS, RNA seq, human genome analysis are requested to contact. We can jointly publish the articles.
Relevant answer
Answer
Please have look on our(Eminent Biosciences (EMBS)) collaborations.. and let me know if interested to associate with us
Our recent publications In collaborations with industries and academia in India and world wide.
EMBS publication In association with Universidad Tecnológica Metropolitana, Santiago, Chile. Publication Link: https://pubmed.ncbi.nlm.nih.gov/33397265/
EMBS publication In association with Moscow State University , Russia. Publication Link: https://pubmed.ncbi.nlm.nih.gov/32967475/
EMBS publication In association with Icahn Institute of Genomics and Multiscale Biology,, Mount Sinai Health System, Manhattan, NY, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918
EMBS publication In association with University of Missouri, St. Louis, MO, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30457050
EMBS publication In association with Virginia Commonwealth University, Richmond, Virginia, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211
EMBS publication In association with ICMR- NIN(National Institute of Nutrition), Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611
EMBS publication In association with University of Minnesota Duluth, Duluth MN 55811 USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211
EMBS publication In association with University of Yaounde I, PO Box 812, Yaoundé, Cameroon. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335
EMBS publication In association with Federal University of Paraíba, João Pessoa, PB, Brazil. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30693065
Eminent Biosciences(EMBS) and University of Yaoundé I, Yaoundé, Cameroon. Publication Link: https://pubmed.ncbi.nlm.nih.gov/31210847/
Eminent Biosciences(EMBS) and University of the Basque Country UPV/EHU, 48080, Leioa, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852204
Eminent Biosciences(EMBS) and King Saud University, Riyadh, Saudi Arabia. Publication Link: http://www.eurekaselect.com/135585
Eminent Biosciences(EMBS) and NIPER , Hyderabad, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759
Eminent Biosciences(EMBS) and Alagappa University, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335
Eminent Biosciences(EMBS) and Jawaharlal Nehru Technological University, Hyderabad , India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910
Eminent Biosciences(EMBS) and C.S.I.R – CRISAT, Karaikudi, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237676
Eminent Biosciences(EMBS) and Karpagam academy of higher education, Eachinary, Coimbatore , Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672
Eminent Biosciences(EMBS) and Ballets Olaeta Kalea, 4, 48014 Bilbao, Bizkaia, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918
Eminent Biosciences(EMBS) and Hospital for Genetic Diseases, Osmania University, Hyderabad - 500 016, Telangana, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910
Eminent Biosciences(EMBS) and School of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, Panangad-682 506, Cochin, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27964704
Eminent Biosciences(EMBS) and CODEWEL Nireekshana-ACET, Hyderabad, Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26770024
Eminent Biosciences(EMBS) and Bharathiyar University, Coimbatore-641046, Tamilnadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27919211
Eminent Biosciences(EMBS) and LPU University, Phagwara, Punjab, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/31030499
Eminent Biosciences(EMBS) and Department of Bioinformatics, Kerala University, Kerala. Publication Link: http://www.eurekaselect.com/135585
Eminent Biosciences(EMBS) and Gandhi Medical College and Osmania Medical College, Hyderabad 500 038, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27450915
Eminent Biosciences(EMBS) and National College (Affiliated to Bharathidasan University), Tiruchirapalli, 620 001 Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27266485
Eminent Biosciences(EMBS) and University of Calicut - 673635, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611
Eminent Biosciences(EMBS) and NIPER, Hyderabad, India. ) Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759
Eminent Biosciences(EMBS) and King George's Medical University, (Erstwhile C.S.M. Medical University), Lucknow-226 003, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579575
Eminent Biosciences(EMBS) and School of Chemical & Biotechnology, SASTRA University, Thanjavur, India Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579569
Eminent Biosciences(EMBS) and Safi center for scientific research, Malappuram, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672
Eminent Biosciences(EMBS) and Dept of Genetics, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25248957
EMBS publication In association with Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26229292
Sincerely,
Dr. Anuraj Nayarisseri
Principal Scientist & Director,
Eminent Biosciences.
Mob :+91 97522 95342
  • asked a question related to GWAS
Question
6 answers
I did Marker trait Association in Cotton
Relevant answer
Answer
you can also do it using Tassel
  • asked a question related to GWAS
Question
4 answers
We have some good projects on bioinformatics. Interested researches having expertise in virtual screening/in silico drug discovery, GWAS, RNA seq, human genome analysis are requested to contact. We can jointly publish the articles.
