How to have Polygenically Screened Children

Polygenic screening is a method for modifying the traits of future children via embryo selection. If that sounds like gobbledygook, then think of it a bit like choosing stats for your baby.

That may sound amazing. It may sound like science fiction. It may even sound horribly dystopian. But whatever your feelings, it is in fact possible. And these benefits are available right now for a price that, while expensive, is within reach for most middle-class families.

On a more serious note, there is limited selection power available with today’s technologies, so you will not be able to have a baby Einstein unless you are already a Nobel laureate. But polygenic screening will allow you to decrease your child’s risk of common diseases by 10-60%, reduce their risk of mental disorders, and increase their IQ by somewhere between 3 and 8 points. If you are willing to wait a few years, you may be able to increase IQ by up to 13 points. These benefits are available for between $30k-100k depending on how strong of a benefit you want and what kinds of traits you want to select for.

There has been quite a bit of discussion of this topic on LessWrong and adjacent communities but very little concrete advice for would-be parents who are curious whether the benefits are worth the price, particularly for those who have no other reason to do IVF. The purpose of this post is to fill that gap by addressing costs, potential medical complications, choice of clinic, which labs are best, and how age and infertility diagnosis affect the expected benefits.

This is a long post and I expect most people will not want to read the whole thing. If this is you, please use the section selector in the sidebar to navigate to the section you are most interested in. You may want to simply skip to the section titled “The Benefits of Polygenic Embryo Screening”.

Background on IVF

A diagram showing the steps in an IVF cycle. Source

Wait, what even is polygenic embryo selection?

Embryo selection is all about picking an embryo to (hopefully) turn into a baby. This occurs during the process of In-Vitro Fertilization, or IVF. In the typical IVF cycle, a couple goes into a fertility clinic because they want to have a baby. Usually this is because they’ve been having trouble conceiving naturally, but couples also seek out IVF when they want to do genetic testing, select the sex of their child, or to preserve fertility for later pregnancy.

The doctor conducts a bunch of medical tests, and if they all check out, the woman begins a hormone regimen that will stimulate an abnormally large number of her eggs to mature all at once.

At the end of the regiment, the doctor extracts a bunch of mature eggs from the woman’s ovaries, which are then fertilized using the father’s sperm and grown in a lab dish for 4-7 days. When the embryo has finished growing, there are often four or more that can be implanted in the mother. Most couples do not want four children, so a choice must be made about which embryo to pick.

In ye olden days, doctors would often just transfer all the embryos at once in the hope that at least one of them would result in a baby. Sometimes this would work well; one of the embryos would happen to stick and the parents would be very happy. Other times it would work a little too well and more than one of the embryos would implant. This is why twin births are so much more common during IVF than during normal pregnancy.

Multiple birth rate in the UK over time. The trend is broadly similar in the USA. Source

Transferring multiple embryos at a time is less common nowadays because IVF clinics have figured out how to reduce the odds of failed pregnancy using genetic testing. With a higher chance of live birth from a single embryo transfer, the risk of a failed embryo transfer is outweighed by the risks of a twin pregnancy. The outcomes for twin births are on average worse than for single pregnancies. Twins are more likely to be born preterm, develop health problems, and put excess stress on the mother’s body.

This brings us back to embryo selection; the doctor or embryologist has to make a choice about which embryo to transfer first. All clinics have to make this choice, so all practice embryo selection of some kind. But the criteria for selecting the embryo have, until recently, been pretty dumb.

The standard practice is for an embryologist to look at all the embryos under a microscope and pick the one that looks the prettiest. I am not kidding. The embryologist will rank the embryos from best to worst based on their “morphology”, which accounts for factors like their rotational symmetry and whether or not they have a dark and rough colored appearance.

This embryo is pretty and will be transferred first
This embryo is ugly and will only be transferred as a last resort

To be fair to the embryologists, this method is better than just randomly picking an embryo; embryos with particularly bad morphology gradings do actually have a lower chance of resulting in a live birth. And for a long time, there was simply no other option But times have changed and we can now select embryos by DNA rather than simply the appearance of their cells under a microscope.

But how do they even get an embryo’s DNA?

The Blastocyst Biopsy - RGI
This is what an embryo biopsy looks like. You can see a video of the process here

All the best techniques for genotyping rely on destructive sequencing, meaning the cells whose DNA is read must be destroyed. Embryos don’t have very many cells. So how do we get information about what’s in its genome without destroying it?

It turns out that after roughly five days of development, embryos possess a very interesting property; one may remove up to about 10 cells with little to no measurable impact on the embryo’s ability to develop into a healthy child. The embryo can regenerate up to about 10% of its mass! That’s the equivalent of losing and then regrowing both your arms as an adult. This is very fortunate for us because these cells contain a treasure trove of information.

