01
Overview
Nucleus Embryo allows parents to understand their future child’s health, personality, and physical characteristics based on what matters most to them.
We offer the following analyses:
Appearance and traits: Eye color, height, hair color, male-pattern baldness, severe acne
Body and physical health: BMI, chronic pain, left-handedness, osteoarthritis, rheumatoid arthritis
Women’s health: Endometriosis, polycystic ovarian syndrome
Nutrition and metabolic conditions: Alcohol dependence, celiac disease, type 2 diabetes
Heart health: Coronary artery disease, hypertension
Cancers: Breast cancer, male breast cancer, ovarian cancer, gastric cancer, prostate cancer, colorectal cancer
Neurological and mental health: ADHD, Alzheimer’s disease, anxiety, bipolar disorder, depression, insomnia, migraine, multiple sclerosis, OCD, Parkinson’s disease, schizophrenia, intelligence
Other conditions: Asthma, age-related macular degeneration, restless legs syndrome, seasonal allergies
Hereditary disorders: 900+ hereditary diseases, including rare conditions like cystic fibrosis and hemochromatosis if a whole-genome file is uploaded.
02
How It Works
Patients can request genetic files from their IVF clinic to upload directly to Nucleus for comprehensive analyses that go far beyond viability.
We accept whole-genome files, which read nearly all of an embryo’s DNA, or microarray files from an analysis known as PGT-P, which read only common genetic markers. Because microarray files are limited to common genetic markers, we encourage Nucleus Embryo users with these files to sequence their genomes with Nucleus Family, which includes whole-genome carrier screening encompassing more than 900 hereditary diseases their children could be at risk for.
By combining their Nucleus Family results with their Nucleus Embryo analyses, users with microarray files can ensure their embryos won’t have any rare, pathogenic markers that we analyze for.
We accept data only from embryos with typical chromosome counts based on a preimplantation test known as PGT-A. That means users will receive analyses only on embryos that have already been screened for conditions like Down syndrome, Edward syndrome, and other conditions linked to chromosome number.
03
Classical Mendelian analysis with Nucleus whole-genome DNA analysis
For whole-genome DNA files, Nucleus starts by analyzing embryonic DNA for rare pathogenic and likely pathogenic variants — known as high-effect variants in our reports — in over 900 genes. You can find the growing list of genes we analyze here.
Our team uses a combination of tools to identify consequential genetic variants, including the Ensembl Variant Effect Predictor, public and private databases, and guidelines from the American College of Medical Genetics, or the ACMG1 (1).
Our clinical geneticist ensures accurate, high-quality results by individually verifying each suspected pathogenic or likely pathogenic marker based on standards from the ACMG and the American Board of Bioanalysis.
Pathogenic variants are more likely than common genetic variants to have critical and immediate effects on the health of an individual patient or the future health of an embryo. Pathogenic markers supersede polygenic scores for any given condition in Nucleus reports.
For example, certain pathogenic markers in the LDLR gene have a disproportionately high impact on someone’s risk for heart disease (2). As a result, a polygenic score is less meaningful for assessing risk in such patients.
In these cases, Nucleus reports only the pathogenic marker — the highest-impact predictor of risk — to a patient or prospective parents.
04
Polygenic risk score analysis on microarray and whole-genome files
In the absence of a pathogenic or likely pathogenic variant or in the case of microarray data, we provide polygenic scores, commonly known as PGS, for adults and embryos. We offer these scores, which we refer to as common genetic scores, for the common diseases and traits noted above.
Common genetic scores are calculated by considering the combined impact an embryo’s common genetic variants have on their future disease risk or trait expression. The effect of each common variant is measured from large genome-wide association studies, or GWAS (3). GWAS are large population studies that uncover how common DNA differences, usually single nucleotide polymorphisms, or SNPs, are associated with a particular trait or disease.
The exact source of the GWAS association data differs for each report. They include scores from the Polygenic Score Catalog (4), in-house trained scores, and scores based directly on published results from GWAS studies. This is explained in greater detail in Folkersen et al. (2020) (5), but the guiding principle is to always provide the most recent, well-established, and predictive scores for each disease and trait.
Nucleus calculates polygenic scores by first determining the number of risk alleles an embryo has that are associated with a particular disease or trait. This number is then multiplied by each respective variant’s impact on a phenotype, known as its effect size.
Polygenic models add these effect sizes together, resulting in a single number that measures someone’s genetic disposition for a phenotype based on the combined influence of the common genetic markers in a score.
Different models use varying numbers of SNPs — ranging from just tens of SNPs to millions — with varying effect sizes. As a result, it’s critical to standardize the results to a common scale. Nucleus delivers common genetic scores on a bell curve with an average of 0 and a standard deviation of 1. The distance between someone’s risk and the average risk of a person with their genetic ancestry is known as a Z-score that generally ranges from -4 to 4.
The most predictive polygenic models will come from large, ancestrally diverse datasets containing millions — or even billions — of genomes. Today, however, most GWASs are calculated with data from European populations.
To help account for this gap, Nucleus first standardizes genetic scores across ancestries. This is done based on allele frequencies from the 1000 Genomes Project (6), (7). Secondly, we modulate the polygenic score’s estimated predictive power according to the genetic distance of the user’s ancestry from the ancestry of the training population to ensure non-European populations can still receive rigorous, meaningful genetic analysis results today (8).
As such, common genetic scores from Nucleus indicate the relative increase in genetic risk compared to people of the same ancestry, and the absolute risk numbers take decreased predictivity in non-European populations into account.
05
Improving risk prediction accuracy with non-genetic factors
Non-genetic risk factors can have strong impacts on someone’s risks for common, chronic diseases. Therefore, any risk assessment for a chronic disease that doesn’t account for non-genetic factors is incomplete.
Nucleus is pioneering integrated models of risk assessment that go beyond genetic risk assessment alone to provide the most comprehensive possible look into a patient’s health and well-being (9). We are the only whole-genome assessment that also accounts for age, sex, BMI, blood pressure, cholesterol, and the patient’s smoking history.
