Profiling of Short-Tandem-Repeat Disease Alleles in 12,632 Human Whole Genomes.

Short tandem repeats (STRs) are hyper-mutable sequences in the human genome. They are often used in forensics and population genetics and are also the underlying cause of many genetic diseases. There are challenges associated with accurately determining the length polymorphism of STR loci in the genome by next-generation sequencing (NGS). In particular, accurate detection of pathological STR expansion is limited by the sequence read length during whole-genome analysis. We developed TREDPARSE, a software package that incorporates various cues from read alignment and paired-end distance distribution, as well as a sequence stutter model, in a probabilistic framework to infer repeat sizes for genetic loci, and we used this software to infer repeat sizes for 30 known disease loci. Using simulated data, we show that TREDPARSE outperforms other available software. We sampled the full genome sequences of 12,632 individuals to an average read depth of approximately 30× to 40× with Illumina HiSeq X. We identified 138 individuals with risk alleles at 15 STR disease loci. We validated a representative subset of the samples (n = 19) by Sanger and by Oxford Nanopore sequencing. Additionally, we validated the STR calls against known allele sizes in a set of GeT-RM reference cell-line materials (n = 6). Several STR loci that are entirely guanine or cytosines (G or C) have insufficient read evidence for inference and therefore could not be assayed precisely by TREDPARSE. TREDPARSE extends the limit of STR size detection beyond the physical sequence read length. This extension is critical because many of the disease risk cutoffs are close to or beyond the short sequence read length of 100 to 150 bases.

Health Insurance For People Younger Than Age 65: Expiration Of Temporary Policies Projected To Reshuffle Coverage, 2023-33.

The Congressional Budget Office estimates that in 2023, 248 million people in the US who are younger than age sixty-five have health insurance coverage (mostly through employment-based plans), and twenty-three million people, or 8.3 percent of that age group, are uninsured-with significant variations in coverage by income and, to a lesser extent, by race and ethnicity. The unprecedented low uninsurance rate is largely attributable to temporary policies that kept beneficiaries enrolled in Medicaid and enhanced the subsidies available through the health insurance Marketplaces during the COVID-19 pandemic. As the continuous eligibility provisions unwind in 2023 and 2024, an estimated 9.3 million people in that age group will transition to other forms of coverage, and 6.2 million will become uninsured. If the enhanced subsidies expire after 2025, 4.9 million fewer people are estimated to enroll in Marketplace coverage, instead enrolling in unsubsidized nongroup or employment-based coverage or becoming uninsured. By 2033 the uninsurance rate is projected to be 10.1 percent, which is still below the 2019 rate of about 12 percent.

Target discovery using biobanks and human genetics.

Large-scale biobanks can yield unprecedented insights into our health and provide discoveries of new and potentially targetable biomarkers. Several protective loss-of-function alleles have been identified, including variants that protect against cardiovascular disease, obesity, type 2 diabetes, and asthma and allergic diseases. These alleles serve as indicators of efficacy, mimicking the effects of drugs and suggesting that inhibiting these genes could provide therapeutic benefit, as has been observed for PCSK9. We provide a context for these findings through a multifaceted review covering the use of genetics in drug discovery efforts through genome-wide and phenome-wide association studies, linking deep mutation scanning data to molecular function and highlighting some additional tools that might help in the interpretation of newly discovered variants.