The most common thing IVF clinics look for is aneuploidy, which is a medical term meaning “this embryo has an abnormal number of chromosomes”. The term for this type of testing is “PGT-A”, and it’s performed in roughly half of all IVF cycles in the US today.

Human embryos with the wrong number of chromosomes are surprisingly common, both among IVF patients and natural pregnancy. But this wasn’t very well understood before the first use of pre-implantation genetic testing in the late 1980s.

IVF Doctors started wondering why so many transfers were failing to result in pregnancy, or resulting in pregnancy followed by very early miscarriage. They discovered that roughly a third(!) of all pregnancies, both natural and via IVF have chromosomal abnormalities. Most of the time these go undetected because their immediate effect is to result in the arrest of the embryo’s growth, or to cause a very early miscarriage (often before the woman even knows she is pregnant).

IVF clinics began testing embryos for aneuploidy in the early 1980s. But in the late 2000s, something happened that completely changed the landscape of genetic testing.

Why Polygenic Screening wasn’t possible before 2015

The cost of sequencing a fixed amount of DNA has declined dramatically since Fred Sanger and his team pioneered the first methods in 1977. There is a kind of “Moore’s law of sequencing” in which the cost of sequencing a fixed amount of DNA has declined exponentially over time.

However, something incredibly dramatic happened to DNA sequencing in about 2007. Take a look at this graph:

I don’t think I’ve ever seen a graph that looks like this anywhere else. Between 2007 and 2010, the cost of sequencing a megabase of DNA dropped by a factor of a million! That unbelievable, super exponential drop was made possible by a technology called “Next Generation Sequencing”.

By the mid-2010s, you could genotype all the parts of a person’s DNA most likely to differ from other people’s for under $100. At that price point, it became possible to gather genomes from hundreds of thousands of people and assemble them into giant databases that researchers could access.

This was incredibly important, because you NEED hundreds of thousands of samples to make good genetic predictors. It turns out that most of the traits we care about like heart disease risk or intelligence or attractiveness are determined not by a handful of high-impact genes, but by the cumulative effect of thousands of genes, each of which has only a tiny impact.

Take educational attainment. Educational attainment is not the most heritable trait, but because research on it is more politically acceptable in a university environment than the direct study of intelligence, we know quite a bit about its genetic roots. The latest large-scale study of it included data from 2.7 million participants. Among all genes identified, the one with the single largest effect size only increased the amount of time you spent in school by at most 2.8 weeks (see section 3.4). That’s it! The average gene has a tiny, tiny impact on how long you spend in school. The predictor used 2,925 genes to explain just 15% of the variance in how many years of school a person completed.

So you actually NEED these gigantic databases to explain more than a tiny fraction of the variance in complex traits. This is why polygenic embryo selection was impossible before about 2016; there just wasn’t enough data to figure out which genes did what.

How do they know which genes do what?

Once you have a giant biobank and information about people’s traits and diseases, you still need to figure out which genes do what. I mentioned an educational attainment predictor in the section above, but I didn’t explain how they created it. So how did they do it?

The answer is actually not too complicated: a researcher will use one of these gigantic biobanks plus a machine learning model to identify which genetic variants are associated with an increase or decrease in a given trait.

The dumbest possible way to do this is with a Genome Wide Association Study, or GWAS. It works something like this:

Let’s say there’s a gene with two different variants commonly present in the biobank population. 96% of participants have an “A” at some particular location in the gene, but 4% have a “T” instead. We want to know whether having a “T” makes you taller.

A GWAS just measures the average height of people with an A and the height of people with a T to see if they’re different. Then it uses a statistical significance test to see if the result could have plausibly been the result of random chance. If not, the researchers reason that the gene is having an effect on height. If it passes this test, it is added to the “list of important genes for height”.

For such a dumb method, this works remarkably well. Height predictors created using GWAS results correlate with actual height at about 0.55.

The smarter way to do this is to use some kind of machine learning method like LASSO. This will give you a better predictor for the same amount of data. But for some reason I still don’t really understand, academia almost exclusively uses GWAS.

A graph showing LASSO predicted vs observed height in a validation cohort.

Correlation or causation?

“OK”, you might say. “That’s well and good, but how do we know that these genetic differences are actually CAUSING someone to be taller or smarter rather than just spuriously correlated with height?”

The main reason this is possible is because nature has already conducted a randomized control trial on our behalf. Every time your body produces a sperm or egg cell, your DNA is more or less randomly mixed up and half of it is given to the reproductive cell. This means that, conditional on parental genomes, sibling genomes are randomized!

A diagram showing how genetic randomization (meiosis) works for a single chromosome.

In turn, this means that if a gene can predict differences between siblings, you can be quite confident that it is in fact CAUSING the difference. This is actually quite a remarkable fact, and one that underpins the entire reason for believing embryo selection should work.

