Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max).

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits

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The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.</div

Manhattan plots of GWAS for flowering time (R1), maturity time (R8), and reproductive length (RL) in three hundred twenty-nine <i>G</i>. <i>max</i> accessions.

GWAS results correspond to the analysis across the nine environments. The horizontal dashed lines indicate the statistically significant cut-off of–log (p-value) = 4.16. Significant SNPs IDs correspond to the Wm82.a1.</p

Mean flowering (R1), and maturity (R8) time in days after planting (DAP) and mean reproductive length (RL) in number of days per accession across all environments.

Mean flowering (R1), and maturity (R8) time in days after planting (DAP) and mean reproductive length (RL) in number of days per accession across all environments.</p

Summary of the single-nucleotide polymorphisms (SNPs) significantly associated with flowering time (R1), maturity time (R8), and reproductive length (RL) in three hundred twenty-nine G. max accessions across environments and years.

Summary of the single-nucleotide polymorphisms (SNPs) significantly associated with flowering time (R1), maturity time (R8), and reproductive length (RL) in three hundred twenty-nine G. max accessions across environments and years.</p

Significant SNPs from genome-wide association analysis by individual environments, years, and across all environments.

R1 is flowering time, R8 is maturity time, and RL is reproductive length. The plot is color coded by the effect of the SNPs for each trait. SNP positions are based on the Wm82.a2.v1 genome assembly. (TIF)</p

Principal component analysis (PCA) biplot of three hundred twenty-nine <i>G</i>. max USDA accessions.

Individuals are represented with different colors and shapes according to maturity group (MG) and stem termination type (ST). N-indeterminate, S- semideterminate.</p

Description of the candidate genes.

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.</div

Average squared correlation of allele frequencies (<i>r</i>^<i>2</i>) against distance across the whole genome from heterochromatic (red) and euchromatic regions (blue).

Average squared correlation of allele frequencies (r2) against distance across the whole genome from heterochromatic (red) and euchromatic regions (blue).</p