Relevant answer
Answer
Let me know if you are interested in collaborating with us.
Our recent publications In collaborations with industries and academia in India and world wide. Eminent Biosciences(EMBS) and Universidad Tecnológica Metropolitana, Santiago, Chile. Publication Link: https://pubmed.ncbi.nlm.nih.gov/33397265/ Eminent Biosciences(EMBS) and Moscow State University , Russia. Publication Link: https://pubmed.ncbi.nlm.nih.gov/32967475/ Eminent Biosciences(EMBS) and  Icahn Institute of Genomics and Multiscale Biology,, Mount Sinai Health System, Manhattan, NY, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918 Eminent Biosciences(EMBS) and  University of Missouri, St. Louis, MO, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30457050 Eminent Biosciences(EMBS) and  Virginia Commonwealth University, Richmond, Virginia, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211 Eminent Biosciences(EMBS) and  ICMR- NIN(National Institute of Nutrition), Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611 Eminent Biosciences(EMBS) and  University of Minnesota Duluth, Duluth MN 55811 USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211 Eminent Biosciences(EMBS) and  University of Yaounde I, PO Box 812, Yaoundé, Cameroon. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335 Eminent Biosciences(EMBS) and  Federal University of Paraíba, João Pessoa, PB, Brazil. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30693065 Eminent Biosciences(EMBS) and  University of Yaoundé I, Yaoundé, Cameroon. Publication Link: https://pubmed.ncbi.nlm.nih.gov/31210847/ Eminent Biosciences(EMBS) and  University of the Basque Country  UPV/EHU, 48080, Leioa, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852204 Eminent Biosciences(EMBS) and  King Saud University, Riyadh, Saudi Arabia. Publication Link: http://www.eurekaselect.com/135585 Eminent Biosciences(EMBS) and  NIPER , Hyderabad, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759 Eminent Biosciences(EMBS) and  Alagappa University, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335 Eminent Biosciences(EMBS) and  Jawaharlal Nehru Technological University,  Hyderabad , India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910 Eminent Biosciences(EMBS) and  C.S.I.R – CRISAT, Karaikudi, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237676 Eminent Biosciences(EMBS) and  Karpagam academy of higher education, Eachinary, Coimbatore , Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672 Eminent Biosciences(EMBS) and  Ballets Olaeta Kalea, 4, 48014 Bilbao, Bizkaia, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918 Eminent Biosciences(EMBS) and  Hospital for Genetic Diseases, Osmania University, Hyderabad - 500 016, Telangana, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910 Eminent Biosciences(EMBS) and  School of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, Panangad-682 506, Cochin, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27964704 Eminent Biosciences(EMBS) and  CODEWEL Nireekshana-ACET, Hyderabad, Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26770024 Eminent Biosciences(EMBS) and  Bharathiyar University, Coimbatore-641046, Tamilnadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27919211 Eminent Biosciences(EMBS) and  LPU University, Phagwara, Punjab, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/31030499 Eminent Biosciences(EMBS) and  Department of Bioinformatics, Kerala University, Kerala. Publication Link: http://www.eurekaselect.com/135585 Eminent Biosciences(EMBS) and  Gandhi Medical College and Osmania Medical College, Hyderabad 500 038, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27450915 Eminent Biosciences(EMBS) and  National College (Affiliated to Bharathidasan University), Tiruchirapalli, 620 001 Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27266485 Eminent Biosciences(EMBS) and  University of Calicut - 673635, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611 Eminent Biosciences(EMBS) and  NIPER, Hyderabad, India. ) Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759 Eminent Biosciences(EMBS) and  King George's Medical University, (Erstwhile C.S.M. Medical University), Lucknow-226 003, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579575 Eminent Biosciences(EMBS) and  School of Chemical & Biotechnology, SASTRA University, Thanjavur, India Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579569 Eminent Biosciences(EMBS) and  Safi center for scientific research, Malappuram, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672 Eminent Biosciences(EMBS) and  Dept of Genetics, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25248957 Eminent Biosciences(EMBS) and  Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26229292
  • asked a question related to GWAS
Question
4 answers
Hi, I am working on GWAS for identification of markers and candidate genes associated with leaf rust and powdery mildew resistance. I was reading an old paper and in that paper they identified one QTL associated with leaf rust resistance on long arm of chromosome 2H. They mentioned one SSR marker close to that QTL. Now I was interested to compare that QTL position with my own results because I also identified one QTL on long arm of chromosome 2H.