There is one asterisk here; though a sibling GWAS can tell you where the causal variant is, it usually can only narrow down the list of candidates to perhaps 10 distinct variants within a region of very roughly 100,000 base pairs. This is sufficient for embryo selection because that set of 10 base pairs will almost always be inherited together. But if sometime down the line we want to do embryo editing, it will require us to either pinpoint the causal variant precisely or to edit all 10 variants that have a decent chance of causing the observed change.

Another crucial insight from these studies is that nearly all of the genetic differences between humans can be explained by additive effects; there are very few gene-gene interactions going on; If gene A makes you taller, it doesn’t depend on gene B being present to work its magic. It’s a strong, independent gene that don’t need no help.

This fact is extremely important because it makes both evolution and embryo selection possible. There is a common misconception that genes are tied together in a hopelessly complex web and that if we mess with one part of it the whole thing will come crashing down. While that may be true for genes that are universally present in the human population, it is very rarely true for genes that commonly vary between people.

You have the predictors. Now what?

Once you have created genetic predictors using GWAS or LASSO or some other method, you can then feed in the embryo’s DNA to the trained model and get a prediction of each embryo’s expected trait value. You do this for every predictor you have (or at least those you care about), and then pick an embryo based on the results.

But there’s one last question to answer: which traits should you care about? If one embryo has a 20% chance of getting breast cancer and a 10% chance of getting heart disease, is that better or worse than an embryo with a 10% risk of breast cancer and a 20% risk of heart disease? Or how about one that has a high risk of both but is also predicted to have an IQ 5 points above average?

There is no universally agreed-upon method for making the choice about which embryo to implant. My personal hope is that someone (maybe even me!) makes a tool to assess what parents find important and then ranks embryos according to those criteria.

The Benefits of Polygenic Embryo Screening

CategoryTraitImprovement RangePublicly Available?
Non-diseaseIntelligence+1.6-7.5 IQ pointsNo*
Non-diseaseHeight1-6 cmNo*
DiseaseAlzheimer’s15-50% reductionYes
DiseaseAtrial Fibrillation10-50%Yes
DiseaseBreast Cancer3-50%Yes
DiseaseBasal Cell Carcinoma3-45%Yes
DiseaseCoronary Artery Disease20-60%Yes
DiseaseHeart Attack25-70%Yes
DiseaseHigh Cholesterol12-50%Yes
DiseaseInflammatory Bowel Disorder5-65%Yes
DiseaseIschemic Stroke5-20%Yes
DiseaseProstate Cancer2-60%Yes
DiseaseType 1 Diabetes10-55%Yes
DiseaseType 2 Diabetes20-60%Yes
DiseaseTesticular Cancer0-55%Yes
Mental DisorderMajor Depressive Disorder5-20%Yes
Mental DisorderSchizophrenia5-75%Yes

*See the section below for how to get access to these predictors

Ok, enough with the theory. How big of a benefit can you actually get from going through IVF and screening your embryos?

I’ll start with the one everyone always asks about: intelligence. How much can you boost your child’s IQ with embryo selection?

How much can embryo selection increase my child’s IQ?

First, there is no company that publicly offers embryo selection for intelligence. I have heard rumors that there is a group of people offering this service, but I haven’t been able to find anything public about them. If you’re actually doing IVF, you might want to reach out to Jonathan Anomaly, who knows people working in this space..

I don’t know the exact quality of their predictor, but I have a pretty good guess based on publicly available research which they are likely using as their starting point. Assuming they’ve produced something at least as good as what’s available in the Educational Attainment 4 study, I think it’s likely that the predictor correlates with measured IQ at about 0.3.

So how big would the gain be? Using some code from Gwern’s monster post on embryo selection for intelligence, I’d estimate that if both parents are of European ancestry and you have 10 euploid embryos to pick from, the gain would be about 5 IQ points. It’s plausible that you would get up to maybe 40 euploid embryos if the mother is young and you do multiple rounds of egg retrieval. In that case, you could probably get a gain closer to 7.5 points. If the mother is older it will be less. There’s also a reduction in benefit if one of the parents is of non-european ancestry, though I’m not sure exactly how much. A safe estimate is that the expected gain in non-Europeans is cut by a third. So if one parent is European and the other is Chinese, for example, the expected IQ gain would be about 4 points.

This is an unfortunate side-effect of the fact that there aren’t enough non-Europeans in the large biobanks on which these predictors are trained.

There is significant room for this benefit to improve in the near future. If we simply gave intelligence tests to people who are already participating in existing biobanks, we could increase the IQ gain from embryo selection by about 70% or more. This would imply a gain of 8.5-13 IQ points. Administering these intelligence tests could be done for a few tens of millions of dollars (or perhaps less if you’re clever about it).

