The Science
Behind Our Results

Our mission is to give couples the most advanced tools available to have the healthiest child possible.

To achieve this, our scientific team continually reviews the latest research in genomics and follows which studies hit our quality bar before translating into meaningful genomic insights for you and your future children.

The highest standards


It’s not enough for results to be reported by a single author or institution. We look for reproducible case-control studies before putting any results into our products.

Large N

No results on our reports have a population size under 10,000 individuals

Clinical lab

Your results are processed in a CLIA/CAP certified laboratory, the same standards trusted by the largest academic medical centers and physicians.

Expert review

Each report is reviewed by a genetics expert before delivery to you.

Measuring genetic susceptibility using billions of data points

Advances in computational and molecular biology allow us to characterize the genetic basis of common conditions such as heart disease or cancers more accurately than ever before. Two decades after sequencing the first human genome, we finally have enough data available on hundreds of thousands of sequenced individuals, to quantify genetic susceptibility to disease.

We use multiple markers across the genome known as single nucleotide polymorphisms (SNPs) to calculate you and your future child’s chances of developing several common conditions. While individually, each of these SNPs has a small impact on disease risk, their combined effect, calculated as a genetic risk score, is larger than any single gene, or family history alone.

The specific SNPs used in the genetic risk scores for each condition were curated by observing which SNPs are found more often in individuals with a condition compared to individuals without the condition. SNPs included in our risk calculations have been consistently demonstrated and validated through aggregated data from hundreds of thousands of individuals.

Our approach allows us to provide genetic predisposition results to common conditions that can happen to anyone - not just rare genetic diseases found in a small group of people.

Statistical modeling of your potential child’s complex genomic inheritance patterns

When considering more than one gene at a time, estimating the chance of a genetic predisposition to a condition being passed onto the next generation gets complicated. We run many simulations of the ways you and your partners’ DNA (and associated genetic risks) can combine. This gives us a sense of which disease risks might be elevated in your potential child or children. This sophisticated modeling technique allows us to model complex inheritance patterns previously not possible due to its large statistical computing requirements.

Results are reported as a range because each person’s genetic risk score is personalized and no two family members - siblings included - may have the same exact genetic risk to a common condition.

Central to the simulation is modeling when matching regions on matching chromosomes break and reconnect to the other chromosome. This is known as recombination, which happens during the formation of sperm and egg cells to introduce additional genetic diversity.

Our simulation model is based on key papers demonstrating it is possible to model the potential genomes that you and your partner are likely to create:

Genetic crossovers are predicted accurately by the computed human recombination map

PLoS Genetics, 6(1), e1000831.

Previous studies have found that population-averaged hotspot maps can accurately predict where recombination events are going to happen in an individual’s chromosome pairs.

Khil, P. P., & Camerini-Otero, R. D. (2010).

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Genetic analysis of variation in human meiotic recombination

PLoS Genetics, 5(9), e1000648.

The number of recombinations on each chromosome pair vary between males and females. We account for these sex-specific differences in our statistical model.

Chowdhury, R., et al. (2009).

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Characterization of human crossover interference

American Journal of Human Genetics, 66(6), 1911–1926.

Recombination happens more often at the ends of the chromosomes and less often when there has already been a recombination event nearby.

Broman, K. W., & Weber, J. L. (2000).

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Advanced embryo screening for families going through IVF

In addition to statistical modeling and forecasting in our preconception report, if you are considering in vitro fertilization (IVF), we use whole genome next-generation sequencing to assess the specific genetic risks for each embryo. This allows you to potentially reduce your child’s genetic risks during in vitro fertilization (IVF). We apply the same rigorous science used in analyzing your DNA to embryo biopsy samples at our CLIA-certified laboratory. The embryo report allows you to make an informed decision about which embryo to consider prioritizing for transfer.

Meaningful results, backed by science

Review some landmark studies we used to help generate our genetic risk scores:

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Nature Genetics, 50(9):1219-1224.

A seminal study with data from over 400,000 individuals suggests that based on genetics, 20% of the general population have over a three-fold increased risk for at least one of the five conditions: coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer.

These genetic risk scores identified more individuals with increased risk of disease previously missed by conventional risk assessment like family history, routine blood tests, or single gene testing.  

Khera A.V., et al. (2018).

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Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

Nature Genetics, 50, 928-93.

The validity of genetic risk scores for prostate cancer has been demonstrated in meta-analysis of more than 140,000 men. Those with an elevated genetic risk can have up to a 5-fold increased risk in developing prostate cancer compared to the average.

Schumacher, F.R., et al., (2018).

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Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Nature Communications, 10(1), 5819.

Recent approaches combining multiple genetic risk scores into one meta-score substantially improves risk prediction for conditions like ischemic stroke. This study used UK Biobank data to generate a cross-validated genetic risk score for ischemic stroke that is similar to or more predictive than established non-genetic risk factors alone.

Abraham, G., et al., (2019).

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Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

Diabetes Care, 42(2), 200–207.

Genetic prediction of type 1 diabetes has improved considerably from human leukocyte antigen (HLA) genotyping. Individuals in the 90th percentile of genetic risk captures 77% of type 1 diabetes cases. Typically an early-childhood onset condition, susceptibility to type 1 diabetes can be predicted through genetic risk scoring even before pregnancy.

Sharp, S. A., et al., (2019).

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Biological insights from 108 schizophrenia-associated genetic loci

Nature, 511(7510), 421–427

In the largest molecular genetic study of neuropsychiatric disorders, the Psychiatric Genomics Consortium combined all available schizophrenia cases into a single systematic analysis of over 150,000 individuals. They found novel increased risk SNPs, many of which are linked to genes expressed in the brain and involved with the immune system.

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014).

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Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

PLoS Medicine, 14(3), e1002258.

Using data from over 70,000 individuals, genetic risk scoring for Alzheimer’s disease can now predict both the chance of developing Alzheimer’s and the estimated age of onset. Individuals with elevated genetic risk are more likely to develop Alzheimer’s at an earlier age, even among individuals with a normal ApoE genotype (E3/E3).

Desikan, R. S., et al., (2017).

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