Is there any way to identify the physical position of that QTL through SSR marker information ??
I will appreciate your response.
Thanks,
Relevant answer
Answer
You may compare both the marker sequence through NCBI Blast.
  • asked a question related to GWAS
Question
6 answers
Can anyone please tell me the database names or websites from where I can download human SNP datasets along with the quantitative traits (phenotypes) for genome-wide association studies (GWAS)?
Relevant answer
Answer
Thank you, Eduardo, Fadoua, Shin, Shahiid, and Alamin for your answers.
  • asked a question related to GWAS
Question
2 answers
I conducted a Mendelian randomisation study for assessing the association between X (exposure) on Y (outcome or case). But, I am not sure whether our outcome (cases) are valid. My question is, how can I check the validity of my outcome using genetic data? For example, is there any reasonable method for checking genetic correlation between our outcome and previously published GWAS including gold standard case ascertainment?
Relevant answer
Answer
Machine learning possibly helps in these situations.
  • asked a question related to GWAS
Question
3 answers
I am doing on the salt tolerance gene identification based on GWAS
Relevant answer
Answer
I recommend you to read our paper:
Arab MM, Marrano A, Abdollahi-Arpanahi R, Leslie CA, Askari H, Neale DB, Vahdati K.(2019) Genome-wide patterns of population structure and association mapping of nut-related traits in Persian walnut populations from Iran using the Axiom J. regia 700K SNP array. Scientific Reports 9(1): 6376.
  • asked a question related to GWAS
Question
5 answers
The relationship between gene expression and disease was widely known, however, is the gene ratio (Gene A expression value/Gene B expression value) might cause any diseases or phenotypes in any organism?
Relevant answer
Answer
You are going to have to give a LOT more details if you want helpful responses. What organism (human, mouse, strawberries, etc.)? What disease? Are you talking about gene expression or gene duplication or mtDNA depletion or another phenomenon?
  • asked a question related to GWAS
Question
5 answers
I want to perform GWAS analysis in a crop in which markers are not assigned to chromosomes. We have developed SSRs from ddRad seq information, these markers are not assigned to specific chromosomes yet. Please share information to address this issue.
Relevant answer
Answer
Thanks Kuldeep, I will take care of model issue.
  • asked a question related to GWAS
Question
5 answers
Hello everyone,
I am currently working on bioinformatics. I have received raw data of NGS showing SNPs, CHR, position, etc... For the first step of GWAS, I would like to create Manhattan Plots by using QQman packing in R. However, I do not know how to calculate the p-value of each SNPs on my data.
Anyone, please help me?
Thanks so much!
Relevant answer
Answer
By online p value evaluation of SNPs
  • asked a question related to GWAS
Question
4 answers
I am starting to work in genome wide association studies (GWAS) in Arabidopsis and I would like to know what is the difference between GWAPP Web application and performing the analysis on R. Can I get the same results with either of these tools?
Relevant answer
Answer
Hi Dear Brenda;
As far as I know, R was generated as a statistical language, and GWAS-related packages allow you to easily manipulate your data and make the more efficient implementation of various statistical analyzes, and R is widely used in practice.
GWAPP web application was designed specifically for Arabidopsis, although it has been highly regarded by plant scientists, it has its limitations. And of course, since GWAPP uses Python in part of its operations, it seems to have a higher execution speed.
In the end, It seems logical that if the experiment designs and the assumptions are the same, the same answers should be received.
  • asked a question related to GWAS
Question
5 answers
Hi, Greetings!
1. If we don't have a population size of more than 100 or let suppose (between 50-70). Can we go for QTL mapping?
2. Can we use different generation (F3, F4, F5 ) obtained from the cross combination of different parents (A x B, A x C, A x D ) for mapping a QTL or GWAS?
Relevant answer
Answer
Hi Dharmendra, the idea of creating a large sized NAM population is to increase the number of crossovers and reduce the interval where your gene is. Having 120 doesn't seem enough for fine mapping, also depends on the size of the genome, but you can still get indications on which chromosome your qtl is located. I would just give it a try.