This future increase in the efficacy of embryo selection has an obvious implication: if you freeze eggs or embryos now, you’ll have more embryos to pick from (since egg production declines with maternal age), and if you wait a few years to implant them, the expected gain from selection will be higher. This is my own personal plan.

Disease Reduction

Unlike intelligence, there actually are several companies that offer polygenic embryo screening for disease risk. For this reason, I can tell you quite a bit about exactly how much you can reduce disease via embryo selection.

There are two main “categories” of disease risk to think about: monogenic disease risk (which includes diseases like Tay Sachs, Cystic Fibrosis etc), and polygenic disease risk, which includes heart disease, alzheimers, schizophrenia, diabetes and most others.

The chance of you and your spouse having a child with a monogenic disease is about 1%. So unless you know that both you and your spouse are known carriers, this is not a reason worth doing IVF and embryo selection.

However, everyone has a non-zero risk of some kind of disease, and embryo selection to reduce polygenic disease risk is already available in clinics.

The first company to offer this was Genomic Prediction. Orchid Health also finally offers polygenic embryo screening, though I believe they charge more per embryo. One other lab in China appears to have recently deployed a similar test, though it looks as though they screened embryos for type 2 diabetes risk exclusively, which is not a very sensible strategy in my opinion.

Because of that, and because I suspect few of my readers would be interested in flying to China to use worse screening technology, I will look exclusively at work done by Genomic Prediction.

They use a pretty straightforward and simple method for determining an embryo’s relative ranking: each disease is weighted according to its impact on disability-adjusted lifespan. According to one of their recent papers, selecting embryos in this manner results in a fairly impressive reduction in disease risk across multiple conditions. Here’s a graph from one of their latest papers showing the expected reduction in the relative risk of various diseases from selecting the best of five embryos.

AD = Alzheimers, AFib = Atrial Fibrillation, ASA = Asthma, BC = Breast Cancer, BCC = Basal Cell Carcinoma, CAD = Coronary Artery Disease, HA = Heart Attack, HCL = High Cholesterol, HTN = Hypertension, IBD = Inflammatory Bowel Disorder, IS = Ischemic Stroke, MDD = Major Depressive Disorder, MM = Malignant Melanoma, Obes = Obesity, PC = Prostate Cancer, SCZ = Schizophrenia, T1D = Type 1 Diabetes, T2D = Type 2 Diabetes, TC = Testicular Cancer

The figure above shows disease reductions for selection among 5 “pseudosiblings” of European descent (apparently there are not enough real siblings to train predictors on). My guess is the benefits will be reduced compared to those shown, perhaps by around 20%.

Note also that this doesn’t take losses from implantation or miscarriage into account. So you’ll need more like 7 or 8 euploid embryos to achieve gains 20% lower than this. But still, the benefits are reasonably strong.

The most amazing thing to me about the above graph is that selection seems to reduce EVERY disease in the index. One of the major concerns I hear raised about embryo selection is that there might be some “hidden downside” to selection. This graph seems to suggest that, at least so far as diseases go, that’s not much of a concern.

Another way of looking at the benefits of disease reduction is to look at how much the quality-adjusted lifespan of an average child born via polygenic screening would increase compared to one born without its benefits. Here’s their analysis of this framing:

Just replace “Group size” with “number of achievable births” in your head

Keep in mind, this is the pseudosibling benefit, so take what you see on the graph above and multiply it by 0.8 to get a more realistic number. Also, the benefit is not quite as large for non-European groups. It looks like the gain is reduced by about 20-30% for South Asians, 30% for Africans and 35% for East Asians.

Of course, this all kind of ignores the elephant in the room: many of these diseases have an average age of onset of 50-75. In fifty years the world is likely to look incredibly different. If humans are still around, it seems likely that we’ll have cures or at least very effective treatments for many of these conditions.

The exceptions are mental disorders like clinical depression and ADHD, which generally have an average age of onset before age 25, and obesity, which is now showing up more and more in childhood.

Other Non-Disease Traits

Personality Traits

As with intelligence, there are no companies publicly offering screening for personality traits to the best of my knowledge. Maybe the secret startup I mentioned in the section on intelligence offers screening for personality, but I have yet to find out.

Predictors for personality traits such as conscientiousness and neuroticism are poor. This seems to be partly related to the lack of good data on such traits. A paper published in March of 2022 was able to explain 2% of the variance in conscientiousness and much less for others. There is significant room for improvement here, as most estimates peg the heritability of the big five personality traits at roughly 40-60%.

There are existing third-party services like Genomelink and SelfDecode which provide personality predictors. Unfortunately, they don’t have any published data on the quality of the predictors they use, and the anecdotes I’ve heard suggest that, at least for intelligence predictors, they are very poor. So if you’re looking for something that isn’t terrible, your best bet is to contact Jonathan Anomaly and hope he knows a group that offers a better predictor.

