Predictive genetics for AMD: Hype and hopes for genetics-based strategies for treatment and prevention.

Age-related macular degeneration (AMD) is a complex disease with multiple genetic and environmental risk factors. In the age of molecular genetics, many investigators have established a link between genes and development or progression of the disease. This later evolved to determine whether phenotypic features of AMD have distinct genetic profiles. Molecular genetics have subsequently been introduced as factors in risk assessment models, increasing the predictive value of these tools. Models seek to predict either development or progression of disease, and different AMD-related genes aid our understanding of these respective features. Several investigators have attempted to link molecular genetics with treatment response, but results and their clinical significance vary. Ocular and systemic biomarkers may interact with established genes, promising future routes of ongoing clinical assessment. Our understanding of AMD molecular genetics is not yet sufficient to recommend routine testing, despite its utility in the research setting. Clinicians must be wary of misusing population-based risk models from genetic and biomarker associations, as they are not necessarily relevant for individual counseling. This review addresses the known uses of predictive genetics, and suggests future directions.

Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms.

BACKGROUND: Here we present an in-depth characterization of the mechanism of sequencer-induced sample contamination due to the phenomenon of index swapping that impacts Illumina sequencers employing patterned flow cells with Exclusion Amplification (ExAmp) chemistry (HiSeqX, HiSeq4000, and NovaSeq). We also present a remediation method that minimizes the impact of such swaps. RESULTS: Leveraging data collected over a two-year period, we demonstrate the widespread prevalence of index swapping in patterned flow cell data. We calculate mean swap rates across multiple sample preparation methods and sequencer models, demonstrating that different library methods can have vastly different swapping rates and that even non-ExAmp chemistry instruments display trace levels of index swapping. We provide methods for eliminating sample data cross contamination by utilizing non-redundant dual indexing for complete filtering of index swapped reads, and share the sequences for 96 non-combinatorial dual indexes we have validated across various library preparation methods and sequencer models. Finally, using computational methods we provide a greater insight into the mechanism of index swapping. CONCLUSIONS: Index swapping in pooled libraries is a prevalent phenomenon that we observe at a rate of 0.2 to 6% in all sequencing runs on HiSeqX, HiSeq 4000/3000, and NovaSeq. Utilizing non-redundant dual indexing allows for the removal (flagging/filtering) of these swapped reads and eliminates swapping induced sample contamination, which is critical for sensitive applications such as RNA-seq, single cell, blood biopsy using circulating tumor DNA, or clinical sequencing.