Biology subjects

Robert Milan Porsch

Publications and source records attributed to Robert Milan Porsch.

2 recordsLinked to original sources

bioRxiv2016

Sacral agenesis: A whole exome sequencing and copy number study

BackgroundCaudal regression syndrome (CRS) or sacral agenesis is a rare congenital disorder characterized by a constellation of congenital caudal anomalies affecting the caudal spine and spinal cord, the hindgut, the urogenital system, and the lower limbs. CRS is a complex condition, attributed to an abnormal development of the caudal mesoderm, likely caused by the effect of interacting genetic and environmental factors. A well-known risk factor is maternal type 1 diabetes.\n\nResultsIn this pilot study, exome sequencing and copy number variation (CNV) analyses of 4 CRS trios implicate a number of candidate genes, including MORN1, ZNF330, CLTCL1 and PDZD2. De novo mutations were found in SPTBN5, MORN1 and ZNF330 and inherited predicted damaging mutations in PDZD2 (homozygous) and CLTCL1 (compound heterozygous) as well as in CRS-related genes PTEN (heterozygous) and VANGL1 (heterozygous). In addition, a compound heterozygous mutation in GLTSCR2, a direct regulator of PTEN was identified.\n\nTwo CNV deletions, one de novo (chr3q13.13) and one homozygous (chr8p23.2), were detected in one of our CRS patients. These deletions overlapped with CNVs previously reported in patients with similar phenotype.\n\nConclusionDespite the genetic diversity and the complexity of the phenotype, this pilot study identified genetic features common across CRS patients.

Pathology↗

bioRxiv2016

Polygenic scores using summary statistics via penalized regression

Polygenic scores (PGS) summarize the genetic contribution of a persons genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating polygenic scores have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can make use of LD information available elsewhere to supplement such analyses. To answer this question we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and p-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Genomics↗