Facial Features/​Attractiveness

It would be nice (or possibly dystopian, depending on your views) if you could add physical attractiveness to your embryo selection criteria. More attractive people have higher lifetime income, better dating prospects, and seem to benefit generally from the halo effect.

To the best of my knowledge, no one offers this as a service right now, so unless the super secret startup offers it, you’d probably have to pay a researcher tens of thousands of dollars to develop a predictor for you.

The predictor might be halfway decent, though I don’t expect it to be as good as the ones we have for intelligence or most diseases. Here’s a meta-analysis of facial feature genetics from 2020 that found 203 genome-wide significant signals. If someone made a predictor using that paper it might be able to predict facial attractiveness reasonably well. However, there is clearly much room for improvement.

A neat picture from the meta-analysis linked above showing how some of the top SNPs affect facial structure.

Is attractiveness a purely position good? That is to say, if everyone was made attractice, would society be better off? My guess is probably would be a little better off, but I haven’t done enough research into this topic to be sure.

If you want to see what selection on attractiveness looks like in the extreme, take a look at male birds of paradise. Female birds have been selecting mates based on their plumage and dancing ability for thousands of generations, resulting in some very beautiful, but very strange looking birds.

What’s the human equivalent of these birds?

It is worth asking ourselves if dozens of generations of selection for attractiveness might result in similarly strange and pointless features whose sole purpose is to elicit arousal in members of the opposite sex.

And in the shorter run, selection for physical attractiveness probably trades off at least somewhat against selection for other features that have a greater benefit for society as a whole. So for now there is a non-zero cost to making everyone more attractive.

Still, from a purely selfish perspective, some level of selection for attractiveness makes sense. As with intelligence, no company currently offers the ability to select for physical attractiveness, unless you include selection for height, which already works extremely well. Your best bet is contacting Jonathan Anomaly to see if the groups he knows offer something. Or, barring that, you could hire a PHD student for 50k and pay them to develop a predictor for you.

Concrete Advice for Would-Be Parents

If you’ve read the sections above and you decided you want to do polygenic embryo screening, your primary objective should be to get maximum gain for minimum cost. This section is about how to achieve that goal.

There are two primary inputs that determine the effect size of polygenic screening:

  1. The power of the polygenic predictors used to select an embryo for implantation.

  2. The number of “achievable births”, meaning the number of children you would have if you implanted all of your embryos one by one.

Number 1 is largely out of your control unless your name is Gwern or you are a research scientist with access to a large biobank.

Number 2 IS within your control, at least to some degree. Here is a list of factors you might be able to change that will influence achievable births:

  1. How many IVF cycles you go through

  2. The IVF clinic you choose

  3. The PGT lab used for aneuploidy testing

  4. The age of the mother or egg donor at the time eggs are extracted (younger is always better)

  5. The stimulation protocol used

  6. The age of the father or sperm donor (younger is better)

  7. Whether you choose to freeze eggs or embryos

  8. Routine prenatal care that any good obstetrician will be able to tell you (though see Emily Oster’s excellent book if you want more details)

Why do these factors matter? Because at every step of the IVF process from initial consultation to birth, fewer eggs/​embryos/​pregnancies come out than go in. There’s a loss associated with each step, and the factors listed above have a large influence on the size of the loss. Here are some suggestions on how to reduce losses and costs:

Suggestion #1: Reduce uncertainty about how many euploid embryos you will produce

The single most important input into the “gain” equation is the number of mature eggs harvested per retrieval. However, this quantity has a very wide distribution. In conversations an acquaintance of mine had with egg donor clinics, they mentioned that some donors produce as many as 100 eggs per retrieval. A woman in her mid-40s with infertility issues might produce 3.

There are several heuristics you can use to reduce the uncertainty about how many eggs you are likely to harvest during an egg retrieval. Knowing these beforehand can help reduce uncertainty about the exact size of the benefits of polygenic screening.

The easiest heuristic to use is the woman’s age. As a general rule, I wouldn’t do IVF for the purpose of polygenic embryo selection unless the mother is under 38. Beyond age 38, the losses in the IVF funnel are just too steep to justify the pain and expense, unless you plan to use donor eggs.

Another lower-cost but not free thing you can do is assess your likely egg production by undergoing the very first part of IVF; a consultation and ovarian ultrasound. This ultrasound is performed right at the very start of the IVF process and usually costs less than $1000. Ovarian reserve and antral follicle count are strongly correlated with the expected number of mature eggs you or your female partner will produce after hormonal treatments. If you’re willing to do embryo selection in theory if the gain is large enough, this can be a relatively inexpensive way to reduce the uncertainty about the benefits of the procedure.

