Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations.

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients. The increasing sizes of ultra-rare, "N-of-1" disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We apply existing and introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We make our gene-level findings and variant-level information across the cohort available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Unique Challenges of NIPT for Sex Chromosome Aneuploidy.

Noninvasive prenatal testing (NIPT) for the sex chromosome aneuploidies (45,X, 47,XXY, 47,XXX, and 47,XYY) differs significantly from that for the autosomal aneuploidies (trisomy 13, 18, and 21). As a group, sex chromosome aneuploidies occur more commonly (1/400) than any one isolated autosomal aneuploidy, the phenotypic variation is greater, the role of mosaicism more challenging, and the positive predictive value of a high-risk NIPT result is substantially lower. These considerations should be identified during pretest counseling, the inclusion of sex chromosome testing offered separately, and the differences from autosomal aneuploidy NIPT clearly delineated.

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

Validation of claims-based algorithms to identify non-live birth outcomes.

PURPOSE: Perinatal epidemiology studies using healthcare utilization databases are often restricted to live births, largely due to the lack of established algorithms to identify non-live births. The study objective was to develop and validate claims-based algorithms for the ascertainment of non-live births. METHODS: Using the Mass General Brigham Research Patient Data Registry 2000-2014, we assembled a cohort of women enrolled in Medicaid with a non-live birth. Based on ≥1 inpatient or ≥2 outpatient diagnosis/procedure codes, we identified and randomly sampled 100 potential stillbirth, spontaneous abortion, and termination cases each. For the secondary definitions, we excluded cases with codes for other pregnancy outcomes within ±5 days of the outcome of interest and relaxed the definitions for spontaneous abortion and termination by allowing cases with one outpatient diagnosis only. Cases were adjudicated based on medical chart review. We estimated the positive predictive value (PPV) for each outcome. RESULTS: The PPV was 71.0% (95% CI, 61.1-79.6) for stillbirth; 79.0% (69.7-86.5) for spontaneous abortion, and 93.0% (86.1-97.1) for termination. When excluding cases with adjacent codes for other pregnancy outcomes and further relaxing the definition, the PPV increased to 80.6% (69.5-88.9) for stillbirth, 86.6% (80.5-91.3) for spontaneous abortion and 94.9% (91.1-97.4) for termination. The PPV for the composite outcome using the relaxed definition was 94.4% (92.3-96.1). CONCLUSIONS: Our findings suggest non-live birth outcomes can be identified in a valid manner in epidemiological studies based on healthcare utilization databases.

Prenatal diagnosis of sex chromosome aneuploidy-What do we tell the prospective parents?

Sex chromosome aneuploidy (SCA) can be detected on prenatal diagnostic testing and cell free DNA screening (cfDNA). High risk cfDNA results should be confirmed with diagnostic testing. This summary article serves as an update for prenatal providers and assimilates data from neurodevelopmental, epidemiologic, and registry studies on the most common SCA. This information can be helpful for counseling after prenatal diagnosis of sex chromosome aneuploidy. Incidence estimates may be influenced by ascertainment bias and this article is not a substitute for interdisciplinary consultation and counseling.

Validation of algorithms to identify adverse perinatal outcomes in the Medicaid Analytic Extract database.

BACKGROUND: The Medicaid Analytic eXtract (MAX) is a health care utilization database from publicly insured individuals that has been used for studies of drug safety in pregnancy. Claims-based algorithms for defining many important maternal and neonatal outcomes have not been validated. OBJECTIVE: To validate claims-based algorithms for identifying selected pregnancy outcomes in MAX using hospital medical records. METHODS: The medical records of mothers who delivered between 2000 and 2010 within a single large healthcare system were linked to their claims in MAX. Claims-based algorithms for placental abruption, preeclampsia, postpartum hemorrhage, small for gestational age, and noncardiac congenital malformation were defined. Fifty randomly sampled cases for each outcome identified using these algorithms were selected, and their medical records were independently reviewed by two physicians to confirm the presence of the diagnosis of interest; disagreements were resolved by a third physician reviewer. Positive predictive values (PPVs) and 95% confidence intervals (CIs) of the claims-based algorithms were calculated using medical records as the gold standard. RESULTS: The linked cohort included 10,899 live-birth pregnancies. The PPV was 92% (95% CI, 82%-97%) for placental abruption, 82% (95% CI, 70%-91%) for preeclampsia, 74% (95% CI, 61%-85%) for postpartum hemorrhage, 92% (95% CI, 82%-97%) for small for gestational age, and 86% (95% CI, 74%-94%) for noncardiac congenital malformation. CONCLUSIONS: Across the perinatal outcomes considered, PPVs ranged between 74% and 92%. These PPVs can inform bias analyses that correct for outcome misclassification.

Genetic diagnosis in the fetus.

