| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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| 3. |
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| 4. |
- Olson, Nathan D., et al.
(författare)
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precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.
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| 5. |
- Olson, Nathan D., et al.
(författare)
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PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions
- 2022
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Ingår i: Cell Genomics. - : Elsevier BV. - 2666-979X. ; 2:5, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.
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| 6. |
- Vasan, Senthil K, et al.
(författare)
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Associations of variants in FTO and near MC4R with obesity traits in South Asian Indians
- 2012
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Ingår i: Obesity. - : Wiley. - 1930-7381 .- 1930-739X. ; 20:11, s. 2268-2277
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Tidskriftsartikel (refereegranskat)abstract
- Recent genome-wide association studies show that loci in FTO and melanocortin 4 receptor (MC4R) associate with obesity-related traits. Outside Western populations the associations between these variants have not always been consistent and in Indians it has been suggested that FTO relates to diabetes without an obvious intermediary obesity phenotype. We investigated the association between genetic variants in FTO (rs9939609) and near MC4R (rs17782313) with obesity- and type 2 diabetes (T2DM)-related traits in a longitudinal birth cohort of 2,151 healthy individuals from the Vellore birth cohort in South India. The FTO locus displayed significant associations with several conventional obesity-related anthropometric traits. The per allele increase is about 1% for BMI, waist circumference (WC), hip circumference (HC), and waist-hip ratio. Consistent associations were observed for adipose tissue-specific measurements such as skinfold thickness reinforcing the association with obesity-related traits. Obesity associations for the MC4R locus were weak or nonsignificant but a signal for height (P < 0.001) was observed. The effect on obesity-related traits for FTO was seen in adulthood, but not at younger ages. The loci also showed nominal associations with increased blood glucose but these associations were lost on BMI adjustment. The effect of FTO on obesity-related traits was driven by an urban environmental influence. We conclude that rs9939609 variant in the FTO locus is associated with measures of adiposity and metabolic consequences in South Indians with an enhanced effect associated with urban living. The detection of these associations in Indians is challenging because conventional anthropometric obesity measures work poorly in the Indian "thin-fat" phenotype.
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