Antral Follicle Counting with Ultrasound Video
An ultrasound to assess antral follicle count

What is really needed here is a tool that allows one to see the expected gain across a variety of traits given an antral follicle count. The data necessary to do this research is present in the CDC’s NASS database, but I don’t yet have access. I hope to do this for a future research project. Until then, here’s a table showing roughly how your odds vary as a function of antral follicle count.

Suggestion #2: Use my table to pick a clinic

I have spent an embarrassing amount of time on a research project to rank every IVF clinic in the US from best to worst. I compiled this list using data from the CDC’s NASS database, which has information about clinics going back to the mid-90s. I believe I am the first and only person in the world to do this. If you’re curious about a clinic that isn’t on the last, feel free to reach out to me and I can give you the numbers.

A somewhat boring explanation of the research I did (skip this if you just want results)

Why Does The Best Sleep Come In A Boring Lecture? » Science ABC
Warning: you may look like this student after reading about my research methodology

Clinics are ranked according to their cumulative live birth rate per intended egg retrieval among patients using their own eggs (not donor eggs). In simple English, that means we’re looking at what percentage of women who started hormone treatments actually delivered at least one child.

In an ideal world I’d give you clinic rankings based on the number of expected births per retrieval. But without access to intermediate outcome data from NASS, this is impossible. I plan to apply for access eventually, but in the meantime I think live birth rate is probably quite a good proxy, and clinics with very high live birth rates are likely to be able to produce more achievable births than those with lower live birth rates.

I’ve taken care to control for various factors that could confound the analysis. Some clinics attract a larger proportion of younger patients, who have better prospects than older cohorts. Some clinics attract a large proportion of patients solely interested in freezing eggs or embryos for some later treatment. Some clinics have a very small number of cycles each year and can score very well or very poorly depending on the luck of the draw. I’ve controlled for all of these confounders in my analysis.

The one big thing I didn’t control for was the percentage of patients presenting with a given infertility diagnosis. I attempted to to this in an earlier version of the project but had weeks and weeks of nightmares trying to find some defensible way to deal with the large amount of censored data present in spreadsheets. Supposedly the CDC censors these values to protect patient privacy. This justification is obviously nonsense; they still have uncensored values from before 2018 on their website. I emailed to ask if I could apply for some kind of special access as a researcher and was denied.

I tried to work around these missing vaues but eventually I simply gave up. I had to make too many unjustified assumptions to compute clinic rankings, and ranking was highly variable depending on which assumptions I made.

So this final analysis controls only for maternal age, use of patient’s eggs (vs donor eggs), percentage of retrievals conducted with the intention to freeze embryos or eggs (obviously those people aren’t going to have a baby), and some bayesian averaging sprinkled in on top to differentially bring clinics with low retrievals/​year more towards the mean of all clinics.

I plan to actually publish my results in proper academic setting at some point, so this post contains only the headline numbers.

Without further ado, here are the top 25 IVF clinics in the US as of 2020.

The best IVF clinics in the USA

Clinic NameAdjusted Live Birth RateClinic StatePhone Number
Carolinas Fertility Institute0.516123North Carolina(336) 448-9100
The Georgia Center for Reproductive Medicine (no website)0.490339Georgia(912) 352-8588
Reproductive Gynecology & Infertility-Westerville (Columbus location)0.482645Ohio(614) 895-3333
Reproductive Medicine Associates of New Jersey0.458511New Jersey(973) 971-4600
Center for Reproductive Medicine, Advanced Reproductive Technologies0.457538Minnesota(612) 863-5390
Missouri Fertility0.456779Missouri(573) 443-4511
Spring Fertility (San Francisco location)0.446066California(415) 964-5618
CCRM Boston (main center in Chestnut Hill)0.439929Massachusetts(617) 449-9750
SpringCreek Fertility (Dayton location)0.437458Ohio(937) 458-5084
Duke Fertility Center, Duke University Medical Center0.419579North Carolina(919) 572-4673
New Direction Fertility Centers0.413924Arizona(480) 351-8222
Shady Grove Fertility Colorado0.411165Colorado(720) 704-8221
Center for Advanced Reproductive Medicine0.409579Kansas(913) 588-2229
Fertility Center of the Carolinas0.406704South Carolina(864) 455-1600
Fertility Center of San Antonio0.403583Texas(210) 692-0577
Baystate Reproductive Medicine0.403039Massachusetts(413) 794-1950
University of Iowa Hospitals and Clinics, Center for Advanced Reproductive Care0.401587Iowa(319) 356-8483
Carilion Clinic Reproductive Medicine and Fertility0.400715Virginia(540) 985-8078
Advanced Fertility Center of Chicago0.399771Illinois(847) 662-1818
Shady Grove Fertility-Richmond0.394682Virginia(804) 379-9000
Fertility Center of Southern California0.391824California(949) 955-0072
Northern California Fertility Medical Center0.390555California(916) 773-2229
The Nevada Center for Reproductive Medicine0.390344Nevada(775) 828-1200
Center for Advanced Reproductive Services (Farmington Location)0.388567Connecticut(844) 467-3483

For reference, the average adjusted live birth rate for all clinics nationwide was 0.278.