Sex is an important known non-genetic variable that can be factored into embryonic risk assessment. For example, a Nucleus report on a female embryo’s risk for coronary artery disease will reflect that an adult woman’s risk for coronary artery disease is significantly less than that of an adult man’s (10).
Most non-genetic risk factors for chronic diseases, however, can’t be known at the embryonic stage. In these cases, Nucleus assumes the most likely risk factors the embryo will develop over the course of their life and uses that to enhance our risk assessment, just as we do in adults.
For example, an average adult has one risk factor, like high cholesterol or blood pressure, for heart disease. Therefore, Nucleus factors at least one risk factor for heart disease into embryonic risk predictions to more accurately capture overall risk.
For age-related conditions, like Alzheimer’s disease, Nucleus predicts an embryo’s risk between the ages of 74 and 85, when the disease is most likely to manifest.
Nucleus combines these non-genetic factors with polygenic scores to estimate absolute scores for a trait or disease. Polygenic analyses illustrate risk relative to adults in the same ancestry-adjusted population. Absolute scores reflect the likelihood an embryo will develop a trait or a disease as either percentage or as the expected difference from the population average for a given set of non-genetic factors. We continually calibrate these overall risk predictions on large public biobank databases.
Combining polygenic prediction with non-genetic factors via absolute scores provides a much more accurate and understandable prediction of phenotypes in embryos and adults.
06
Applying PGS and Mendelian analysis to embryonic prediction
Recent studies have shown the efficacy of polygenic scores and classical Mendelian analysis in embryos, starting by showing that embryonic DNA matches DNA in the newborn child.
In 2022, researchers found that embryonic DNA could be genotyped with over 99% accuracy, meaning that polygenic scores can be reliably constructed at the embryonic level (11). Another preprint in 2024 showed that embryonic sequencing accurately detected rare genetic variants in embryos (12). Taken together, these studies demonstrate that Mendelian and polygenic predictions can be reliably constructed in human embryos.
Researchers have also found that when choosing between two and five viable embryos, selecting embryos with lowest polygenic scores for a complex disease can result in substantial reduction of an embryo’s relative risk for the disease (13).
Moreover, there is strong evidence that polygenic scores can reflect true genetic influences, not artifacts from environment or population stratification. Indeed, studies have shown that polygenic scores can meaningfully distinguish disease risk even among siblings raised in the same environment — supporting their ability to capture real genetic influence.
For example, in sibling pairs where one had a much higher polygenic score than the other, the higher-risk sibling was the one affected by diseases like breast cancer, diabetes, or heart disease in up to 90% of cases (14).
The same was true for trait predictions like height, a strong model that correctly identified the taller sibling in up to 80% of cases.
07
Responsible embryonic polygenic prediction
The nature of most genetic prediction is one of uncertainty. For common diseases and traits, genetics alone will never be able to predict an outcome. Put simply, DNA is not destiny.
Nucleus takes great care in delivering complex, probabilistic results to parents, while respecting their right to access a wide range of genetic analyses.
The limit of genetic influence on any outcome is known as broad-sense heritability, or H². Broad-sense heritability is a percentage that indicates how much of the variation in any given disease or trait can theoretically be explained by DNA (15). Polygenic models today predict a fraction of the heritability of any given phenotype. This fraction — a number that encapsulates the degree of genetic influence that can be captured today — is known as the model’s variability explained.
Every embryo analysis Nucleus offers is clearly labeled a strong, modest, or weak predictor of a genetic outcome based on the model’s variability explained, contextualizing the genetic predictions they receive from Nucleus to inform their selection strategy.
Nucleus further contextualizes polygenic scores by reflecting their variability explained in the absolute scores we provide.
The higher a genetic model’s variability explained, the more accurately, on average, Nucleus can predict a phenotype in an embryo. Moreover, because Nucleus includes non-genetic factors in our risk assessments, we can still provide parents with informative predictions for a phenotype — even when a polygenic model has a lower variability explained.
For example, genetics can explain 80% of the variation in height among the population. Nucleus considers the best known genetic model for height a strong predictor able to predict about half of that variation (16). As a result, Nucleus can predict an embryo’s expected height with more accuracy than any other continuous trait, like IQ or BMI.
Alternatively, the research into the genetics of ovarian cancer is still at an early stage. While scientists estimate the broad-sense heritability of ovarian cancer to be 39%, today’s models capture less than 1% of that risk (17). As a result, the average ovarian cancer risk for an embryo is largely based on the best-known population studies today.
Nucleus also strives to provide users with the most highly predictive models available today — which includes those that are least likely to conflate non-genetic influences for heritable ones.
For example, research has shown that genetic effects on certain traits, such as educational attainment, are weaker among siblings than what studies today capture in unrelated adults, suggesting that certain population studies may conflate environmental and social factors with genetic influence (18). As a result, Nucleus chooses to provide a genetic analysis for intelligence rather than educational attainment.
While Nucleus excludes analyses that our scientists determine are more likely to disproportionately capture non-genetic influences, our offerings will continue to evolve as our understanding of embryonic polygenic prediction improves.
08
Why we made Nucleus Embryo
We provide transparent and clear science in a digestible format that powers informed choices for individual health and that of future generations.
There’s no time in life when choice matters more than the moment parents pursuing IVF choose their embryo, often after a long, emotional journey. In fact, most parents undergo at least three IVF cycles before successfully having a baby, each of which can cost up to $25,000 (19). Other studies show that women still feel like having children is like a lottery (20).
Parents deserve accurate and understandable information at every stage of their IVF journey. Nucleus Embryo provides this in its detailed reports by indicating which diseases and traits can be most accurately predicted at this time, and by providing graphs, charts, and references for each condition or trait.
These kinds of insights are in high demand, and more parents are embracing new technology to have healthy children who thrive. A wide-ranging study of Americans found the majority accepted the use of genetic technology to choose embryos based on health and personality traits (21). Four in 10 parents would use genetic optimization as another tool to increase their child’s chances of going to a top college.
