Kaitlin Samocha Profile picture
Oct 27 11 tweets 4 min read
Maureen Pittman #ASHG22: Oligogenic inheritance describes situations were there are some loci (more than one, less than thousands) that contribute to a disease.

Congenital heart disease (CHD) is potentially one of these situations.
Maureen Pittman #ASHG22: Example of a family where GATA6 was passed on from an unaffected parent to a child with CHD. Common variants don't seem to contribute much -- could it be due to other rare variants?
Maureen Pittman #ASHG22: Oligogenic genes are presumed to have related functions and could potentially compensate for each other or could interact, etc.

But finding oligogenic gene examples has been hard due to all of the combinations you have to test.
Maureen Pittman #ASHG22: Want to use parent genotypes to establish how often certain pairs of variants should appear in the child. GCOD (gene combinations in oligogenic disease) algorithm.

Using 3.3k cases in pediatrics cardiac genomics consortium.
Maureen Pittman #ASHG22: Can make simulated offspring datasets to compare the rare predicted damaging variant combinations to what you'd see in a null simulation. ("family-by-gene matrix simulations")

Then can evaluate if there is significant overtransmission.
Maureen Pittman #ASHG22: GCOD network pulls out genes explicitly related to heart structure or indirectly related to heart development.
Maureen Pittman #ASHG22: Looking at a large protein complex network from CREBBP and EP300 pair.
Maureen Pittman #ASHG22: What were the most improbable pairs?

GCOD predicts and interaction between GATA6 and POR. Three probands but all of them were a combination of one inherited variants + a de novo.
Maureen Pittman #ASHG22: GATA6 and POR knockdowns show interactions in mouse heart development -- seeing half with atrial septal defect.
(As an aside, Maureen is handling the mouse struggles with grace. Particularly love the "we're doing it" quip.) #ASHG22
Maureen Pittman #ASHG22: Can read more about the work in the preprint:
biorxiv.org/content/10.110…

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More from @ksamocha

Oct 27
Graham Erwin (@grahamserwin) #ASHG22: Half of the human genome is repetitive, most are transposons but about 3% is made up of tandem repeats. Tandem repeats cause >40 human diseases.
Graham Erwin (@grahamserwin) #ASHG22: Friedreich's ataxia is caused by a triplet expansion in the first intron of FXN gene. Patients have 70++ repeats, which leads to lower expression/protein levels.
Graham Erwin (@grahamserwin) #ASHG22: Polyamides bind to DNA specifically and are very small (cell permeable). Prior example to tie one of these to a factor that would lead to higher transcription.
pubmed.ncbi.nlm.nih.gov/29192133/

Now want to target other repeats.
Read 7 tweets
Oct 27
Scott Younger #ASHG22: Genomic Answers for Kids (GA4K). Currently have >10k subjects enrolled and have returned ~1k diagnoses.

Focus here is large scale assays, specifically establishing a protocol to do patient-derived iPSC reprogramming at scale. Have generated 250 iPSC lines
Scott Younger #ASHG22: Using antisense oligonucleotides (ASOs) to modulate gene expression. Proof of their use in TNNT2 gene.
Scott Younger #ASHG22: Robust restoration of dystrophin expression in DMD-patient organoids when applying a DMD-targeting ASO. Phenotypes restored as well (beating cells in dish).
Read 5 tweets
Oct 27
Yi Ding (@yi_ding_) #ASHG22: Polygenic scoring estimates genetic value by aggregating signal across many loci. Potential for polygenic scores has already been well established -- but so have the challenges, especially the low accuracy in non-European populations.
Yi Ding (@yi_ding_) #ASHG22: Genetic ancestries are continuous and cluster-free.
- By forcing clusters on ancestry, miss inter-individual variability.
- The boundaries between these groups is also fairly arbitrary.
- Clustering also typically removes any unclassified individuals
Yi Ding (@yi_ding_) #ASHG22: Goal is to evaluate PGS accuracy at an individual level.

Using genetic distance to characterize and individual's unique position (Euclidiean distance) in genetic ancestry continuum. Also evaluating individual PGS accuracy.
Read 6 tweets
Oct 27
Now on in the plenaries -- Catherine Robertson #ASHG22: Beta cells in the islets of the pancreas produce insulin. Type 1 diabetes is defined by loss of insulin production due to cell death or dysfunction. Disease risk is both genetic and environmental.
Catherine Robertson #ASHG22: Many common variants tied to T1D, but causal variants are hard to find -- some seem to impact cis-regulatory processes.

Now doing single cell/nuclei RNA-seq, ATAC-seq, and 5' RNA-seq.
Catherine Robertson #ASHG22: Nice overlap of all three data types for the PDX1 locus. Can describe regulatory processes across various cell types or stimuli +/-.

Hypothesis: T1D variants alter regulatory processes in islets.
Read 4 tweets
Oct 26
Jazlyn Mooney (@Jazlyn_Mooney) #ASHG22: Want to trace the genealogical ancestors in African American individuals. 1870 census was the first collected to have more detailed information on African Americans in the US -- but still not a lot. Many do not know their roots.
Jazlyn Mooney (@Jazlyn_Mooney) #ASHG22 draws a distinction: genealogical and genetic ancestors are not the same.

Can use ancestry proportions from the present to estimate the number of ancestors from each source population in past. Implementing a mechanistic model of admixture
Jazlyn Mooney (@Jazlyn_Mooney) #ASHG22: Model estimates the number of genealogical ancestors. Applying to African American ancestry individuals. Slave trade moved many individuals to the Americas. Shows time epochs on the model (slave trade, slavery, segregation era).
Read 5 tweets
Oct 26
Alex Diaz-Papkovich #ASHG22: Genetic data has structure due to geography, non-random mating, culture, etc. Visualization helps with this, but how do we delineate these clusters?
Alex Diaz-Papkovich #ASHG22: Genetic data is very noisy and we frequently may not know how many clusters truly exist. Clustering is also open to misinterpretation -- clusters are useful but are abstractions!
Alex Diaz-Papkovich #ASHG22: Notes that UMAP isn't a clustering algorithm. HDBSCAN is the algorithm used to cluster in this talk -- unsupervised and works well with UMAP data.
Read 6 tweets

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