How predictive are an IVF clinic’s past success rates of their future success rates? Here’s a graph showing how well a clinic’s 2019 live birth rates correlated with their 2020 live birth rates after adjusting for the confounders I mentioned above:

It kills me that I can’t show you the correlation between 2017 and 2020 success rates, since that would do a much better job of answering the question “How predictive are 2020 success rates for 2023 outcomes”. But for a bunch of boring reasons I won’t get into, tracking clinic performance over 4 years has been very hard. I’ll try to update this post with the data once I get it.

In the meantime, here’s a graph from AN ENTIRELY DIFFERENT RESEARCH PROJECT I DID AND THEN THREW OUT showing how the performance of the top 50 clinics in the SART database (which also tracks IVF clinic live birth rates) varies over the years.

This chart was created using data from SART and shows that even 3 years out, the live birth rate among the top 50 US clinics was still about 20% above average.

Suggestion #3: Use a good PGT lab

TL;DR Make sure you use Orchid Health or Genomic Prediction for aneuploidy testing. They very likely have lower false positive and false negative rates for embryo aneuploidy when compared with other PGT labs.

It is a little known fact that there is a significant difference in aneuploidy false positive and false negative rates between PGT labs. To the best of my knowledge, there have been no randomized control trials comparing PGT labs. The best we have are independently conducted retrospective cohort analyses.

Unfortunately most of these analyses do not disclose which labs are being analyzed, making them completely useless for paients. However, I happen to know which clinics are which for one particular study submitted to ASRM in 2021. In this study, clinic A is Igenomix, clinic B is Genomic Prediction, and Clinic C is Cooper Genomics.

There’s a lot of sort of random statistics thrown out in this presentation, but I’d like to focus on those most relevant to achievable births: aneuploidy rates, pregnancy rates and miscarriage rates (which they break up into early and late miscarriages in the study)

If you watch the video, you’ll see that “Lab B” is either as good or significantly better than the other two labs in the study across virtually every metric. If you add up the impact of these metrics, here’s what the expected number of achievable births look like for each clinic from the study:

Caption: Note that Genomic Prediction is labelled as “LifeView” on this graph. LifeView is the name of their PGT platform.

I don’t know of any direct comparisons of Orchid Health with other PGT labs, but my guess is their false positive and false negative rates are at least low as those of Genomic Prediction. They use whole genome embryo sequencing, which means they retrieve a gigantic amount of data from each embryo. It’s likely this amount of data reduces testing errors compared to low density NGS sequencing, but I am not certain about this.

Suggestion #4: If possible, freeze embryos instead of eggs

This one is short and sweet: if you have a choice, freeze embryos instead of eggs. At a good storage facility using vitrification, about 90% of eggs will survive cryopreservation. At that same storage facility, 99% of embryos will survive. So if you already know who you want kids with, freeze embryos instead of eggs for an easy 10% boost in achievable births.

Suggestion #5: Freeze eggs or embryos as soon as possible

Since expected gain increases the more achievable births you have, all tips for maximizing it revolve around increasing the number of euploid blastocysts you can produce during IVF. You can pick a good clinic, use a good PGT lab, freeze embryos instead of eggs, and follow good prenatal care guidelines. But at the end of the day, the single biggest input variable into the “gain” equation is the age of the mother.

Here’s a graph from another research project I did showing the relationship between maternal age and number of eggs retrieved at three different clinics

Note that the numbers here are somewhat pessimistic as they are among an infertile population.

You can see there’s more or less a linear decline in expected egg count per retrieval as a function of maternal age. It’s much the same story for expected zygotes and blastocysts; a linear or even exponential decline as a function of maternal age.

If you decide to do polygenic embryo screening, the sooner you start the process the better.

A compendium of other advice

  • When choosing a clinic, there’s several things you need to ensure they are OK with before you agree to become a patient

    • You need to be able to send your embryo’s biopsies to a lab of your choosing

    • They need to be willing to implant an arbitrary embryo of your choice.

  • Make sure that the clinic you choose either has reasonable embryo storage costs or will let you ship your frozen embryos to a facility that does. Some clinics charge up to $1500/​year for embryo storage and will raise the price on you as time goes on. Cheaper clinics charge under $1000/​year for storage (some as little as $500)

  • Read Emily Oster’s excellent book about the things you should and shouldn’t do before, during and after pregnancy. Seriously, Oster is excellent and enjoyable to read. Ex: gardening is dangerous for pregnant women due to soil microbes but <3 drinks per week seems to be completely fine.