One in 50 people in the U.S. are conceived with IVF. We believe that the advanced analyses Nucleus Embryo can provide will help parents deeply understand their choices when it comes to their future child — sparking a new level of choice in reproductive care, just like IVF first did for hopeful parents decades ago.
References
1
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5. PMID: 25741868.
2
Ison HE, Clarke SL, Knowles JW. Familial Hypercholesterolemia. 2014 Jan 2 [Updated 2022 Jul 7]. In: Adam MP, Feldman J, Mirzaa GM, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2024.
3
Lee SH, van der Werf JH, Hayes BJ, et al. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet. 2008 Oct;4(10):e1000231. doi: 10.1371/journal.pgen.1000231. Epub 2008 Oct 24. PMID: 18949033.
5
Folkersen L, Pain O, Ingason A, et al. Impute.me: An open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front Genet. 2020 Jun 30;11:578. doi: 10.3389/fgene.2020.00578. PMID: 32714365.
6
1000 Genomes Project Consortium; Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74. doi: 10.1038/nature15393. PMID: 26432245.
7
“The International Genome Sample Resource” InternationalGenome.org.
8
Privé F, Aschard H, Carmi S, et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am J Hum Genet. 2022 Jan 6;109(1):12-23. doi: 10.1016/j.ajhg.2021.11.008. Erratum in: Am J Hum Genet. 2022 Feb 3;109(2):373. PMID: 34995502.
9
Wand H, Lambert SA, Tamburro C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021 Mar;591(7849):211-219. doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10. PMID: 33692554; PMCID: PMC8609771.
10
Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006 Feb 14;113(6):791-8. doi: 10.1161/CIRCULATIONAHA.105.548206. Epub 2006 Feb 6. PMID: 16461820.
11
Kumar A, Im K, Banjevic M, et al. Whole-genome risk prediction of common diseases in human preimplantation embryos. Nat Med. 2022 Mar;28(3):513-516. doi: 10.1038/s41591-022-01735-0. Epub 2022 Mar 21. PMID: 35314819.
12
Shenglai Li, Thomas Giardina, Maria Katz, et al. Concordance of whole-genome amplified embryonic DNA with the subsequently born child. medRxiv 2024.01.12.24301086; doi: https://doi.org/10.1101/2024.01.12.24301086.
13
Lencz T, Backenroth D, Granot-Hershkovitz E, et al. Utility of polygenic embryo screening for disease depends on the selection strategy. Elife. 2021 Oct 12;10:e64716. doi: 10.7554/eLife.64716. PMID: 34635206.
14
Lello L, Raben TG, Hsu SDH. Sibling validation of polygenic risk scores and complex trait prediction. Sci Rep. 2020 Aug 6;10(1):13190. doi: 10.1038/s41598-020-69927-7. PMID: 32764582.
15
“Estimating Trait Heritability” Scitable.
16
Chung W, Chen J, Turman C, et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun. 2019 Feb 4;10(1):569. doi: 10.1038/s41467-019-08535-0. PMID: 30718517.
17
Fritsche LG, Patil S, Beesley LJ, et al. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet. 2020 Nov 5;107(5):815-836. doi: 10.1016/j.ajhg.2020.08.025. Epub 2020 Sep 28. PMID: 32991828.
18
Howe LJ, Nivard MG, Morris TT, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022 May;54(5):581-592. doi: 10.1038/s41588-022-01062-7. Epub 2022 May 9. PMID: 35534559.
19
“What to Know About I.V.F.” New York Times.
20
Cooke A, Mills TA, Lavender T. Advanced maternal age: delayed childbearing is rarely a conscious choice a qualitative study of women's views and experiences. Int J Nurs Stud. 2012 Jan;49(1):30-9. doi: 10.1016/j.ijnurstu.2011.07.013. Epub 2011 Aug 12. PMID: 21839454.
21
Meyer MN, Tan T, Benjamin DJ, et al. Public views on polygenic screening of embryos. Science. 2023 Feb 10;379(6632):541-543. doi: 10.1126/science.ade1083. Epub 2023 Feb 9. PMID: 36758092.
01
Overview
Nucleus Embryo allows parents to understand their future child’s health, personality, and physical characteristics based on what matters most to them.
We offer the following analyses:
Appearance and traits: Eye color, height, hair color, male-pattern baldness, severe acne
Body and physical health: BMI, chronic pain, left-handedness, osteoarthritis, rheumatoid arthritis
Women’s health: Endometriosis, polycystic ovarian syndrome
Nutrition and metabolic conditions: Alcohol dependence, celiac disease, type 2 diabetes
Heart health: Coronary artery disease, hypertension
Cancers: Breast cancer, male breast cancer, ovarian cancer, gastric cancer, prostate cancer, colorectal cancer
Neurological and mental health: ADHD, Alzheimer’s disease, anxiety, bipolar disorder, depression, insomnia, migraine, multiple sclerosis, OCD, Parkinson’s disease, schizophrenia, intelligence
Other conditions: Asthma, age-related macular degeneration, restless legs syndrome, seasonal allergies
Hereditary disorders: 900+ hereditary diseases, including rare conditions like cystic fibrosis and hemochromatosis if a whole-genome file is uploaded.
02
How It Works
Patients can request genetic files from their IVF clinic to upload directly to Nucleus for comprehensive analyses that go far beyond viability.
We accept whole-genome files, which read nearly all of an embryo’s DNA, or microarray files from an analysis known as PGT-P, which read only common genetic markers. Because microarray files are limited to common genetic markers, we encourage Nucleus Embryo users with these files to sequence their genomes with Nucleus Family, which includes whole-genome carrier screening encompassing more than 900 hereditary diseases their children could be at risk for.
By combining their Nucleus Family results with their Nucleus Embryo analyses, users with microarray files can ensure their embryos won’t have any rare, pathogenic markers that we analyze for.
We accept data only from embryos with typical chromosome counts based on a preimplantation test known as PGT-A. That means users will receive analyses only on embryos that have already been screened for conditions like Down syndrome, Edward syndrome, and other conditions linked to chromosome number.
