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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2021)

Introduction

Twin studies indicate that a substantial fraction of ovarian cancers should be predictable from genetic testing. Genetic risk scores can stratify women into different classes of risk. Higher risk women can be treated or screened for ovarian cancer, which should reduce ovarian cancer death rates. However, current ovarian cancer genetic risk scores do not work that well. We developed a genetic risk score based on variations in the length of chromosomes.

Methods

We evaluated this genetic risk score using data collected by The Cancer Genome Atlas. We synthesized a dataset of 414 women who had ovarian serous carcinoma and 4225 women who had no form of ovarian cancer. We characterized each woman by 22 numbers, representing the length of each chromosome in their germ line DNA. We used a gradient boosting machine to build a classifier that can predict whether a woman had been diagnosed with ovarian cancer.

Results

The genetic risk score based on chromosomal-scale length variation could stratify women such that the highest 20% had a 160x risk (95% confidence interval 50x-450x) compared to the lowest 20%. The genetic risk score we developed had an area under the curve of the receiver operating characteristic curve of 0.88 (95% confidence interval 0.86-0.91).

Conclusion

A genetic risk score based on chromosomal-scale length variation of germ line DNA provides an effective means of predicting whether or not a woman will develop ovarian cancer.
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2020)

    Introduction

    The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

    Methods

    We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.

    Results

    We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10-11) as measured against a randomized control and (p = 3 · 10-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

    Conclusion

    Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
      Cover page: Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation
      • Thesis
      • Peer Reviewed
      UC Irvine Electronic Theses and Dissertations (2021)

      Next generation sequencing has created large databases of human genomic information. Utilizing this information to understand disease and genetic risks is a large engineering task. Previous studies have focused primarily on single nucleotide polymorphisms (SNPs) in assessing patient risk for diseases such as cancers and other diseases such as Schizophrenia. These SNP panels do not consider epistatic interactions in the human genome.Chromosomal scale-length variation (CSLV) is a promising approach for assessing genetic risk scores. CSLV evaluates copy number variations (CNVs), condensing genomic information into a smaller number of parameters. Reducing parameters allows the use of machine learning without the need for millions of patients’ data. Machine learning can consider epistatic interactions that might be missed by conventional genome wide association studies (GWAS). Utilizing machine learning classification algorithms, we assessed prediction of diseases such as ovarian cancer and schizophrenia using CSLV as the sole features for prediction. We have demonstrated the viability of this method in assessing germline inheritance of complex human diseases in The Cancer Genome Atlas (TCGA) and UK Biobank. We tested 33 different types of cancer from TCGA’s 11,000 patients. Glioblastoma multiforme (AUC = 0.87), ovarian cancer (AUC = 0.89), colon adenocarcinoma (AUC = 0.82), and breast invasive carcinoma (AUC = 0.75) could be distinguished greater than chance from cancers. These results were replicated the UK Biobank using 88 numbers computed from the 22 autosomes for 1,534 women with breast cancer and a control population of 4,391 women without breast cancer and found a classifier with an AUC of 0.83. 1,129 people from the UK Biobank have a diagnosis of schizophrenia. Using a randomized set of 1,129 individuals without schizophrenia we created 150 models using 92 number CSLVs as our feature set. The results provided an average AUC of 0.545 (95% CI 0.539-0.550). Our results indicate that CSLV data can provide an effective genetic risk score for schizophrenia. In conclusion, CSLV is a promising and novel way to utilize large scale human genetic information in the prediction off complex. Continued improvement of this technique can dramatically improve individualized patient care and can aid physicians in earlier diagnosis.

        Cover page: Chromosomal scale length variations as a genetic risk score for predicting complex human diseases in large scale genomic datasetsCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2021)
        Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539-0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.
          Cover page: A genetic risk score using human chromosomal-scale length variation can predict schizophreniaCreative Commons 'BY' version 4.0 license
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