  • If you’re a older man (>40) planning to go through this process, freeze your sperm unless you plan on starting the IVF process fairly soon.

The IVF Loss Funnel

Ok, if we put together all the data above, how many live births can you get per egg retrieval?

IVF loss funnel for an average IVF patient

Naively interpreted, the above graph would imply that an average IVF cycle would only yield a single achievable birth on average. This is true! The average IVF patient is a 36 year old woman with significant fertility issues, so it’s not particularly common for such individuals to have more than one birth per egg retrieval. In fact, over half of IVF cycles do not result in live birth. The average is dragged up somewhat by the fact that some women are able to have multiple children from a single egg retrieval.

But what if you choose a better IVF clinic and a better PGT lab than average? What if you and your partner have no known infertility issues and your female partner is younger than the average 36 year-old IVF patient (say 32 for this example).

In that case, we’d expect the graph to look something like this:

IVF funnel for a woman in her early 30s with no infertility issues

Looking better! With a younger mother, a top-tier clinic, and no history of infertility, roughly 5 achievable births per retrieval is possible.

How about in the best case scenario? Assume the following:

  • The mother is at peak fertility (early to mid 20s)

  • Egg retrieval is performed using conventional IVF hormone treatment

  • The father is young-ish (under 40)

  • Neither parent has any infertility issues

  • The parents use a top-tier clinic that is very good at culturing eggs into blastocysts

  • They use a top-tier PGT clinic with a very accurate aneuploidy test

  • The eggs are fertilized immediately and the resulting blastocysts were frozen

In that case, things start to look A LOT better.

IVF funnel for a woman in her early to mid 20s with no infertility issues

The numbers above are based on a combination of sources including SART data on miscarriage and transfer loss rates, podcast episodes, publicly available data from egg donor clinics, and my own knowledge of the IVF industry. I suspect that it may be somewhat conservative for couples without infertility since I have used the infertile transfer and miscarriage rates. But they should nonetheless give a fairly accurate view of the loss funnel.

One last topic I shoud address; how much will all of this cost?

How much does IVF and PGT cost?

IVF is expensive. To do polygenic embryo screening you’ll need to pay for a consultation, ultrasounds, transportation to and from the clinic, IVF services like lab techs, medication, pre-implantation genetic testing, and data analysis services to select for non-disease traits.

I’ve called a couple of dozen top-tier IVF clinics on the phone to ask about prices. I’ll give you a general cost estimate based on those calls:

ServicePrice RangeModal Price
Follicular Ultrasound$150-500$400
Egg retrieval (not including transfer)$6000-20,000$14,000
Embryo Transfer$3000-$6000$4000
PGT-P$1500-5000$1000 + $400/​embryo
Selection for intelligence, height, etc???Best guess: $20,000. But I am highly uncertain here.
Total$9000-$35000$26,500 + intelligence screening

This process is not cheap. If you want to do two egg retrievals and select for intelligence you’re looking at a minimum of $50k and possibly much higher depending on how much the secret startup charges for their services. This is one of the things I hope we can fix in the coming years.


Polygenic embryo selection can currently increase your child’s quality-adjusted life expectancy by 1-4 years, decrease their risk of various chronic diseases by 10-60%, increase their IQ by 2-7.5 points, increase height by up to 2.25 inches, and moderately improve other traits. The exact gain you can expect to get for each of these traits varies depending on the genetic correlation between the traits, the number of embryos you have to choose from, and the strength of the predictor used to select embryos, as well as simple luck. Subsequent children will see a somewhat smaller but still positive benefit, though for every child to benefit you will need at least 3x the number of euploid embryos as you want children.

To get these benefits, you will have to go through IVF and genetic testing of your embryos, which will cost $20k-$60k (and perhaps more depending on whether you want custom testing) and require the female partner to take 2-6 weeks off work.

You can increase the expected gain by choosing a good IVF clinic, choosing a good PGT Lab, freezing embryos instead of eggs, and beginning the process as soon as possible since younger mothers produce significantly more eggs than older mothers.

The IVF process is not particularly pleasant, and is expensive to boot, if you and your partner are willing to put up with the discomfort and expense it you can give your children advantages that are impossible to get any other way.

If you freeze embryos now, the expected gain will increase over time as the genetic predictors used to select embryos improve, and the panel of traits which you can select for will also increase.

There are technologies on the horizon that will allow for significantly greater gains across all heritable traits, making possible gains of 4 or more standard deviations across multiple traits simultaneously.

If AI doesn’t destroy the world first, the next 30 years will likely see the greatest crop of geniuses and athletes the human species has ever produced. If we are wise and select for traits like kindness, altruism, and happiness in addition to health, attractiveness and intelligence, the children born with these benefits may be able to guide the human species through the incredible upheaval and instability we are likely to see over the next century.