03
Classical Mendelian analysis with Nucleus whole-genome DNA analysis
For whole-genome DNA files, Nucleus starts by analyzing embryonic DNA for rare pathogenic and likely pathogenic variants — known as high-effect variants in our reports — in over 900 genes. You can find the growing list of genes we analyze here.
Our team uses a combination of tools to identify consequential genetic variants, including the Ensembl Variant Effect Predictor, public and private databases, and guidelines from the American College of Medical Genetics, or the ACMG1 (1).
Our clinical geneticist ensures accurate, high-quality results by individually verifying each suspected pathogenic or likely pathogenic marker based on standards from the ACMG and the American Board of Bioanalysis.
Pathogenic variants are more likely than common genetic variants to have critical and immediate effects on the health of an individual patient or the future health of an embryo. Pathogenic markers supersede polygenic scores for any given condition in Nucleus reports.
For example, certain pathogenic markers in the LDLR gene have a disproportionately high impact on someone’s risk for heart disease (2). As a result, a polygenic score is less meaningful for assessing risk in such patients.
In these cases, Nucleus reports only the pathogenic marker — the highest-impact predictor of risk — to a patient or prospective parents.
04
Polygenic risk score analysis on microarray and whole-genome files
In the absence of a pathogenic or likely pathogenic variant or in the case of microarray data, we provide polygenic scores, commonly known as PGS, for adults and embryos. We offer these scores, which we refer to as common genetic scores, for the common diseases and traits noted above.
Common genetic scores are calculated by considering the combined impact an embryo’s common genetic variants have on their future disease risk or trait expression. The effect of each common variant is measured from large genome-wide association studies, or GWAS (3). GWAS are large population studies that uncover how common DNA differences, usually single nucleotide polymorphisms, or SNPs, are associated with a particular trait or disease.
The exact source of the GWAS association data differs for each report. They include scores from the Polygenic Score Catalog (4), in-house trained scores, and scores based directly on published results from GWAS studies. This is explained in greater detail in Folkersen et al. (2020) (5), but the guiding principle is to always provide the most recent, well-established, and predictive scores for each disease and trait.
Nucleus calculates polygenic scores by first determining the number of risk alleles an embryo has that are associated with a particular disease or trait. This number is then multiplied by each respective variant’s impact on a phenotype, known as its effect size.
Polygenic models add these effect sizes together, resulting in a single number that measures someone’s genetic disposition for a phenotype based on the combined influence of the common genetic markers in a score.
Different models use varying numbers of SNPs — ranging from just tens of SNPs to millions — with varying effect sizes. As a result, it’s critical to standardize the results to a common scale. Nucleus delivers common genetic scores on a bell curve with an average of 0 and a standard deviation of 1. The distance between someone’s risk and the average risk of a person with their genetic ancestry is known as a Z-score that generally ranges from -4 to 4.
The most predictive polygenic models will come from large, ancestrally diverse datasets containing millions — or even billions — of genomes. Today, however, most GWASs are calculated with data from European populations.
To help account for this gap, Nucleus first standardizes genetic scores across ancestries. This is done based on allele frequencies from the 1000 Genomes Project (6), (7). Secondly, we modulate the polygenic score’s estimated predictive power according to the genetic distance of the user’s ancestry from the ancestry of the training population to ensure non-European populations can still receive rigorous, meaningful genetic analysis results today (8).
As such, common genetic scores from Nucleus indicate the relative increase in genetic risk compared to people of the same ancestry, and the absolute risk numbers take decreased predictivity in non-European populations into account.
05
Improving risk prediction accuracy with non-genetic factors
Non-genetic risk factors can have strong impacts on someone’s risks for common, chronic diseases. Therefore, any risk assessment for a chronic disease that doesn’t account for non-genetic factors is incomplete.
Nucleus is pioneering integrated models of risk assessment that go beyond genetic risk assessment alone to provide the most comprehensive possible look into a patient’s health and well-being (9). We are the only whole-genome assessment that also accounts for age, sex, BMI, blood pressure, cholesterol, and the patient’s smoking history.
Sex is an important known non-genetic variable that can be factored into embryonic risk assessment. For example, a Nucleus report on a female embryo’s risk for coronary artery disease will reflect that an adult woman’s risk for coronary artery disease is significantly less than that of an adult man’s (10).
Most non-genetic risk factors for chronic diseases, however, can’t be known at the embryonic stage. In these cases, Nucleus assumes the most likely risk factors the embryo will develop over the course of their life and uses that to enhance our risk assessment, just as we do in adults.
For example, an average adult has one risk factor, like high cholesterol or blood pressure, for heart disease. Therefore, Nucleus factors at least one risk factor for heart disease into embryonic risk predictions to more accurately capture overall risk.
For age-related conditions, like Alzheimer’s disease, Nucleus predicts an embryo’s risk between the ages of 74 and 85, when the disease is most likely to manifest.
Nucleus combines these non-genetic factors with polygenic scores to estimate absolute scores for a trait or disease. Polygenic analyses illustrate risk relative to adults in the same ancestry-adjusted population. Absolute scores reflect the likelihood an embryo will develop a trait or a disease as either percentage or as the expected difference from the population average for a given set of non-genetic factors. We continually calibrate these overall risk predictions on large public biobank databases.
Combining polygenic prediction with non-genetic factors via absolute scores provides a much more accurate and understandable prediction of phenotypes in embryos and adults.
06
Applying PGS and Mendelian analysis to embryonic prediction
Recent studies have shown the efficacy of polygenic scores and classical Mendelian analysis in embryos, starting by showing that embryonic DNA matches DNA in the newborn child.
In 2022, researchers found that embryonic DNA could be genotyped with over 99% accuracy, meaning that polygenic scores can be reliably constructed at the embryonic level (11). Another preprint in 2024 showed that embryonic sequencing accurately detected rare genetic variants in embryos (12). Taken together, these studies demonstrate that Mendelian and polygenic predictions can be reliably constructed in human embryos.
Researchers have also found that when choosing between two and five viable embryos, selecting embryos with lowest polygenic scores for a complex disease can result in substantial reduction of an embryo’s relative risk for the disease (13).
