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APOL1 genotype-associated morphologic changes among patients with focal segmental glomerulosclerosis.

Jarcy ZeeMichelle T McNultyJeffrey B HodginOlga ZhdanovaSangeeta HingoraniJonathan Ashley JeffersonKeisha L GibsonHoward TrachtmanAlessia FornoniKatherine M DellHeather N ReichSerena BagnascoLarry A GreenbaumRichard A LafayetteDebbie S GipsonElizabeth BrownMatthias KretzlerGerald AppelKamalanathan K SambandamKatherine R TuttleDhruti ChenMeredith A AtkinsonMarie C HoganFrederick J KaskelKevin E MeyersJohn O'TooleTarak SrivastavaChristine B SethnaMichelle A HladunewichJ J LinCynthia C NastVimal K DerebailJiten PatelSuzanne VentoLawrence B HolzmanAmbarish M AthavaleSharon G AdlerKevin V LemleyJohn C LieskeJonathan J HoganCrystal A GadegbekuFernando C FervenzaChia-Shi WangRaed Bou MatarPamela SingerJeffrey B KoppLaura BarisoniMatthew Gordon Sampson
Published in: Pediatric nephrology (Berlin, Germany) (2021)
While APOL1-associated FSGS is associated with two risk alleles, both one and two risk alleles are associated with cellular/tissue changes in this study of FSGS patients. Our lack of discovery of a large group of tissue differences in FSGS and no significant difference in MCD may be due to the lack of power but also supports investigating whether machine learning methods may more sensitively detect APOL1-associated changes.
Keyphrases
  • machine learning
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • peritoneal dialysis
  • prognostic factors
  • high throughput