  • asked a question related to GWAS
Question
1 answer
Hello All,
Greetings,
I have 2 cohort datasets with 500 sample size each and 200 SNPS of genotype files as (example1.gen and example2.gen) as input files and trying to generate other formats from the .gen files such as (example1.bgen and example2.bgen), sample ID and phenotype for the cohort data set (example1.sample and example2.sample) and VCF format (example1.vcf & example2.vcf). I am currently using SNPTEST (latest version) for GWAS analysis . Is there any possible ways to look into it ???
Thanks in advance
Relevant answer
Answer
  • asked a question related to GWAS
Question
1 answer
Type Hap_type Indica Japonica
C--C--C---C---GGGGAAAA Hap1 10 128
C--C--C---C---GGGGCCAA Hap2 224 53
CCACCACCAGCCAGGGGGCCGG Hap3 0 73
can you notice the deletions and insertions?what is the implications of these changes on the three haplotypes?what can be the correct description or conclusion you can make from here regarding the haplotypes of this genes and the subspecies?
its urgent,thanks in advance for your responses
Relevant answer
Answer
Sorry Sir, this is way beyond my experitise. I only are familiar with statistical analyses like ANOVA or chi-square.
  • asked a question related to GWAS
Question
7 answers
I want to calculate independent number of snps to set a threshold for GWAS. As the basic bonferroni correction is too conservative. Due to correlation between snps, we cannot assume they are independent. FDR adjustment doesn’t seems suitable too. My genotype data has 1.2 million snps. I applied basic bonferroni and FDR correction, but the results are not satisfactory. As I read in many papers, setting threshold as follows.
1- calculating the independent number of snps.
2- Apply a basic bonferroni correction.
Let’s suppose if independent number of markers are 11500, then at 0.05 error rate, the threshold would be 11500/0.05 =4.3E-6
A software called genetic type 1 error calculator can calculate it. The problem is, the website of GEC is unstable. I tried multiple times to download it , but couldn’t get there. Highly appreciated , if anyone having this software, can share it to me.
Another question is, can we get independent snps by LD pruning. I tried it in plink. But i am not sure, whether we can get independent snps by LD pruning or not.
Relevant answer
Answer
Hi,
You can use the simpleM approach which was developed by Gao et al. (2008, 2009). I recently used this method in my paper ( ).
Please see this link: "http://simplem.sourceforge.net/". I also attached the R codes and demo data.
In addition, I strongly recommend seeing Dr. Kaler paper (Avjinder Kaler ):
I also agree with Shaun Clare answers. You can use the R GAPIT package. Some recently developed GWAS algorithms (BLINK, FarmCPU, SUPER, etc) are included in this package. Some of these approaches are multilocus and you can use threshold 3 or 3.5. You can cite some of Dr. Kaler's recent papers.
Regards.
  • asked a question related to GWAS
Question
3 answers
This is just a hypothetical question. For example, you genotyped 500 BC3F2 rice plants, phenotypes them and conduct GWAS. Then, two generations later, you genotyped the BC3F4 plants and you got interesting phenotypic results. Would it be possible to do GWAS using the phenotypic data from BC3F4 plants but using the genotyping data from BC3F2 plants?
Relevant answer
Answer
See this paper: Mapping Quantitative Trait Loci in F2 Incorporating Phenotypes of F3 Progeny, Yuan-Ming Zhang and Shizhong Xu, Genetics 166: 1981–1993 (April 2004)
  • asked a question related to GWAS
Question
1 answer
Hello everyone
I have conducted GWAS and also estimated genetic variance and h2 explained by all SNPs, but I require to have these estimations for each SNP individually.
Could someone advise me or introduce a special software or way that calculates exactly these values? I appreciate it if anyone helps me.
Best Regards
Relevant answer
Answer
Hi Peymaneh:
I believe after you have the proportion of genetic variance explained by each marker, the heritability for a set is easy to calculate.
Perhaps this material (http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=tutorial_blupf90.pdf; chapter 8.5) can help you.
