Simulasi Metode Statistik untuk Seleksi Single Nucleotide Polymorphism
DOI:
https://doi.org/10.21776/ub.biotropika.2019.007.01.04Keywords:
data genetik, metode statistik, simulasi, SNPAbstract
Kemajuan teknologi sekuensing menyebabkan peningkatan ketersediaan sekuen genom organisme. Ribuan strain dan isolat dari berbagai populasi organisme telah diisolasi serta diketahui sekuen genomnya. Informasi genetik populasi organisme dapat dimanfaatkan sebagai marka molekuler. Marka Single Nucleotide Polymorphism (SNP) terdapat dalam jumlah banyak namun tidak seluruhnya informatif. Metode seleksi yang telah ada belum efektif menyeleksi SNP informatif, oleh karena itu perlu dilakukan pengembangan metode seleksi SNP yang efektif. Metode seleksi SNP dikembangkan menggunakan metode statistik dengan FST sebagai filter (penyaring) utamanya dan digabungkan dengan Linkage Disequilibrium (LD). Struktur populasi dari SNP diketahui menggunakan Principal Component Analysis (PCA), Principal Coordinate Analysis (PCoA), pairwise FST, dan neighbor-joining population tree. Kriteria SNP informatif diketahui dengan menghitung FST dan Minor Allele Frequency (MAF). Metode statistik diuji efektivitasnya dalam menyeleksi SNP informatif menggunakan simulasi data genetik populasi. Hasil penelitian menunjukkan pengembangan metode statistik dengan menggunakan FST sebagai penyaring utama efektif dalam menyeleksi SNP informatif. Kriteria SNP informatif adalah SNP dengan MAF 0,2-0,4 serta FST 0,1-0,4 dan 0,8-1,0.
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