Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case-control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E-06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E-07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype-genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.
Abstract Background Genetic factors associated with intellectual disability (ID) include chromosomal aberrations, copy number variations (CNVs), and pathogenic variants. Identifying the genetic etiologies is beneficial for patient classification, therapy, management, and prognostic evaluation. Emerging genetic tests are helpful in identifying these genetic causes. Methods We enrolled two girl siblings with ID. Trio whole‐exome sequencing (WES) and Copy number variation sequencing (CNV‐Seq) were performed for genetic molecular analysis in these probands and their parents. The parents also accepted high‐resolution G‐banded karyotype studies. Results No significant homozygous or heterozygous variants were identified through WES. By CNV‐seq, we identified an abnormal 3p26.3p25.3 microdeletion and 14q32.13q32.33 microduplication in the two girl siblings but not in their parents. A balanced translocation 46, XX, t (3, 14) (p25; q32) was found in their mother. Conclusion The affected siblings have similar phenotype, including ID, short stature, and microcephaly. Their mother had a history of seven first‐trimester miscarriages and one elective termination because of multiple malformations. This abnormal karyotype was also thought to be responsible for the mother's recurrent miscarriage. WES in combination with CNV‐seq analysis is very helpful for identification of the genetic causes of ID without positive karyotype findings.
Abstract STUDY QUESTION Does luteal phase estrogen valerate pretreatment improve oocyte yield and clinical outcomes in patients with low ovarian response during ovarian stimulation with the antagonist protocol? SUMMARY ANSWER Pretreatment with oral estrogen valerate from Day 7 after ovulation to Day 2 of the next menstrual cycle did not increase oocyte yield in patients with a low ovarian response compared to no pretreatment. WHAT IS KNOWN ALREADY Previous studies showed that patients with a normal ovarian response can obtain better clinical outcomes after pretreatment with estrogen in the antagonist protocol. For patients with advanced age and low ovarian response, it remains unclear if estrogen valerate pretreatment with the antagonist protocol yields more oocytes and improves pregnancy outcomes. STUDY DESIGN, SIZE, DURATION This non-blinded randomized controlled trial (RCT) was conducted between November 2017 and March 2021. Participants were 552 women with low response who requested IVF treatment. The primary endpoint was comparison of the total number of retrieved oocytes between the two groups. The secondary endpoints were the total number of retrieved metaphase II (MII) oocytes, duration and total dosage of recombinant FSH (rFSH), good-quality embryo rate and clinical pregnancy rate. PARTICIPANTS/MATERIALS, SETTING, METHODS The study was conducted at a reproductive center. The RCT enrolled 552 infertile women with a low ovarian response (according to the Bologna criteria) who were undergoing IVF. In the study group, on Day 7 after ovulation patients were administered oral estrogen valerate (2 mg twice a day) until Day 2 of their next menstruation. Ovary stimulation was performed using rFSH, and a GnRH antagonist (0.25 mg/day) was started when a dominant follicle had a mean diameter ≥13 mm. MAIN RESULTS AND THE ROLE OF CHANCE No significant difference was observed in the number (mean [SD]) of oocytes retrieved from the estrogen valerate pretreatment and control group (3.2 [2.8] versus 3.4 [2.6], respectively). The treatment difference was −0.18 (95% CI −0.67, 0.32, P = 0.49). No significant differences were observed in the number of MII oocytes (2.9 [2.5] versus 3.1 [2.4], mean difference −0.23, 95% CI (−0.69, 0.23), P = 0.16) and good-quality embryos (1.0 [1.3] versus 1.20 [1.6], mean difference −0.23, 95% CI (−0.50, 0.04), P = 0.19) between the two groups. The duration of rFSH treatment was significantly longer in the estrogen valerate pretreatment group than in the control group (10.3 [2.2] versus 8.6 [2.1] days, mean difference 1.7, 95% CI (1.3, 2.2), P = 0.00), and the total rFSH dosage was significantly higher in the estrogen valerate pretreatment group than in the control group (3081 [680] versus 2548 [649] IU, mean difference 553.7, 95% CI (405.8, 661.6), P = 0.00). The clinical pregnancy rate in the pretreatment group (19.3% [23/119]) was not significantly different from that in the control group (28.7% [43/150]). The mean difference was −0.09, 95% CI (−0.20, 0.01), P = 0.08. LIMITATIONS, REASONS