# GCTA for big datasets and sequencing data

**URL:** <https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754>\
**Category:** Day 1\
**Tags:** gcta\
**Created:** [June 2, 2026, 7:35am UTC](https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754 "2026-06-02T07:35:42Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![carmen.ramoser](https://isgw-forum.colorado.edu/letter_avatar_proxy/v4/letter/c/3d9bf3/32.png) [@carmen.ramoser](https://isgw-forum.colorado.edu/u/carmen.ramoser)\
**Post date:** [June 2, 2026, 7:35am UTC](https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754/1 "2026-06-02T07:35:42Z")

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Thank you for the very interesting practical despite the problems.

Loic mentioned that GCTA should not be used for big datasets. I wanted to ask if it is possible to use it for a dataset of 70.000? As I understood the theory, GCTA gives the pedigree heritability as opposed to summary statistics, which will give the SNP heritability. I am interested in this difference. For this reason I also wanted to ask if anybody has experience with running gcta on sequencing data on the UKB RAP. How much computational resources would this take?

Thank you in advance!

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**Author:** ![maria.koromina](https://isgw-forum.colorado.edu/letter_avatar_proxy/v4/letter/m/51bf81/32.png) [@maria.koromina](https://isgw-forum.colorado.edu/u/maria.koromina)\
**Post date:** [June 2, 2026, 5:59pm UTC](https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754/2 "2026-06-02T17:59:42Z")

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I believe you can also use GCTA (GREML) to estimate SNP heritability but happy to see what people here think too.

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**Author:** ![l.yengo](https://isgw-forum.colorado.edu/letter_avatar_proxy/v4/letter/l/a9adbd/32.png) [@l.yengo](https://isgw-forum.colorado.edu/u/l.yengo)\
**Post date:** [June 3, 2026, 2:00am UTC](https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754/3 "2026-06-03T02:00:41Z")

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Thanks for the question, Carmen. I’d clarify that the issue with sample size applies to the GREML method. GCTA also implements Haseman-Elston regression, which scales quite nicely and therefore can be used with N=70k. GCTA can give you both pedigree-based or SNP-based heritability depending on the range of genetic relationships included in your analysis. If you only give it “unrelated” individuals then you’d get SNP-based heritability and if you include closer relatives (e.g., siblings, parent-children pairs) then your estimate will be closer to a pedigree-based method. Methods using GWAS summary statistics would generally only give you an estimate of SNP-based heritability. We used GCTA (or PLINK) to calculate GRMs in one of our recent papers ([https://www.nature.com/articles/s41586-025-09720-6](https://www.nature.com/articles/s41586-025-09720-6)). Cost inference depends on how many SNPs and people you include in your analysis. You may need to budget for at least 1000 GBP for data processing.

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**Author:** ![carmen.ramoser](https://isgw-forum.colorado.edu/letter_avatar_proxy/v4/letter/c/3d9bf3/32.png) [@carmen.ramoser](https://isgw-forum.colorado.edu/u/carmen.ramoser)\
**Post date:** [June 3, 2026, 12:21pm UTC](https://isgw-forum.colorado.edu/t/gcta-for-big-datasets-and-sequencing-data/754/4 "2026-06-03T12:21:29Z")

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Dear Loic,

thank you for your answer, this is very valuable to me! Indeed I would like to do something similar as you did in your paper.