  • asked a question related to GWAS
Question
6 answers
I am trying to perform a genome-wide association on mitogenome variants (obtained from GATK-Mutect2) vs. phenotypes by using GAPIT. The number of individuals is seven and GAPIT function does not work due to the low number of indvs. We have increased the number of the same genotype (vcfs) and phenotype files by basically copying-pasting them under different names and run the function. Would this method affect the results in any way? Is there any other way to increase the number of individuals for GWAS studies?
Relevant answer
Answer
You copy-paste the same records with different individual names. Therefore, the software considers the most frequent/exact values in different individuals. It adversely influence of the validity of your results.
  • asked a question related to GWAS
Question
2 answers
I have conducted GWAS analysis through TASSEL software using SNPs generated from genotyping by sequencing data(GBS). But I do not have the reference genome. How do I calculate LD decay values and locate QTLs on LD map? Kindly let me know.
Relevant answer
Answer
Hey Divya,
Have you got an answer to your question because I have the same question.
Thanks,
Gurkamal
  • asked a question related to GWAS
Question
6 answers
I'm looking for a software to perform GWAS of binary traits, using imputed data, and that can take into account the relatedness between the individuals.
Relevant answer
Answer
Hi Danelia,
I know it is a bit late Danelia but did you test GMMAT package? I just have a question about the power of this package because the method use an approximation based on Null Model for all SNPs. (This estimation is used for example in FaST-LMM for continuous trait, but leads to miss some significant SNPs)
I do not know exact procedure to select SNPs using binary trait. I believe it is too expensive in term of computational time. I know about milorGWAS which is the same as GMMAT.
Have you finally find a way to perform exact GWAS ?
Regards,
Fabien
  • asked a question related to GWAS
Question
2 answers
I have recently started studying genomic data privacy and it seems the field is relatively new. I am looking for the existing problems. Implementing Homomorphic encryption or differential privacy has a lot going on. Can anyone suggest any other existing challenges?
Relevant answer
Answer
Given that the main routes to breach privacy are
- identity tracing,
- attribute disclosure attacks using DNA (ADAD)
- and completion of sensitive DNA information*,
the main questions would emerge from each of these distinct yet overlapping issues.
Genomic privacy though, I would argue, isn't necessarily about the academic exercise (fascinating as one might find it), but rather a more and more pressing issue, given the development of AI, as well as the various regulatory frameworks concerning data and individual privacy...
I do hope this may have been of some use, though I'm sure a lot has happened in the field since the question was posted. Best of luck with your research!
  • asked a question related to GWAS
Question
9 answers
Hi Everyone,
I have a list of 2000 SNPs (chicken)and require to find close genes to all these 2000 SNPs.
I used to do that one by one in a small set of SNPs by using org.Gg.eg.DB, BiocManager, Entrezquery, and chicken database in R, but that way is most time-consuming.
It is appreciative, if anyone suggests to me a reasonable and practical way to do so.
Frankly, I have a hitch here and I need your constructive comments or suggestions.
Thanks in advance
Relevant answer
Answer
Hi,
As my friend Mr. Ghoreishifar and also Dr. Duy said, you can use R biomaRt package for this. You also can use the plink "--gene-report" command.
However, Here is a chunk of R code that helps you to find adjacent genes for your SNPs.
### Preparing Gallus Gallus Genes
library(biomaRt)
Gallus_Dataset = useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = "ggallus_gene_ensembl",host = "www.ensembl.org")
genes = getBM(attributes = c("chromosome_name","external_gene_name", "ensembl_gene_id", "start_position", "end_position") ,mart = Gallus_Dataset)
genes$chromosome_name <- as.numeric(genes$chromosome_name) # please Ignore warning message
genes <- genes[!is.na(genes$chromosome_name),]
genes <- genes[order(genes$chromosome_name, genes$start_position),]