FOR CAUTION The major limitation was the high dropout rate of patients. Some patients did not return to the hospital for treatment because of predicted low success rates and for economic reasons. In addition, it is possible that the fixed dose of 300 IU rFSH was not sufficient to see differences in oocyte yield between the groups. WIDER IMPLICATIONS OF THE FINDINGS Estrogen valerate pretreatment with an antagonist protocol did not increase oocyte yield in patients with low ovarian response. Similar to the number of retrieved oocytes, there was no significant difference in clinical pregnancy rate between estrogen pretreatment group and control group. More research is needed on whether patients with low ovarian response need pretreatment and which pretreatment is more appropriate. STUDY FUNDING/COMPETING INTEREST(S) This study was supported in part by a research grant from the Investigator-Initiated Studies Program of MSD (China) Holding Co., Ltd. and Organon (Shanghai) Pharmaceutical Technology Co., Ltd. (Grant number: IIS 56284). The authors declare that they have no competing interests regarding authorship or publication of this study. TRIAL REGISTRATION NUMBER ClinicalTrials.gov NCT03300518. TRIAL REGISTRATION DATE 28 September 2017. DATE OF FIRST PATIENT’S ENROLMENT 15 November 2017.
Abstract Inspecting concordance between self-reported sex and genotype-inferred sex from genomic data is a significant quality control measure in clinical genetic testing. Numerous tools have been developed to infer sex for genotyping array, whole-exome sequencing, and whole-genome sequencing data. However, improvements in sex inference from targeted gene sequencing panels are warranted. Here, we propose a new tool, seGMM, which applies unsupervised clustering (Gaussian Mixture Model) to determine the gender of a sample from the called genotype data integrated aligned reads. seGMM consistently demonstrated > 99% sex inference accuracy in publicly available (1000 Genomes) and our in-house panel dataset, which achieved obviously better sex classification than existing popular tools. Compared to including features only in the X chromosome, our results show that adding additional features from Y chromosomes (e.g. reads mapped to the Y chromosome) can increase sex classification accuracy. Notably, for WES and WGS data, seGMM also has an extremely high degree of accuracy. Finally, we proved the ability of seGMM to infer sex in single patient or trio samples by combining with reference data and pinpointing potential sex chromosome abnormality samples. In general, seGMM provides a reproducible framework to infer sex from massively parallel sequencing data and has great promise in clinical genetics.
Impatiens occultans Hook. f. (Balsaminaceae) is newly recorded for China, from Gyirong County, Xizang Province. A morphological description and notes on its distribution and ecology are provided. A phylogenetic analysis yields a placement of the species that it is sister to I. tuberculata (sect. Racemosae) with which it agrees in having a navicular lower sepal without a spur and 4-colpate pollen grains, but differs in elliptic leaves, 1-flowered racemes, 4 lateral sepals and glabrous capsules.
Autism is a group of etiology and clinical heterogeneous neurodevelopmental disorders with an onset before 3 years old. It has 3 core characteristics: deficits in verbal communication; impairment of social interaction; restricted interests and repetitive behaviors. The incidence is increasing over time worldwide. Twin and family studies have demonstrated that autism has a high heritability (>90%). Although certain progress of autism genetic study has been made in the last decades and several autism susceptibility genes and loci have been identified, there are still about 70%-80% of patients for whom an autism-related genetic change cannot be identified.
Additional file 1: Supplementary Tables S1-S12. Table S1. Cohorts utilized in current study. Table S2. Pathogenicity predictions by gnomAD and GeVIR for each hnRNP. Table S3. Significant p values of two-way ANOVA between cell type and transcripts per million (TPM) for each NDD HNRNP’s GTEx data. Table S4. P values and fold change for scRNA data from developing human cortex. Table S5. Significant p values of two-way ANOVA between cell type and fold expression for each HNRNP. Table S6. De novo enrichment and CLUMP analyses of current cohort. Table S7. Phenotypes among HNRNP-related disorders. Table S8. Uncorrected p values for pairwise Fisher’s exact tests for each phenotype occurring in 20% of an HNRNP group. Table S9. Uncorrected p values for phenotype correlations between each HNRNP-related disorder. Table S10. Variants and phenotypes for HNRNPs with
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