Moreover, there is strong evidence that polygenic scores can reflect true genetic influences, not artifacts from environment or population stratification. Indeed, studies have shown that polygenic scores can meaningfully distinguish disease risk even among siblings raised in the same environment — supporting their ability to capture real genetic influence.
For example, in sibling pairs where one had a much higher polygenic score than the other, the higher-risk sibling was the one affected by diseases like breast cancer, diabetes, or heart disease in up to 90% of cases (14).
The same was true for trait predictions like height, a strong model that correctly identified the taller sibling in up to 80% of cases.
07
Responsible embryonic polygenic prediction
The nature of most genetic prediction is one of uncertainty. For common diseases and traits, genetics alone will never be able to predict an outcome. Put simply, DNA is not destiny.
Nucleus takes great care in delivering complex, probabilistic results to parents, while respecting their right to access a wide range of genetic analyses.
The limit of genetic influence on any outcome is known as broad-sense heritability, or H². Broad-sense heritability is a percentage that indicates how much of the variation in any given disease or trait can theoretically be explained by DNA (15). Polygenic models today predict a fraction of the heritability of any given phenotype. This fraction — a number that encapsulates the degree of genetic influence that can be captured today — is known as the model’s variability explained.
Every embryo analysis Nucleus offers is clearly labeled a strong, modest, or weak predictor of a genetic outcome based on the model’s variability explained, contextualizing the genetic predictions they receive from Nucleus to inform their selection strategy.
Nucleus further contextualizes polygenic scores by reflecting their variability explained in the absolute scores we provide.
The higher a genetic model’s variability explained, the more accurately, on average, Nucleus can predict a phenotype in an embryo. Moreover, because Nucleus includes non-genetic factors in our risk assessments, we can still provide parents with informative predictions for a phenotype — even when a polygenic model has a lower variability explained.
For example, genetics can explain 80% of the variation in height among the population. Nucleus considers the best known genetic model for height a strong predictor able to predict about half of that variation (16). As a result, Nucleus can predict an embryo’s expected height with more accuracy than any other continuous trait, like IQ or BMI.
Alternatively, the research into the genetics of ovarian cancer is still at an early stage. While scientists estimate the broad-sense heritability of ovarian cancer to be 39%, today’s models capture less than 1% of that risk (17). As a result, the average ovarian cancer risk for an embryo is largely based on the best-known population studies today.
Nucleus also strives to provide users with the most highly predictive models available today — which includes those that are least likely to conflate non-genetic influences for heritable ones.
For example, research has shown that genetic effects on certain traits, such as educational attainment, are weaker among siblings than what studies today capture in unrelated adults, suggesting that certain population studies may conflate environmental and social factors with genetic influence (18). As a result, Nucleus chooses to provide a genetic analysis for intelligence rather than educational attainment.
While Nucleus excludes analyses that our scientists determine are more likely to disproportionately capture non-genetic influences, our offerings will continue to evolve as our understanding of embryonic polygenic prediction improves.
08
Why we made Nucleus Embryo
We provide transparent and clear science in a digestible format that powers informed choices for individual health and that of future generations.
There’s no time in life when choice matters more than the moment parents pursuing IVF choose their embryo, often after a long, emotional journey. In fact, most parents undergo at least three IVF cycles before successfully having a baby, each of which can cost up to $25,000 (19). Other studies show that women still feel like having children is like a lottery (20).
Parents deserve accurate and understandable information at every stage of their IVF journey. Nucleus Embryo provides this in its detailed reports by indicating which diseases and traits can be most accurately predicted at this time, and by providing graphs, charts, and references for each condition or trait.
These kinds of insights are in high demand, and more parents are embracing new technology to have healthy children who thrive. A wide-ranging study of Americans found the majority accepted the use of genetic technology to choose embryos based on health and personality traits (21). Four in 10 parents would use genetic optimization as another tool to increase their child’s chances of going to a top college.
One in 50 people in the U.S. are conceived with IVF. We believe that the advanced analyses Nucleus Embryo can provide will help parents deeply understand their choices when it comes to their future child — sparking a new level of choice in reproductive care, just like IVF first did for hopeful parents decades ago.
References
1
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5. PMID: 25741868.
2
Ison HE, Clarke SL, Knowles JW. Familial Hypercholesterolemia. 2014 Jan 2 [Updated 2022 Jul 7]. In: Adam MP, Feldman J, Mirzaa GM, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2024.
3
Lee SH, van der Werf JH, Hayes BJ, et al. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet. 2008 Oct;4(10):e1000231. doi: 10.1371/journal.pgen.1000231. Epub 2008 Oct 24. PMID: 18949033.
5
Folkersen L, Pain O, Ingason A, et al. Impute.me: An open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front Genet. 2020 Jun 30;11:578. doi: 10.3389/fgene.2020.00578. PMID: 32714365.
6
1000 Genomes Project Consortium; Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74. doi: 10.1038/nature15393. PMID: 26432245.
7
“The International Genome Sample Resource” InternationalGenome.org.
8
Privé F, Aschard H, Carmi S, et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am J Hum Genet. 2022 Jan 6;109(1):12-23. doi: 10.1016/j.ajhg.2021.11.008. Erratum in: Am J Hum Genet. 2022 Feb 3;109(2):373. PMID: 34995502.
9
Wand H, Lambert SA, Tamburro C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021 Mar;591(7849):211-219. doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10. PMID: 33692554; PMCID: PMC8609771.
10
Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006 Feb 14;113(6):791-8. doi: 10.1161/CIRCULATIONAHA.105.548206. Epub 2006 Feb 6. PMID: 16461820.
11
Kumar A, Im K, Banjevic M, et al. Whole-genome risk prediction of common diseases in human preimplantation embryos. Nat Med. 2022 Mar;28(3):513-516. doi: 10.1038/s41591-022-01735-0. Epub 2022 Mar 21. PMID: 35314819.
12
Shenglai Li, Thomas Giardina, Maria Katz, et al. Concordance of whole-genome amplified embryonic DNA with the subsequently born child. medRxiv 2024.01.12.24301086; doi: https://doi.org/10.1101/2024.01.12.24301086.
