LECTURE 22 GENETICS

Allele-Specific Oligonucleotide (ASO) analysis is restricted by its capacity to only detect a small number of mutations.
Modern clinical needs require genetic tests capable of detecting complex mutation patterns.
Examples of genomic complexity requiring advanced testing: - Cystic Fibrosis (CFTR gene): Consists of $27$ exons and spans $250,000\,bp$ (250k b) of genomic DNA. - Tumor suppressor TP53 gene: Known to have at least $500$ different mutations.
Screening for such high-complexity mutations requires high-throughput methods to be efficient and accurate.

DNA microarrays can contain expanded fields for genetic analysis, with a capacity for up to $500,000$ different fields.
Each field on the microarray can represent a unique DNA sequence.
Microarrays can be configured to test for mutations within a single gene or include specific probes designed to detect Single Nucleotide Polymorphisms (SNPs).
Technological evolution: It is anticipated that Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and RNA sequencing (RNA-seq) will eventually replace most microarray technologies.

Genome Scanning: - Used to detect DNA mutations across the genome. - Utilizes SNP sequences as probes on the DNA microarray to allow for the screening of thousands of mutations simultaneously. - Capable of analyzing hundreds of disease-associated alleles in one assay.
Gene-Expression Microarrays: - Designed to detect specific gene-expression patterns within genes. - Contain probes for specific genes that are hypothesized to be expressed differently depending on the cell state or tissue type.

Comparative Analysis: Researchers use microarrays to analyze gene expression in normal cells versus cancer cells.
Two-Channel Microarray Process: - Two cDNA samples derived from different tissues are labeled and compete for binding to the same probe on the microarray.
Data Visualization: - Heat maps are utilized to display the levels of gene expression. - Research has revealed that certain cancers possess distinct and identifiable patterns of gene expression.
Case Study: Diffuse Large B-cell Lymphoma (DLBCL): - Examination of gene expression in normal white blood cells vs. DLBCL. - Discovery of two distinct types of DLBCL based on expression patterns: - GCB-like: Associated with higher patient survival rates. - B-like: Associated with lower patient survival rates.

SNPs serve multiple critical functions in modern genetics: - Identification and forensics. - Mapping and Genome-Wide Association Studies (GWAS) for complex diseases. - Estimating an individual’s predisposition to specific diseases. - Predicting specific genetic traits (as seen in services like 23andMe). - Classifying patients for participation in clinical trials.

Global Screening Array (GSA) Chip (Illumina): - Combines multi-ethnic genome-wide content with curated clinical research variants. - Includes quality control (QC) markers specifically for precision medicine research. - Features a massive capacity of $665,608$ markers.
Technical Components: - Uses the Infinium Assay (Infinium I and Infinium II probe designs). - Probe design involves bead types, captured genomic DNA, and specific labels for intensity detection. - Data is processed using the iScan system.

Genomic data is documented via rsid (Reference SNP cluster ID), chromosome number, position, and genotype.
Representative data from Dr. Barnett’s SNPs (Chromosome 1): - $rs12564807$ : Position $734462$ , Genotype AA. - $rs3131972$ : Position $752721$ , Genotype GG. - $rs148828841$ : Position $760998$ , Genotype CC. - $rs115093905$ : Position $787173$ , Genotype GG. - $rs7537756$ : Position $854250$ , Genotype AG.
HLA Markers: - Gene: HLA-DQA1; Marker: $rs2187668$ . - Analysis identifies typical copies from one parent versus variant copies from the other (e.g., HLA-DQ2.5 CC).

Whole Genome Sequencing (WGS): - Necessary for diagnosing and treating diseases caused by multiple genes. - Case Study: WGS in individuals with Autism Spectrum Disorder (ASD). - Found at least $100$ different genes involved in the disorder. - Accounts for the broad range of phenotypes associated with ASD. - Revealed the presence of both inherited and de novo (new) mutations. - Strategy: Sequence-based knowledge enables patient-specific treatment strategies.
Whole Exome Sequencing (WES): - A more focused alternative to WGS that sequences only the protein-coding regions (exome). - The patient's exome is sequenced and compared to the general population using bioinformatics. - WGS and WES are often used in tandem to identify causative mutations.

Single-Cell Sequencing (SCS): - Involves isolating and sequencing genomic DNA from a single isolated cell. - Essential for analyzing somatic cell mutations versus germ-line mutations. - Allows for the exploration of genetic variation that occurs from cell to cell within the same organism. - Data visualization often involves tSNE plots (t-distributed Stochastic Neighbor Embedding) representing clusters (e.g., clusters PM1 through PM12, CC1, CC2, LM1, LM2).
RNA Sequencing (RNA-seq): - Provides transcriptome-wide analysis of all genes expressed by cells in a population. - Single-cell RNA sequencing (scRNA-seq) enables researchers to isolate both DNA and RNA from the same individual cell. - Facilitates the comparison of the genes present in a cell versus the relative expression levels of each transcript encoded by the genome.

Embryonic Coalescence Studies (Nakamura et al., 2010): - Research on newly laid and stage 6 oocytes. - RNA-seq of Gryllus oocyte halves (Anterior vs. Posterior). - Statistical analysis includes $\log_2$ Fold Change and $-10\log(P‐Value)$ (Barnett et al., in prep).
Functional Genomics via RNA Interference (RNAi): - Study by Donoughe and Extavour (2016) regarding RNAi of anterior-open. - Analysis of expression/effects in specific tissues: Eye, Leg 1, Leg 2, and Leg 3. - Ongoing research cited as Barnett et al., in prep.