Dataset Information

High Frequency Haplotypes are Expected Events, not Historical Figures.

ABSTRACT: Cultural transmission of reproductive success states that successful men have more children and pass this raised fecundity to their offspring. Balaresque and colleagues found high frequency haplotypes in a Central Asian Y chromosome dataset, which they attribute to cultural transmission of reproductive success by prominent historical men, including Genghis Khan. Using coalescent simulation, we show that these high frequency haplotypes are consistent with a neutral model, where they commonly appear simply by chance. Hence, explanations invoking cultural transmission of reproductive success are statistically unnecessary.

SUBMITTER: Guillot EG

PROVIDER: S-EPMC4722684 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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High Frequency Haplotypes are Expected Events, not Historical Figures.

Guillot Elsa G EG Cox Murray P MP

F1000Research 20150904

Cultural transmission of reproductive success states that successful men have more children and pass this raised fecundity to their offspring. Balaresque and colleagues found high frequency haplotypes in a Central Asian Y chromosome dataset, which they attribute to cultural transmission of reproductive success by prominent historical men, including Genghis Khan. Using coalescent simulation, we show that these high frequency haplotypes are consistent with a neutral model, where they commonly appe ...[more]

PMID: 26834987

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Project description:Human leukocyte antigens (HLA) diversity has a tremendous impact on shaping the transplantation practices, transfusion-associated graft versus host disease prevention strategies, and host–pathogen interactions. Here, we conducted a retrospective study of HLA class I and class II homozygosity at allelic and haplotype levels in unrelated individuals genotyped from 2012 to 2016 in a tertiary hospital in the capital of Saudi Arabia. Among 5,000 individuals, 2,773 individuals meet inclusion criteria and were retrospectively analyzed for HLA-A, -B, -C–DRB1, and -DQB1 homozygosity at allelic and haplotype levels. HLA molecular typing was performed using a commercial reverse sequence-specific oligonucleotide (rSSO) kit. We were able to identify 15 HLA-A, 20 HLA-B, 11 HLA-C, 13 HLA-DRB1, and five HLA-DQB1 homozygous alleles demonstrating a very low genetic diversity in the Saudi population. The highest homozygosity in HLA class I was found in locus C followed by A and B (20.3% > 16.1% > 15.5%; p < 0.001) where the most homozygote alleles were A*02 (9.2%), B*51 and B*50 (5.7% and 3.7%), and C*07, C*06, and C*15 (7.2%, 5.48%, and 3.3%) and in HLA class II, the highest homozygosity was found in locus DQB1 compared to DRB1 (31.71% > 19.2%; p < 0.001), with the most common homozygote alleles being DRB1*07 and DRB1*04 (5.33% and 4.2%) and DQB1*02, DQB1*06, and DQB1*03 (13.55%, 7.92%, and 7.64%). The frequency of finding an individual with one homozygote allele was (24.6%), two homozygote alleles (13.5%), three homozygote alleles (4.7%), four homozygote alleles (3.4%), and five alleles were (4.8%). The most frequent homozygote haplotypes are A*23∼C*06∼B*50∼DRB1*07∼DQB1*02 and A*02∼C*06∼B*50∼DRB1*07∼DQB1*02. This study shows low diversity of both class I and II alleles and haplotypes in the Saudi population, which would have a significant impact on shaping the transplantation practices, transfusion-associated graft versus host disease prevention strategies, and host–pathogen interactions.

Project description:ObjectiveHigh-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high-frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape.MethodWe propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared.ResultIn the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude-based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125).SignificanceADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.

Dataset Information

High Frequency Haplotypes are Expected Events, not Historical Figures.

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High Frequency Haplotypes are Expected Events, not Historical Figures.

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