Sequencing studies in human genetics: design and interpretation

Abstract

Next-gene ration sequencing is becoming the primary discovery tool in human genetics. There have been many clear successes in identifying genes that are responsible for Mendelian diseases, and sequencing approaches are now poised to identify the mutations that cause undiagnosed childhood genetic diseases and those that predispose individuals to more common complex diseases. There are, however, growing concerns that the complexity and magnitude of complete sequence data could lead to an explosion of weakly justified claims of association between genetic variants and disease. Here, we provide an overview of the basic workflow in next-generation sequencing studies and emphasize, where possible, measures and considerations that facilitate accurate inferences from human sequencing studies.

The human gene damage index as a gene-level approach to prioritizing exome variants

“…We therefore set out to identify a set of highly mutated FP genes from the WES data derived from 84 patients with monogenic PIDs for whom the true disease-causing mutation had already been experimentally validated and reported (1). We used standard variant filtering methods for rare diseases, retaining only those with a minor allele frequency (MAF) <0.01 in the 1,000 Genomes Project, together with missense, nonsense, and frameshift variants (including startloss and stop-loss), and in-frame indels and splice variants for which sequencing quality was high (see Materials and Methods for further details) (4,14). We then determined the frequency of the remaining 44,668 variants among the 11,190 genes harboring them.…”

“…The advent of next-generation sequencing (NGS)-based approaches, including whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNASeq, has facilitated the large-scale detection of gene variants at both the individual and population levels (2)(3)(4)(5)(6). In patients suffering from a monogenic disease, at most two variants are disease causing [true positives (TP)], and the other 20,000 or so proteincoding exome variants are false positives (FP; type I error).…”

Targeted mutation screening panels expose systematic population bias in detection of cystic fibrosis risk

“…PolyPhen-2 operates exclusively on missense mutations with an algorithm that utilizes protein structural data and comparative evolutionary considerations to determine the likelihood of damage. 15,16 PolyPhen-2 results are reported in the form of damage likelihoods on a scale of 0.0 to 1.0, with a score of at least 0.85 considered "probably damaging. "…”

Nanotechnologies for biomedical science and translational medicine

“…intellectual effort within a genomics study now is centered on the scientific design and interpretation of population-based studies (6). However, NGS has major deficiencies.…”