13
Lencz T, Backenroth D, Granot-Hershkovitz E, et al. Utility of polygenic embryo screening for disease depends on the selection strategy. Elife. 2021 Oct 12;10:e64716. doi: 10.7554/eLife.64716. PMID: 34635206.
14
Lello L, Raben TG, Hsu SDH. Sibling validation of polygenic risk scores and complex trait prediction. Sci Rep. 2020 Aug 6;10(1):13190. doi: 10.1038/s41598-020-69927-7. PMID: 32764582.
15
“Estimating Trait Heritability” Scitable.
16
Chung W, Chen J, Turman C, et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun. 2019 Feb 4;10(1):569. doi: 10.1038/s41467-019-08535-0. PMID: 30718517.
17
Fritsche LG, Patil S, Beesley LJ, et al. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet. 2020 Nov 5;107(5):815-836. doi: 10.1016/j.ajhg.2020.08.025. Epub 2020 Sep 28. PMID: 32991828.
18
Howe LJ, Nivard MG, Morris TT, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022 May;54(5):581-592. doi: 10.1038/s41588-022-01062-7. Epub 2022 May 9. PMID: 35534559.
19
“What to Know About I.V.F.” New York Times.
20
Cooke A, Mills TA, Lavender T. Advanced maternal age: delayed childbearing is rarely a conscious choice a qualitative study of women's views and experiences. Int J Nurs Stud. 2012 Jan;49(1):30-9. doi: 10.1016/j.ijnurstu.2011.07.013. Epub 2011 Aug 12. PMID: 21839454.
21
Meyer MN, Tan T, Benjamin DJ, et al. Public views on polygenic screening of embryos. Science. 2023 Feb 10;379(6632):541-543. doi: 10.1126/science.ade1083. Epub 2023 Feb 9. PMID: 36758092.
01
Overview
Nucleus Embryo allows parents to understand their future child’s health, personality, and physical characteristics based on what matters most to them.
We offer the following analyses:
Appearance and traits: Eye color, height, hair color, male-pattern baldness, severe acne
Body and physical health: BMI, chronic pain, left-handedness, osteoarthritis, rheumatoid arthritis
Women’s health: Endometriosis, polycystic ovarian syndrome
Nutrition and metabolic conditions: Alcohol dependence, celiac disease, type 2 diabetes
Heart health: Coronary artery disease, hypertension
Cancers: Breast cancer, male breast cancer, ovarian cancer, gastric cancer, prostate cancer, colorectal cancer
Neurological and mental health: ADHD, Alzheimer’s disease, anxiety, bipolar disorder, depression, insomnia, migraine, multiple sclerosis, OCD, Parkinson’s disease, schizophrenia, intelligence
Other conditions: Asthma, age-related macular degeneration, restless legs syndrome, seasonal allergies
Hereditary disorders: 900+ hereditary diseases, including rare conditions like cystic fibrosis and hemochromatosis if a whole-genome file is uploaded.
02
How It Works
Patients can request genetic files from their IVF clinic to upload directly to Nucleus for comprehensive analyses that go far beyond viability.
We accept whole-genome files, which read nearly all of an embryo’s DNA, or microarray files from an analysis known as PGT-P, which read only common genetic markers. Because microarray files are limited to common genetic markers, we encourage Nucleus Embryo users with these files to sequence their genomes with Nucleus Family, which includes whole-genome carrier screening encompassing more than 900 hereditary diseases their children could be at risk for.
By combining their Nucleus Family results with their Nucleus Embryo analyses, users with microarray files can ensure their embryos won’t have any rare, pathogenic markers that we analyze for.
We accept data only from embryos with typical chromosome counts based on a preimplantation test known as PGT-A. That means users will receive analyses only on embryos that have already been screened for conditions like Down syndrome, Edward syndrome, and other conditions linked to chromosome number.
03
Classical Mendelian analysis with Nucleus whole-genome DNA analysis
For whole-genome DNA files, Nucleus starts by analyzing embryonic DNA for rare pathogenic and likely pathogenic variants — known as high-effect variants in our reports — in over 900 genes. You can find the growing list of genes we analyze here.
Our team uses a combination of tools to identify consequential genetic variants, including the Ensembl Variant Effect Predictor, public and private databases, and guidelines from the American College of Medical Genetics, or the ACMG1 (1).
Our clinical geneticist ensures accurate, high-quality results by individually verifying each suspected pathogenic or likely pathogenic marker based on standards from the ACMG and the American Board of Bioanalysis.
Pathogenic variants are more likely than common genetic variants to have critical and immediate effects on the health of an individual patient or the future health of an embryo. Pathogenic markers supersede polygenic scores for any given condition in Nucleus reports.
For example, certain pathogenic markers in the LDLR gene have a disproportionately high impact on someone’s risk for heart disease (2). As a result, a polygenic score is less meaningful for assessing risk in such patients.
In these cases, Nucleus reports only the pathogenic marker — the highest-impact predictor of risk — to a patient or prospective parents.
04
Polygenic risk score analysis on microarray and whole-genome files
In the absence of a pathogenic or likely pathogenic variant or in the case of microarray data, we provide polygenic scores, commonly known as PGS, for adults and embryos. We offer these scores, which we refer to as common genetic scores, for the common diseases and traits noted above.
Common genetic scores are calculated by considering the combined impact an embryo’s common genetic variants have on their future disease risk or trait expression. The effect of each common variant is measured from large genome-wide association studies, or GWAS (3). GWAS are large population studies that uncover how common DNA differences, usually single nucleotide polymorphisms, or SNPs, are associated with a particular trait or disease.
The exact source of the GWAS association data differs for each report. They include scores from the Polygenic Score Catalog (4), in-house trained scores, and scores based directly on published results from GWAS studies. This is explained in greater detail in Folkersen et al. (2020) (5), but the guiding principle is to always provide the most recent, well-established, and predictive scores for each disease and trait.