#################################
# your SNP list - you just need SNP, Chr, and position columns, and please use these colnames: SNP, Chr, position.
SNP_list <- read.table("your_SNPlist", header = T)
################################# loop
plusi=0; plusf=0 # if you need add an interval (e.g. 10000 for 10kb)
snp_ID = character()
chr_num = numeric()
SNP_Location = numeric()
Ens_Gene_Name = character ()
Gene_name <- character()
m = 1
for(k in 1:max(unique(genes$chromosome_name)))
{ cat("chromosome_Number:",k,"\n")
SNP = subset(SNP_list, SNP_list$Chr == k)
gene = subset(genes, genes$chromosome_name == k)
for(i in 1:length(SNP$Chr))
{
for(j in 1:length(gene$chromosome_name))
{
if((gene$start_position[j] - plusi) <= SNP$position[i] & SNP$position[i] <= (gene$end_position[j] + plusf))
{
snp_ID[m] = as.character(SNP$SNP[i])
chr_num[m] = SNP$Chromosome[i]
SNP_Location[m] = SNP$Location[i]
Ens_Gene_Name[m] = gene$ensembl_gene_id[j]
Gene_name[m] = gene$external_gene_name[j]
m = m + 1
}
}
}
}
My_Genes = data.frame(snp_ID,chr_num,SNP_Location, Ens_Gene_Name, Gene_name)
write.csv(My_Genes, file = "Genes.csv")
I didn't run the loop, please let me know if you have any problem.
Regards.
  • asked a question related to GWAS
Question
3 answers
Hi all,
I have compared common SNPs among two different populations. I need to do an imputation after QC. I would like to know whether I am having an adequate number of SNPs in hand for imputation. I am having around 1lakh common SNPs. Thanks in advance.
Relevant answer
Answer
Thank you for the article link
  • asked a question related to GWAS
Question
2 answers
Hello,
so I have SNPs (RSIDs) from imputation done in 2011 on http://csg.sph.umich.edu/abecasis/MACH/tour/ (call it 2011 data)
and I did imputation on the same genotype files on Michigan Imputation Server, Genotype Imputation (Minimac4) 1.2.4 (call it 2020 data)
using the same QC steps I perfomed GWAS using plink.
In 2011 I have ~2.5 million SNPs and in 2020 I have ~2.7 million SNPs. The issue is that only ~900000 SNPs are matching between those two data sets. Can someone please explain me why? Did RS names changed in the meantime? I did put both genotype files on Build 37. Here I am presenting number of SNPs per chromosome for old (2011) data and new (2020) data.
Also I am comparing snps_that_can_be_found_in_old_but_not_in_new and snps_that_can_be_found_in_new_but_not_in_old.
Can someone please explain me what might be the issue and why there is only ~900000 SNPs matching SNPs?
Relevant answer
Answer
It seems that 2011 imputation was done using HapMap 2 reference panel and 2020 using HRC 2016 reference panel. This paper has a figure that show changes in signal for a partical SNP between those two version of reference panels. Can somebody explain me how different reference panels change the resulting signal for the same phenotype? https://www-nature-com.proxy.cc.uic.edu/articles/ng.3643/figures/2
  • asked a question related to GWAS
Question
1 answer
Hi, I have two data sets from the illumina omniexpress snp array platform. The first data set was mapped using the GRCh37 build and the second one was more recently read using the GRCh38 build. Not surprisingly when I've tried to merge the files in PLINK for a larger analysis it comes up with the warning snp rs... is in a different genetic position. Is there any way to update the build of the first data set? Or suggestions for how best to proceed, I haven't done much genetic analysis before so any help would be welcome :)
Best,
Mari
Relevant answer
Answer
Hi,
Between GRCh37 and GRCh38 some of the items should be the same. For those that differ, I recommend the website:
Unfortunately, I'm afraid that with a large amount of data, the conversion may be a time-consuming process, but it's always a solution.
Hope this helps! Feel free to ask if you have questions about how to use the program to convert.
  • asked a question related to GWAS
Question
1 answer
i am learning GWAS analysis using http://www.stat-gen.org/tut/tut_preproc.html tutorial but am getting these errors:
Error in read.plink(gwas.fn$bed, gwas.fn$bim, gwas.fn$fam, na.strings = ("-9")) :
Couln't open input file: ~/Desktop/gwas//GWAStutorial.bed
Relevant answer
Answer
That double slash in "gwas//GWAStutorial.bed" does not seem right...
Check out your working directory, does the input route is correct? Or, are the necessary files in the working directory? Maybe a name is not ok, or a bad typo is happening
  • asked a question related to GWAS
Question
2 answers
Hi,
I have trouble in understanding GWAS research papers (as they are full of statistics... )for finding phenotypic to genotypic relationship in crop plants.Can someone suggest me the basic material to start reading with specific reference to plants.
Relevant answer
Answer
Thank you very much.
