Helping Genetic Research

[dawntreader3]

I am by no means a geneticist...but I think genetic research into curing genetic diseases, prolonging lifespan, etc. are worthy research goals.

 

With that said, I am wondering if anyone knows of research projects that the normal, average public can get involved with and help?

[CharonY]

As participant in epidemiological studies perhaps?

[Genecks]

I am by no means a geneticist...but I think genetic research into curing genetic diseases, prolonging lifespan, etc. are worthy research goals.

 

With that said, I am wondering if anyone knows of research projects that the normal, average public can get involved with and help?

 

Find an organization/business that specializes in things that you are interested in.

Donate money to it.

 

Possible example: http://en.wikipedia.org/wiki/Immortality_Institute

[motor30]

if you are healthy, you can donate your blood.

[CharonY]

Actually samples from diseases patients are generally more interesting.

[MedGen]

Actually samples from diseases patients are generally more interesting.

 

Every GWAS needs suitable controls. Perhaps there should be a consortium for healthy control volunteers to donate to with all the same informed consent requirements as other clinical samples.

[Variola]

Every GWAS needs suitable controls. Perhaps there should be a consortium for healthy control volunteers to donate to with all the same informed consent requirements as other clinical samples.

 

 

Yes good point, linkage disequilibrium studies and the like are pretty reliant on healthy volunteers. I have wondered before how they managed to get such a wide range of genomic data. Diseased patients samples are much easier to come by because they are under medical monitoring.

[CharonY]

There are a number of depositories for "healthy" samples (mostly body fluid). Main problem is usually that depending on the study the definition of healthy may be off. Due to the low frequency of many genetic diseases the sample size for the disease samples are often too low and biased.

[MedGen]

There are a number of depositories for "healthy" samples (mostly body fluid). Main problem is usually that depending on the study the definition of healthy may be off. Due to the low frequency of many genetic diseases the sample size for the disease samples are often too low and biased.

 

I was thinking more in terms of GWAS for complex common diseases. I think many of the GWAS to date have utilised samples form other disease-based repositories, for instance using cancer patients as controls for autoimmune disease GWAS (can't recall the exact study now). Though now I recall that the 1958 Birth Control Cohort and British Blood Service were used for the WTCCC GWAS of seven common diseases as controls.

 

Of course the HapMap and 1000 Genomes project could provide a certain number of samples, well if HapMap the populations were also able to provide phenotypic information that is. Hopefully the Personal Genome Project will also become a repository of control samples that can be used for GWAS, afterall, it is only the genotypes themselves that are required, not the physical DNA samples (unless of course there are disparities between genotyping platforms, etc - but that's more an issue of standardising genotype calling methods and techniques).

[CharonY]

There is a problem (in the whole area of biomarker research), though. The more complex it is the less likely a significant association can be found. Or rather the associations are likely to be false positives. There are a lot of reasons of course, and not all of them connected to sample size (although this is a huge issue). For complex diseases it is also tricky to characterize the samples properly, for instance.

For these and more reasons GWAS (as well as other high-throughput biomarker) studies have come under fire and there is an ever stronger call for finding mechanistic connections, instead. While some propose that throwing more data at the problem will help (calculations are indicating sample sizes of around 10-100k). Personally, I am not that sure.

[MedGen]

There is a problem (in the whole area of biomarker research), though. The more complex it is the less likely a significant association can be found. Or rather the associations are likely to be false positives. There are a lot of reasons of course, and not all of them connected to sample size (although this is a huge issue). For complex diseases it is also tricky to characterize the samples properly, for instance.

For these and more reasons GWAS (as well as other high-throughput biomarker) studies have come under fire and there is an ever stronger call for finding mechanistic connections, instead. While some propose that throwing more data at the problem will help (calculations are indicating sample sizes of around 10-100k). Personally, I am not that sure.

 

Oh to be sure there is an issue regarding the so-called hidden heritability regarding the findings of most GWAS, especially regarding their limitations, but at the end of the day they still have their uses. For instance, in uncovering previously previously unsuspected pathways that might contribute to disease aetiology and pathogenesis. My personal feelings are that possibly there are multiple rare variants in linkage with the known associations that only fully genome sequencing will uncover. Of course this has its own problems in that these rare variants may each have different synergistic or additive effects with other variants. At the end of the day GWAS have only scratched the surface of complex disease genetics, there is still a long way to go in our understanding of how they contribute disease, and of course to their clinical utility. I think it is a little premature to simply say that GWAS have failed as a tool for uncovering the genetic basis, they are merely another tool, a stepping-stone perhaps, in our understanding. It frustrates me a little that they have come under so much fire, perhaps because of the unwarranted hype that they would ultimately uncover all the dark secrets of the genome. A rather naive view to take if you ask me.

 

Any attempt to uncover a few limited biomarkers as prognostic and diagnostic indicators are also going to fall foul of the same mistakes, as you state because of their complexity. In my opinion it is going to be a case that large panels of different serum/blood/bodily fluid biomarkers are going to be required to be clinically useful rather than one or a few of high impact. This has particular ramifications for pharmacogenomics, and of course personalized medicine. I think we a lot further away from this goal than many people make out to believe.

[CharonY]

I think we a lot further away from this goal than many people make out to believe.

 

We are incredibly far out. In fact, so far out that now more than ever questions arise whether it is possible at all. Increasing the panel size is a hotly proposed element, yet there is a lack of evidence that it will help at all. Practically it may just lead to even more overfitting issues. Personalized medicine is also one of the hip buzzwords (and admittedly I used it more often that I felt comfortable with) that is even more complicated.

 

 

It frustrates me a little that they have come under so much fire, perhaps because of the unwarranted hype that they would ultimately uncover all the dark secrets of the genome.

This is the same with basically all omics based approaches (which is more my field).

 

I believe that we are at a point where we have all these cool postgenomcis tools but we have to start asking how to employ them properly. The initial answer in the nineties was "hire bioinformaticians and let them sort it out" but clearly this cannot and did not work. It is thus the duty of biologists to step back, maybe take a look at other disciplines and figure out how the experiment has to be conducted to yield significant information (rather than just look for differential expression, for instance) and, maybe by taking a more reductionist approach for starters, clearly define what would could be considered a significant information (rather than wading through and cherry picking things in the data moat).

Heck, if I get the funding this is what I would do...