Telomere-lengthening germline variants predispose to a syndromic papillary thyroid cancer subtype.

Papillary thyroid cancer (PTC) is the most common endocrine malignancy. 10% to 15% of individuals show familial clustering with three or more affected members, but the factors underlying this risk are unknown. In a group of recently studied individuals with POT1 pathogenic variants and ultra-long telomere length, PTC was the second most common solid tumor. We tested whether variants in POT1 and four other telomere-maintenance genes associated with familial cancer underlie PTC susceptibility. Among 470 individuals, we identified pathogenic or likely pathogenic variants in three genes encoding telomere-binding proteins: POT1, TINF2, and ACD. They were found in 4.5% and 1.5% of familial and unselected cases, respectively. Individuals harboring these variants had ultra-long telomere length, and 15 of 18 (83%) developed other cancers, of which melanoma, lymphoma, and sarcoma were most common. Among individuals with PTC and melanoma, 22% carried a deleterious germline variant, suggesting that a long telomere syndrome might be clinically recognizable. Successive generations had longer telomere length than their parents and, at times, developed more cancers at younger ages. Tumor sequencing identified a single oncogenic driver, BRAF p.Val600Glu, in 10 of 10 tumors studied, but no telomere-maintenance mechanism, including at the TERT promoter. These data identify a syndromic subset of PTCs with locus heterogeneity and telomere lengthening as a convergent mechanism. They suggest these germline variants lower the threshold to cancer by obviating the need for an acquired telomere-maintenance mechanism in addition to sustaining the longevity of oncogenic mutations.

Measuring, visualizing, and diagnosing reference bias with biastools.

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.

Measuring, visualizing and diagnosing reference bias with biastools.

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios, i.e. (a) when the donor's variants are known and reads are simulated, (b) when donor variants are known and reads are real, and (c) when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.

Reference flow: reducing reference bias using multiple population genomes.

Most sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.