  • asked a question related to GWAS
Question
3 answers
I am conducting GWAS using GAPIT R package with FarmCPU model. However, unlike GLM and MLM, GAPIT does not produce R2 when FarmCPU model is used. I tried to work around this by using the linear model function in R (lm) like this: fit<-lm(trait~SNP, data=mydata) but I am not sure if this is correct because I got R2=0 for some SNPs with very significant GWAS signals! I have seen hierGWAS package which gives R2 for cluster of SNPs but not single SNP.
I appreciate your suggestions on this and thank you for your time.
Relevant answer
Answer
  • asked a question related to GWAS
Question
6 answers
Hi,I am doing GWAS analysis on both quantitative and qualitative trait through GAPIT 3.0.
Code run well for Quantitative traits but shows following error in qualitative trait
Error in plot.window(...) : need finite 'ylim' values
is it because my trait does not follows normality or anything else. Please suggest me a proper method for qualitative trait.
Relevant answer
Answer
Hi,
Thanks a lot @Peymaneh Davoodi
Actually the problem was in phenotype file. Instead writing the actual phenotype, we need to write 0 1 for discrete trait.
It works for me.
GAPIT takes actual phenotype say for example flower colour red/white as NA, so it should be 0/1 instead
  • asked a question related to GWAS
Question
3 answers
What is the minimum acceptable broad-sense heritability value of a quantitative trait to decide if the trait is suitable for GWAS analysis or not?
Relevant answer
Answer
Dear Daniel;
As far as I know, based on studies I have read about GWASs, there is no restriction on GWAS due to the amount of heritability.
However, when broad-sense heritability is an issue, plants should probably be your focus.
As the major problem in GWAS is false positives caused by population structure and hidden relatedness which inflates association results and also because GWAS uses a single-locus approach, testing markers one by one, despite the fact that, the true genetic model of complex traits represents many loci affect a trait simultaneously. Determining the correct P-value threshold is statistical importance for distinguishing true positives from false positives and false negatives and it is the place that the amount of broad-sense heritability comes to help according to my knowledge.
Best regards
  • asked a question related to GWAS
Question
6 answers
Why people are enthusiastic about GWAS given the fact that there are only a few examples of success? Alternative ways to identify genes controlling traits available?
Relevant answer
Answer
I believe this paper can answer your question:
for the next, we go further with Omics and NGS techs, we also can use AI and genome editing.
Best
  • asked a question related to GWAS
Question
13 answers
I am investigating on SNP-trait association in a species with a narrow genetic base. I have phenotyped and genotyped 80 accessions. I would like to use GWAS for the association analysis.
From literature, a suitable GWAS analysis requires a large sample size. My question is: what is the minimum sample size required for GWAS? Or How large should the sample size be?
Relevant answer
Answer
It is better to use more than 100 individuals (>300 is prefer) to perform GWAS. Restricted population size will significantly lower the power and increase false positive. Theoretical and applied genetics will not consider GWAS with population size less than 100.
  • asked a question related to GWAS
Question
2 answers
Hi,
I'm looking at running some 2 sample MR analysis and my outcome GWAS was run in UK biobank, so i'm looking for a sex-specific GWAS for testosterone that was pulled from a different cohort. I've been having a browse online, but most of what i've found is either UKB or men only.
If anyone could point me in the right direction that would be great!
Thanks
Relevant answer
Answer
Hi
there are few testosterone GWAS studies that can be found at EBI (https://www.ebi.ac.uk/gwas/efotraits/EFO_0004908) but you'll need to dig somewhat to get informations on females, if it exists since low production of this hormone in the best part of humankind...
fred
  • asked a question related to GWAS
Question
1 answer
I need to perform a meta-analysis of 2 GWAS studies and I need assistance with the input study files (plink --assoc.logistic) as well as the output meta-analysis file (.TBL).
- Firstly, I am not sure about the METAL script because of the format of the plink assoc.logistic files. (Script is attached)
- And secondly there are missing columns/information in the meta-analysed output file, such as CHR, POS, Allele1, Allele2. Do these columns need to be manually added?
Thanks :)
Relevant answer
Answer
Hello Patricia
It requires a driver file at first for describing the input files, determining the strategy of meta-analysis, and naming the output files ( exactly the same as you provided in "metal_scrip.txt) then you need an input file with certain columns ( allele1,allele2, OR, P-value, SD, and weight column)
For making an input file you need to extract the required information of the top SNPs from the 2 GWASs results from Original long data format file which contains alleles, position, and chromosome by specifying the SNPs ID and matching them together as an input file. Finally running METAL.