Nucleus calculates polygenic scores by first determining the number of risk alleles an embryo has that are associated with a particular disease or trait. This number is then multiplied by each respective variant’s impact on a phenotype, known as its effect size.
Polygenic models add these effect sizes together, resulting in a single number that measures someone’s genetic disposition for a phenotype based on the combined influence of the common genetic markers in a score.
Different models use varying numbers of SNPs — ranging from just tens of SNPs to millions — with varying effect sizes. As a result, it’s critical to standardize the results to a common scale. Nucleus delivers common genetic scores on a bell curve with an average of 0 and a standard deviation of 1. The distance between someone’s risk and the average risk of a person with their genetic ancestry is known as a Z-score that generally ranges from -4 to 4.
The most predictive polygenic models will come from large, ancestrally diverse datasets containing millions — or even billions — of genomes. Today, however, most GWASs are calculated with data from European populations.
To help account for this gap, Nucleus first standardizes genetic scores across ancestries. This is done based on allele frequencies from the 1000 Genomes Project (6), (7). Secondly, we modulate the polygenic score’s estimated predictive power according to the genetic distance of the user’s ancestry from the ancestry of the training population to ensure non-European populations can still receive rigorous, meaningful genetic analysis results today (8).
As such, common genetic scores from Nucleus indicate the relative increase in genetic risk compared to people of the same ancestry, and the absolute risk numbers take decreased predictivity in non-European populations into account.
05
Improving risk prediction accuracy with non-genetic factors
Non-genetic risk factors can have strong impacts on someone’s risks for common, chronic diseases. Therefore, any risk assessment for a chronic disease that doesn’t account for non-genetic factors is incomplete.
Nucleus is pioneering integrated models of risk assessment that go beyond genetic risk assessment alone to provide the most comprehensive possible look into a patient’s health and well-being (9). We are the only whole-genome assessment that also accounts for age, sex, BMI, blood pressure, cholesterol, and the patient’s smoking history.
Sex is an important known non-genetic variable that can be factored into embryonic risk assessment. For example, a Nucleus report on a female embryo’s risk for coronary artery disease will reflect that an adult woman’s risk for coronary artery disease is significantly less than that of an adult man’s (10).
Most non-genetic risk factors for chronic diseases, however, can’t be known at the embryonic stage. In these cases, Nucleus assumes the most likely risk factors the embryo will develop over the course of their life and uses that to enhance our risk assessment, just as we do in adults.
For example, an average adult has one risk factor, like high cholesterol or blood pressure, for heart disease. Therefore, Nucleus factors at least one risk factor for heart disease into embryonic risk predictions to more accurately capture overall risk.
For age-related conditions, like Alzheimer’s disease, Nucleus predicts an embryo’s risk between the ages of 74 and 85, when the disease is most likely to manifest.
Nucleus combines these non-genetic factors with polygenic scores to estimate absolute scores for a trait or disease. Polygenic analyses illustrate risk relative to adults in the same ancestry-adjusted population. Absolute scores reflect the likelihood an embryo will develop a trait or a disease as either percentage or as the expected difference from the population average for a given set of non-genetic factors. We continually calibrate these overall risk predictions on large public biobank databases.
Combining polygenic prediction with non-genetic factors via absolute scores provides a much more accurate and understandable prediction of phenotypes in embryos and adults.
06
Applying PGS and Mendelian analysis to embryonic prediction
Recent studies have shown the efficacy of polygenic scores and classical Mendelian analysis in embryos, starting by showing that embryonic DNA matches DNA in the newborn child.
In 2022, researchers found that embryonic DNA could be genotyped with over 99% accuracy, meaning that polygenic scores can be reliably constructed at the embryonic level (11). Another preprint in 2024 showed that embryonic sequencing accurately detected rare genetic variants in embryos (12). Taken together, these studies demonstrate that Mendelian and polygenic predictions can be reliably constructed in human embryos.
Researchers have also found that when choosing between two and five viable embryos, selecting embryos with lowest polygenic scores for a complex disease can result in substantial reduction of an embryo’s relative risk for the disease (13).
Moreover, there is strong evidence that polygenic scores can reflect true genetic influences, not artifacts from environment or population stratification. Indeed, studies have shown that polygenic scores can meaningfully distinguish disease risk even among siblings raised in the same environment — supporting their ability to capture real genetic influence.
For example, in sibling pairs where one had a much higher polygenic score than the other, the higher-risk sibling was the one affected by diseases like breast cancer, diabetes, or heart disease in up to 90% of cases (14).
The same was true for trait predictions like height, a strong model that correctly identified the taller sibling in up to 80% of cases.
07
Responsible embryonic polygenic prediction
The nature of most genetic prediction is one of uncertainty. For common diseases and traits, genetics alone will never be able to predict an outcome. Put simply, DNA is not destiny.
Nucleus takes great care in delivering complex, probabilistic results to parents, while respecting their right to access a wide range of genetic analyses.
The limit of genetic influence on any outcome is known as broad-sense heritability, or H². Broad-sense heritability is a percentage that indicates how much of the variation in any given disease or trait can theoretically be explained by DNA (15). Polygenic models today predict a fraction of the heritability of any given phenotype. This fraction — a number that encapsulates the degree of genetic influence that can be captured today — is known as the model’s variability explained.
Every embryo analysis Nucleus offers is clearly labeled a strong, modest, or weak predictor of a genetic outcome based on the model’s variability explained, contextualizing the genetic predictions they receive from Nucleus to inform their selection strategy.
Nucleus further contextualizes polygenic scores by reflecting their variability explained in the absolute scores we provide.
The higher a genetic model’s variability explained, the more accurately, on average, Nucleus can predict a phenotype in an embryo. Moreover, because Nucleus includes non-genetic factors in our risk assessments, we can still provide parents with informative predictions for a phenotype — even when a polygenic model has a lower variability explained.
For example, genetics can explain 80% of the variation in height among the population. Nucleus considers the best known genetic model for height a strong predictor able to predict about half of that variation (16). As a result, Nucleus can predict an embryo’s expected height with more accuracy than any other continuous trait, like IQ or BMI.