Many genetic disorders are detectable in the prenatal period, and the capacity to identify them has increased remarkably as molecular genetic testing techniques continue to improve and become incorporated into clinical practice. The indications for prenatal genetic testing vary widely, including follow-up of an anomaly found by routine ultrasound or maternal aneuploidy screening, a family history of genetic disease, advanced maternal or paternal age, or evaluation of a low-risk pregnancy due to parental concern. The interpretation of genetic variants identified in the prenatal period poses unique challenges due to the lack of ability for deep phenotyping as well as the option to make critical decisions regarding pregnancy continuation and perinatal management. In this review, we address the various modalities currently available and commonly used for genetic testing, including preimplantation genetic testing of embryos, cell-free DNA testing, and diagnostic procedures such as chorionic villous sampling, amniocentesis, or percutaneous umbilical blood sampling, from which samples may be sent for a wide variety of genetic tests. We discuss the difference between these modalities for the genetic diagnosis of a fetus, their strengths and weaknesses, and strategies for their optimal use in order to direct perinatal care.

When ultrasound anomalies are present: An estimation of the frequency of chromosome abnormalities not detected by cell-free DNA aneuploidy screens.

OBJECTIVES: This study characterizes cytogenetic abnormalities with ultrasound findings to refine counseling following negative cell-free DNA (cfDNA). METHODS: A retrospective cohort of pregnancies with chromosome abnormalities and ultrasound findings was examined to determine the residual risk following negative cfDNA. Cytogenetic data was categorized as cfDNA detectable for aneuploidies of chromosomes 13, 18, 21, X, or Y or non-cfDNA detectable for other chromosome abnormalities. Ultrasound reports were categorized as structural anomaly, nuchal translucency (NT) ≥3.0 mm, or other "soft markers". Results were compared using chi squared and Fishers exact tests. RESULTS: Of the 498 fetuses with cytogenetic abnormalities and ultrasound findings, 16.3% (81/498) had non-cfDNA detectable results. In the first, second, and third trimesters, 12.4% (32/259), 19.5% (42/215), and 29.2% (7/24) had non-cfDNA detectable results respectively. The first trimester non-cfDNA detectable results reduced to 7.7% (19/246) if triploidy was detectable by cfDNA testing. For isolated first trimester NT of 3.0-3.49 mm, 15.8% (6/38) had non-cfDNA detectable results, while for NT ≥3.5 mm, it was 12.3% (20/162). For cystic hygroma, 4.3% (4/94) had non-cfDNA detectable results. CONCLUSIONS: Counseling for residual risk following cfDNA in the presence of an ultrasound finding is impacted by gestational age, ultrasound finding, and cfDNA detection of triploidy.

Neutral effect of body mass index on implantation rate after frozen-thawed blastocyst transfer.

OBJECTIVE: To examine the effects of body mass index (BMI) on implantation rate after uniform protocol frozen-thawed blastocyst transfer in women with a homogenous uterine environment. DESIGN: Retrospective cohort study. SETTING: Single IVF clinic at a large academic institution. PATIENT(S): Four hundred sixty-one infertile women treated at a large academic institution from January 2007 to January 2014. INTERVENTION(S): All women underwent standardized slow frozen-thawed blastocyst transfers with good-quality day 5-6 embryos, following an identical hormonal uterine preparation, with comparison groups divided according to BMI category: underweight (<18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), and obese (≥30.0 kg/m2). MAIN OUTCOME MEASURE(S): Implantation rate. RESULT(S): There were no statistically significant differences identified when comparing implantation rates among the four BMI cohorts. The implantation rate was 38.2% in normal weight patients, 41.7% in underweight patients, 45.1% in overweight patients, and 34.7% in obese patients. Adjusted odds ratios (OR) demonstrated no association between the main outcome, implantation rate, and BMI. Compared with the normal weight patients, the adjusted OR of implantation was 1.70 (95% confidence interval [CI], 0.40-7.72) for underweight patients, 1.61 (95% CI, 0.97-2.68) for overweight patients, and 0.92 (95% CI, 0.49-1.72) for obese patients. Secondary outcomes, including rates of miscarriage, clinical pregnancy, ongoing pregnancy, and live birth, were not significantly different between cohorts. While powered to detect a 16% difference between overweight and normal weight women, the study was underpowered to detect differences in the underweight and obese women, and no definitive conclusions can be drawn for these small cohorts. Patients with transfers that required the longest amount of time, greater than 200 seconds, had the highest average BMI of 27.5 kg/m2. CONCLUSION(S): Under highly controlled circumstances across 7 years of data from a single institution, using a uniform uterine preparation, following a precise transfer technique with high-quality day 5-6 slow frozen-thawed blastocysts, a BMI in the overweight range of 25-29.9 kg/m2 is not associated with a poorer implantation rate or live-birth rate, nor is it associated with an increased risk of miscarriage when compared with a normal BMI range. The increased length of time required during transfer for women with higher BMI suggests body habitus may contribute to difficult transfers, although this may not translate into poorer implantation rates. By using a standardized protocol for slow freezing and thawing of embryos, using identical hormonal preparation and a uniform ET protocol, a homogenous uterine environment was created in this carefully selected cohort of women, thereby minimizing confounders and uniquely highlighting the neutral effect of overweight BMI on implantation rate.