All the best
  • asked a question related to GWAS
Question
3 answers
There are some regular GWAS data (control and cases with some kind of tumors) in my group. Do you have any novel idea about doing some interesting research using these data?
Relevant answer
Answer
Hi Ye Yao
There are novel and interesting analyzes you can perform according to your preference:
1- Estimation of SNP heritability for assessment of genetic architecture.
2- Testing for haplotype-based case/control and quantitative trait association.
3- Genome-wide CNV analysis for detecting trait-CNV associations.
4- Genome-wide LD evaluation for quantifying genome architecture.
5- Polygenic risk scores for detecting pleiotropy and eventually for validating GWAS discoveries.
6- Estimation of genetic correlation for detecting and quantifying pleiotropy.
7- Mendelian randomization for testing causal relationships and empirical evidence of observational associations that are not causal.
8- Population differences in allele frequencies for reconstructing population history and detecting selection.
and 9- Integration of GWAS data with eQTL studies.
Best Regards
  • asked a question related to GWAS
Question
3 answers
I have a biparental population of Cacao for about 188 lines. I was initially thinking of doing QTL mapping, however, when I got the GBS data for my samples, I was informed by the sequencing company that there is a lot of genetic distance between the markers and I should go with the GWAS approach? Can anyone please give me some advice on it?
The genetic distance between the two parents used to construct the mapping population is 0.5635
Relevant answer
Following
  • asked a question related to GWAS
Question
5 answers
Hello everyone
Sometimes interpreting biological terms/pathways related to a particular phenotype might be very difficult and confusing. I have read many papers which only discussed a few selections of pathways related to their objectives of study and simply ignored the rest of pathways and terms (buried in supplementary materials!). This is an issue which needs an urgent solution. In Animal Science, we work with traits e.g. milk production or birth weight which are complex traits and multiple genes involved in the whole process. Although through GWAS studies we know most of genes involved in a complex trait. To my knowledge the "gap" is: there is no direct biological pathway (for example in Cytoscape or David) related to milk production.
I know "mammary gland development" or "cell-cell junctions" or other known terms, together are related to milk production but they need to be altogether in one term. This was only one example among many other important traits.
I would like to hear other peoples opinion.
Cheers
Relevant answer
Answer
Hello,
Thanks for sharing this mind overwhelming discussion.
Obviously, there is no way to solve natural problems except by simplifying them. But if we want to have the closest answer to reality, we should not simplify the matter too much. In my opinion, when we discuss a trait in a single form, we have simplified the issue too much. Because traits are dependent tightly through the different sources, including the genetic correlation, the physical correlation of genes, the LD between several genes based on assortative mattings, and so on. In order that a complex network of the biological pathways will emerge. So just as we are recently dividing the genes that affect a trait into core genes and peripherals, accordingly we can consider pathways to more highlighted and weaker biological pathways, not just ignore them.
Presumably, any simplification and any isolation in biology lead us far from the reality of nature and we know we can not refuse it in our studies.
  • asked a question related to GWAS
Question
15 answers
I want to test and develop algorithms for predictive modelling, but all GWAS data mentioned in papers are private, available only through request to the authors and much bureaucracy.
Relevant answer
Answer
Dear colleagues,
You can examine the GWAS studies which have been carried out within the framework of NSF projects. In this case, the authors are obliged to make all the data obtained available to the entire scientific community.
With best wishes,
MB
  • asked a question related to GWAS
Question
3 answers
Can anyone suggest a reason why the association between my SNPs (90 thousand) and two quantitative traits (BMI and age) has a FDR-adjusted P-value over 0.9 for all my SNPs? I think it is very unlikely that not a single SNP is associated... They come from ichip genotyping and I did an imputation with the Michigan Imputation Server.
I had my files in PLINK vcf format and I transformed them to HapMap using TASSEL, and my phenotypes are in a tab separated file just as in the GAPIT manual.
My code was:
myG_tAPIT <- GAPIT(
Y=myY_t,
G=myG_t,
PCA.total=3,
model=c("CMLM")
)