Alternatively, the research into the genetics of ovarian cancer is still at an early stage. While scientists estimate the broad-sense heritability of ovarian cancer to be 39%, today’s models capture less than 1% of that risk (17). As a result, the average ovarian cancer risk for an embryo is largely based on the best-known population studies today.
Nucleus also strives to provide users with the most highly predictive models available today — which includes those that are least likely to conflate non-genetic influences for heritable ones.
For example, research has shown that genetic effects on certain traits, such as educational attainment, are weaker among siblings than what studies today capture in unrelated adults, suggesting that certain population studies may conflate environmental and social factors with genetic influence (18). As a result, Nucleus chooses to provide a genetic analysis for intelligence rather than educational attainment.
While Nucleus excludes analyses that our scientists determine are more likely to disproportionately capture non-genetic influences, our offerings will continue to evolve as our understanding of embryonic polygenic prediction improves.
08
Why we made Nucleus Embryo
We provide transparent and clear science in a digestible format that powers informed choices for individual health and that of future generations.
There’s no time in life when choice matters more than the moment parents pursuing IVF choose their embryo, often after a long, emotional journey. In fact, most parents undergo at least three IVF cycles before successfully having a baby, each of which can cost up to $25,000 (19). Other studies show that women still feel like having children is like a lottery (20).
Parents deserve accurate and understandable information at every stage of their IVF journey. Nucleus Embryo provides this in its detailed reports by indicating which diseases and traits can be most accurately predicted at this time, and by providing graphs, charts, and references for each condition or trait.
These kinds of insights are in high demand, and more parents are embracing new technology to have healthy children who thrive. A wide-ranging study of Americans found the majority accepted the use of genetic technology to choose embryos based on health and personality traits (21). Four in 10 parents would use genetic optimization as another tool to increase their child’s chances of going to a top college.
One in 50 people in the U.S. are conceived with IVF. We believe that the advanced analyses Nucleus Embryo can provide will help parents deeply understand their choices when it comes to their future child — sparking a new level of choice in reproductive care, just like IVF first did for hopeful parents decades ago.
References
1
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5. PMID: 25741868.
2
Ison HE, Clarke SL, Knowles JW. Familial Hypercholesterolemia. 2014 Jan 2 [Updated 2022 Jul 7]. In: Adam MP, Feldman J, Mirzaa GM, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2024.
3
Lee SH, van der Werf JH, Hayes BJ, et al. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet. 2008 Oct;4(10):e1000231. doi: 10.1371/journal.pgen.1000231. Epub 2008 Oct 24. PMID: 18949033.
5
Folkersen L, Pain O, Ingason A, et al. Impute.me: An open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front Genet. 2020 Jun 30;11:578. doi: 10.3389/fgene.2020.00578. PMID: 32714365.
6
1000 Genomes Project Consortium; Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74. doi: 10.1038/nature15393. PMID: 26432245.
7
“The International Genome Sample Resource” InternationalGenome.org.
8
Privé F, Aschard H, Carmi S, et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am J Hum Genet. 2022 Jan 6;109(1):12-23. doi: 10.1016/j.ajhg.2021.11.008. Erratum in: Am J Hum Genet. 2022 Feb 3;109(2):373. PMID: 34995502.
9
Wand H, Lambert SA, Tamburro C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021 Mar;591(7849):211-219. doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10. PMID: 33692554; PMCID: PMC8609771.
10
Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006 Feb 14;113(6):791-8. doi: 10.1161/CIRCULATIONAHA.105.548206. Epub 2006 Feb 6. PMID: 16461820.
11
Kumar A, Im K, Banjevic M, et al. Whole-genome risk prediction of common diseases in human preimplantation embryos. Nat Med. 2022 Mar;28(3):513-516. doi: 10.1038/s41591-022-01735-0. Epub 2022 Mar 21. PMID: 35314819.
12
Shenglai Li, Thomas Giardina, Maria Katz, et al. Concordance of whole-genome amplified embryonic DNA with the subsequently born child. medRxiv 2024.01.12.24301086; doi: https://doi.org/10.1101/2024.01.12.24301086.
13
Lencz T, Backenroth D, Granot-Hershkovitz E, et al. Utility of polygenic embryo screening for disease depends on the selection strategy. Elife. 2021 Oct 12;10:e64716. doi: 10.7554/eLife.64716. PMID: 34635206.
14
Lello L, Raben TG, Hsu SDH. Sibling validation of polygenic risk scores and complex trait prediction. Sci Rep. 2020 Aug 6;10(1):13190. doi: 10.1038/s41598-020-69927-7. PMID: 32764582.
15
“Estimating Trait Heritability” Scitable.
16
Chung W, Chen J, Turman C, et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun. 2019 Feb 4;10(1):569. doi: 10.1038/s41467-019-08535-0. PMID: 30718517.
17
Fritsche LG, Patil S, Beesley LJ, et al. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet. 2020 Nov 5;107(5):815-836. doi: 10.1016/j.ajhg.2020.08.025. Epub 2020 Sep 28. PMID: 32991828.
18
Howe LJ, Nivard MG, Morris TT, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022 May;54(5):581-592. doi: 10.1038/s41588-022-01062-7. Epub 2022 May 9. PMID: 35534559.
19
“What to Know About I.V.F.” New York Times.
20
Cooke A, Mills TA, Lavender T. Advanced maternal age: delayed childbearing is rarely a conscious choice a qualitative study of women's views and experiences. Int J Nurs Stud. 2012 Jan;49(1):30-9. doi: 10.1016/j.ijnurstu.2011.07.013. Epub 2011 Aug 12. PMID: 21839454.
21
Meyer MN, Tan T, Benjamin DJ, et al. Public views on polygenic screening of embryos. Science. 2023 Feb 10;379(6632):541-543. doi: 10.1126/science.ade1083. Epub 2023 Feb 9. PMID: 36758092.
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© 2025 Nucleus Genomics, Inc.
© 2025 Nucleus Genomics, Inc.
© 2025 Nucleus Genomics, Inc.